NewProject_DecisionTree.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NewProject_DecisionTree.ipynb","provenance":[],"collapsed_sections":["YjyAOAWt8OHT","Xvv4zkV4Co1n","mD8qaWR8Vop9","rFyBI59cV_IC","cvmlc2S-WaRe"],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"6U7AmBHylPZ9"},"source":["# **Processing the Data**"]},{"cell_type":"code","metadata":{"id":"IoFsn-Upk77r","executionInfo":{"status":"ok","timestamp":1620768715771,"user_tz":240,"elapsed":2030,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["%matplotlib inline\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import numpy as np\n","import pandas as pd\n","df = pd.read_csv(\"https://www.dropbox.com/s/ng1zijfpsayzseu/pokedex_%28Update_04.21%29_3.csv?dl=1\",index_col=0,encoding= 'unicode_escape')"],"execution_count":273,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sGlbyTg6lZbh"},"source":["**Drop features that are clearly not needed.**"]},{"cell_type":"code","metadata":{"id":"-Qve4MSVlYEo","executionInfo":{"status":"ok","timestamp":1620768716517,"user_tz":240,"elapsed":2772,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["df = df.drop(labels = ['pokedex_number','name','german_name','japanese_name','generation','species','type_number','abilities_number',\n","                       'ability_1','ability_2','ability_hidden','base_friendship','percentage_male','egg_type_number','egg_type_2',\n","                       'against_normal','against_fire','against_water','against_electric','against_grass','against_ice','against_fight',\n","                       'against_poison','against_ground','against_flying','against_psychic','against_bug','against_rock','against_ghost',\n","                       'against_dragon','against_dark','against_steel','against_fairy'], axis = 1)\n","df = df.drop(1033) # remove eternamax because it is a huge outlier"],"execution_count":274,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"o0JJSez7m0xp"},"source":["**Preview the distribution of pokemon per type.**"]},{"cell_type":"code","metadata":{"id":"wDb68Wldm6wP","colab":{"base_uri":"https://localhost:8080/","height":313},"executionInfo":{"status":"ok","timestamp":1620768717176,"user_tz":240,"elapsed":3383,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"1e22ab0c-e9b6-4598-df29-d579619f0361"},"source":["df['type_1'].value_counts().sort_index().plot.bar()"],"execution_count":275,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fc541611bd0>"]},"metadata":{"tags":[]},"execution_count":275},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"sG6TBBpEnOnF","colab":{"base_uri":"https://localhost:8080/","height":311},"executionInfo":{"status":"ok","timestamp":1620768717384,"user_tz":240,"elapsed":3577,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"fa2feef9-7261-4ab9-a939-9fe12ec8dd03"},"source":["df['type_2'].value_counts().sort_index().plot.bar()"],"execution_count":276,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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tVFatxe1iQX12CdTGgsXAVPz+Teq9Tl3xM9X25Y3b2vLjOlhkra2/V8AkrYGHlaf++U4BYy49N2E8oG4UBJucekLnCvp/ZSA84vBTjc/wy6TdCRliWQoLbjlLeozeH9LugW4BbgX2Bg4XdLZtt8yZjGP8dCkM9tXS9rB9vWSZjtulF/Y/uXgOEmLmaUbYi7rZfsk4CRJL7R9Rss6DPt8vYK7B/gzSVPA2N2E00jS7ra/UTeeQvvxsNslPYr6d64L+d3csiwkPZ5yIl13sM/2xxoWcz2lRfqLVb1wDF8EPi3pn+v2q+u+sYzo6n0opXvv+BoP2lxNbEZpSD3S9t51ifMn2z6+STm2f7/pe89moQf3NwPnS/pPSqt0W+C1ktYHThqzjLkYNB300w8v6mOg0eUq8AbgnZTLMICzWXGJ3oikQyhdTbdTWpN/YftXktYCvguMG9yvknQ0pfUP9XK6Djr+qmG1vi7p7cB6kp4JvBb4XMMyuq7XlpI2oLTgj6Vckh/uBt1NULpMar/7Xbbvk/Qz2i9tfRBwgqQNKZ/zHwONLumHvI4ys3EHSTcB3wNe3qYgSUcAe1CC+79S7tFwPtA0uP8cuFTSOazcGHpji2q9lRLQ/6xun035vI/rAy3ec1U+CpwI/N+6/R+U73Sj4N7VSeL+8urlwIJVv7yD/tBr3WAQdaiMwaVbo37VNYmkvwROtH3DiOcea/uaMctZjxKEn1p3fYPSN/k/wENt/7RBnQT8KfAsStD6EnCcW3zouqqXpMts7yzpD4DXAO8APu7mA6ovBr5o+25J76CcJN7d4uptuMwNAdxiHGhEWesDa7nF4PVQGVcAO1O6eHauwecTtp/ZsJwDR+2vV1PzombL3DyIJ/XztZnt77co69u2nzjc9z9qbGCMcv6NepKof+/FlL/92IO8wxZ0y13SAdN27VwvnRq1HGrL6trhLp4J67UhcATwtLrr68Bfj/ullPR3tt80QzZQ40vDevJ6ie2/HPX8uIG9vvYe4IP1Z7omgX34hHrsuMfNdb1YMZD6bOBjtq9Si/4m4J22T5P0VOAZwPspWSptsq/WAV4ILAEWD6pj+69blPW3wPu8cjbJm22/o2lZwD22fy3p3nq1cysrr/g6FtsnqSwi+Ji661rbja4CJZ1qe7+ZMoxadK+eBjxlaPu+uq9NOuTPJD2CFV1hu1G6epra1Papkt4G96/Tdd+qDprJgg7urPyHXhfYk5IN0vSyEEr/81WSLmLlkejGfWyUbJQrgf3q9isoZ9xZU6OGDLIiOrlE7PLkpQemeQ7eo1Ga5xycUDupF7Bc0pcpXXxvk/Rw4NctqjT40j0HOMb2F2rGUhtnUoLBcibvl97b9tsHG7Z/LOnZlCuUppappOceW+v2U+CbTQuRtAelG/X7lJPrVpIOdLNUyEPq7666WRe73CkOgDo2tHbLsg6jDMw/StI3KBl+L2pRTlcnCWAN6JYZVj9on7K9V4tjf2/Ufttfb1HWqHSsRpdhtWX7Mdsva/r+M5R3HrALZeS/9clLHaV5dlmnLutVxyCeAFxv+876ZdrC9uUNy/k85T4Fz6R0ydwDXOQGqZBDZTVKVVxFWZcDT7T9i7q9HrDM9uMmLHcJJf240d+pHrsceKnta+v2Y4BTbD9gYuHqIuls4CjbZ9XtfYA3ukWabj1+MeWOcqLFlUktY1fgKEqa75WUk8SLbV/Wpk4LveU+3c8oLa7G2gTxWdwj6am2z4f7W5X3NKzPfZK2kbT2cAtiAu/soAwoA4T/1lFZXdUJOqpX7WbYEnhp7f74uu02g7z7AXsBH6gnic0p+eVtXCDpt21f0fL4YScD52hFDvYrGT/54AFUJkEtocYKSY92w8k5wEMGgR3A9n9IekjL+rwAeC/wG5RAOphAuEHDol4DnCxpkKn2A8oVeJs6PZTSet/G9qskbSdpe9ufb1jUVcDvMXSSYIJVBBZ0y31an/RalFH7U91ucsdulLPiYym514uAn7X4UCDpCZQvzCC74Q7gj5ueYSV9rNbnLFZu2R7ZtE5dkfQeyt9m0jTPTnVVr1rOEylBEGB/Sm7422c+atbyfoOV0wQbd0FJuhp4NCWz5ResCFht0nSRtDelCxPgbNtfalnOCZSZt1exouvKbj4554R6/GD27sspg72NM4IkXUeZWTr2ONIqynsYQJNEgRFlfJpyRXmAy6zghwIXtBhQ7WT29P3HLvDgPtyVci9lqvmNLctaRpnBeRolhfEASu702yao3wYAtn/S8vgjRux2k4E0Sefbfqqku1l5oKlVi0ajZyfaDWYldl2nrupVy7kceILtX9ftRZSMhEaBVNLzKYO7j6QMNG4NfKdN94dWzFRdiUdkPq1Okq62vWMH5axDSdEcZDr9O/BPg66jhmV9w/buHdRpoqSIaWUts710WrbMZeN20anj2dMDC7pbZrgrRdKmQON+32nlXSdpke37gBMlXQI0Du6SDpu2DXVAzPalDYq62vZp08p6cZO62H5q/f3wJsfNUl4XEykG0+c7qVMtq8sJHhtRrragXH218S5gN+ArtneR9Pu0zCen/cSu+404kd7/FC1PqMA3Je1o++pJ6laD+JHAkZI2AbZsGti1Yh2XZbWl/FlWvoJr2lU0aVLEsF/WsY3BQOijaDYw/gfAH1PuNz181X430OqKEhZoy712obyH8gV8FyW7ZFNK18wBtseekTZU5nmUlLXjKDM4b6Z0pbQZAPskpfU/6Kt9LmUa8xLgNNvvG7OcTi/D6vGtugkkvdz2J6afuIbKGburaPjfIOkM2y8c99i5rFctb3/KZ+tcSuB7GmUS06dnPfCB5Qxaa5cBu9S+/LFba9PKGqT3ifJ/ty1lUG6iQdBJ1Svnsyjfl9bdRZK+Bjyf0phcTrnSucD22Ksgqvt1XCZOihg67lmUCUw7Al8GdgdeaXu2NXpGldPV7Glg4bbc/4FyxtqQsg7I3ra/JWkHyiJijYM75cy8FvB6StbFVpTc4ja2BHYd9NPV7pUvUALFcmDW4F77RJ8NbCHp74ee2oDS/dTYiG6CbYBrgHEDxPr196jWdtMWwHDeeNNUxelmq1djtk+pwWaQZvtW27e0KOrO2l97HmVg7laGxk0a1mmlSSo1a+K1bcrq2PGU780VtEsXHdjQ9k8k/SklQ+yI2j02NtuvnOD9R5k4KWLA9pdrRtBulM/+IbZvb1HOGZKeQ/nODjfQGs93GBy44H4YWugIuGbacw9YZKlBuVN0sEAQ8B1KBsBgex1Kf+tY9aPM+jsQuKH+Hvy8ANi4ZZ0uAx4xeH/g94HjW5Sz+zj7VlHGxaMeT/g3f0SHn68tKBNYnjb4aVHG+pTGwuL6f/fGjuvYegG5Duvwza7+LcDmlFbtE+u+ByyaN2ZZJwEbDW1vDJzQopwn1O/M9+v38BJgp5Z1OmecfWOU8xHKHJ4fUMYDrmjzHR78LNSW+3ArYfrZtFErUqVD/AhKi32tuuteSo5ruzNiybS4UNKZdft5wCdVpnyvsn/SJavmMkn/QsnYua/WdRHlRNHGr2z/SNJaktayfa6kv2tRzlGUvO1V7ZvNzpJ+QmnFrFcfw2T9v9+SdCmlX/TfXL8NTWnF8rMrZYBQWuDjlrEI+LzLOMCvmSDVsJY33OW0FuVv/d+TlNmRS2oX5OeYrH/7rylLT5xv+9sqy29/t2WddnKdfVvr8mNJjZfIdhkb23mQFEG56noJpXt1LJLWpSw8tqnKTODhgdAtmtYJeIrtnSRdbvuvJH0QaJ3+u1CD+2zBYd2ZDxvpUEof2BNtfw+gfriOlnSo7Q81rZztd0n6IiumL7/G9rL6uMmkpC9TxgEGaVjr1X1PmfGImU3UTSDpyfV9p6YFmw0oKYhjs93o9WN6DOVv9SfA30s6Ffio7f9oWM6+wPZukakx4DJH4deSNnQH68CwcpfTvZQuvs76XiewHiWoP2tonynpqGNzSRo4bWj7etp3ia4laWPXm9LUAdqx41gN5q+jBN8zga/U7TdTAvvJMx/9AK8G3kTpCh1ezfVuStdyU4N1s34u6ZGUBJLNW5QDLNDg3nFweAXwTA/1gbksE/tySiBtHNxrGd+WdAP1ZKN20+zX9VB+re2f1hzZsQ297z6Uq5xDKSeYDSktpnGtTVlOeTErB5uf0G4qdadqS/1s4OyamfIJygqhl1EGRMedFt/V8rM/Ba5Qmek4PEeh8UqHtv8Kusm57kq9OvmR7T+foIy32H6fpKMYvR5Mm1UhP0jJ4hmcLF4M/E2D4z9OWXXzm8CrKAOhAv7QzTLdAC4ATgVeZPsolQXSXkjp6vnkuIVIelMt6yyVWfjvoyyzYpqteLlyuS2vbtcYmmVq92zPraLMTnKcVdaheIPrRByV+7z+g+0nNyijs8yUWsY2nuf86lFUlgl4OeVk/UPKYN9ZlL7T02zPOnN5KMBsQRnzmGj5Wa280uHgSyS3WOlQZc30j1PuNQBl2eYDXW8JOV8kfbPJZ3HE8c+z/Tl1vCqkylK4g/kNX3WDVE1JV7gOYNcT2M3A1m632uzFwDNs3yHpaZTlqN9A+Uw+1vZYjSJJH6BcNT+WcvXwDcrJ5wK3WPZjYEG23Ds229T+ttP+u8pxfhNwmqT/prQefpPSH9xEl5kpAOtIOoahKecAbjhZaA58kxIA9/XKE9mWSfrIGMcPus2WU04K0ydXjUVlDZItbf9j3b6IMlBvylrjbRwDHOaaOqey0NYxtOue69Klks6idKkMX52M1S3juqzDIIh3cWVS+6GPt92m2wOG1v6v3Ws3tgns1SLbg/kSf0RZQO4M4Iw6PjSWwdWRysJlSyn/738M/LOkO91yItmDIbgP+u+na9N/P9DJ4GXt2tmBspYEtFtwyDM8bus0yqj9cQwt0LUAbD/TIKrt945x/F2UBcImDcpvoQy8DawN/A6lS+tEhvqWG1jfQznRtr9WB+fn27qUft/hE3vjPvdpVyaSdBtlvspVsx850jXAsSoLdZ1IWYCsybjHcDwYHtNrM9i/SNJilxu070m5X/BAm9i6HmWMa8P689+UjJlWeh/c52hwr5McZ3Wz4FDXmSn32j664TFzRkPrC2nEsusef4XJv6CboLy2V76J8vm19XbHBAH5eknvZMVS0C+njA3MK3eXWz7qyuRYWlyZ2D4OOE7S9pRF0S6v3ZvHeoxJQx3Hg1Modxu7nTLe9e8Akh5Ng6V665Xy4ygDsRdS+t+PHAwat9X74D5HJh28HDiR0k0w6Ne8iRJkxg7uXX1Ya9YBwOckvRb4F1buk75j5IFzr6vbonUVlDce3rD9+qHNqZZ1+xPgrygtYlOCRNvb7HVGZfXMoyjZZlDqdYibr+/U6ZVJ7Svfof7cTslXP0zSq22/ZNaDO2T7b1RuHbg58OWhK8u1KH3v49qakgL9XUoMuBG4c9YjxtD7AdWu1Q/WV9zBWieacMGhLkn6HiumwE9nN78pRidaZiGNKuc624+e4bn/tP2oMcs5Gfia7WOn7X81sIft/RvWq7PPU9dqJtAnWfmK4mVufpu9f6FkfwyX8zu2/7BFnT5EWe7jq5S+94uGnrvW9vYzHryAqVyWPo5yNfMUyprud1Amko1aYHCV0nJvqOMc50kXHOrMqrJN5tFnqROoJswGulDSq2YIyhfNcMwohwKflfRSSsCC0r2zDiWHvpGOP09dm7I9vKbLR2vaXlNdXplcDrzD9qhu0Ce1LHPe1Vb/lZLupHTp3EU5iT2JMgmzsQT3drrKcT6Csk7OVrVFuDtllHzeaMXqe8PuokyHv3V114fusoE6Ccr1b/AUSU9nxbo9X7D91Qnq1lnOfMd+VOeDnFK396fByqwqMzhfQ1mr/grKvVwb36FomvsnrdW67Qp82PYNC/DkOBZJb2RFi/1XlD73CygrV7YeUE23TAtd5u3W/O3BgkPfcosFh7ok6QuUMYBBH+kelHGBbSnrXX98hkPnqj7DefwTrZhZyxgOyldNGJQ70WXOfJdU1pk/ivJ5MCXgvHHcbjKVpXl/RWmp7w1833ablv9wmZdT5insBHyUktW1n+2Rt9FcE0g6kpLbfoHtmzsrN8G9HUlTALZva3HsrAHK83jXI0lfoqSp/bBub0ZZzGh/4Dx3dK/PBvW5j9KaFSVV7OeDp2i/Ts2CsKqceU9b639NM23C0GLKPWYnPTlfbHtXSf8PuMn28V2c9Pso3TIN1EGPLhYh++AszxLTPa4AAAHySURBVJmV84pXt60Ggb26te67Q9Kkl9SNzVEq60IxFznzE6uBcya2/a4xixqeMHTvqFTWFu6W9DbKoOzTVG543up+rH2X4N5MJ4uQLcTMiCFfk/R5VgSWF9Z969NBelasZC5y5rswarByfeAgyrLS4wb3LicMDfwR5VZ0B9m+RdLWwPtblNN76ZZpQOW2fCstQlb3T1HyXMdaelR1QaX6+MXDl9+S/tYtb9bchXp18kJW5DZ/Azhjptmh0V5X6ZlzSdLDgUMogf1U4IPzMbA+YnD2+DozNGaw1nxXYA3zkFEDnrXfvcml4fCl+PR7uO7VpmJdcXG67UPrz+kJ7HPmQkmvmr6zRXpm5yRtIundlNTDxZQ7j711njKmoKyZv5QS2Pdm9q7NIN0yTXW1CJlmeDxqe7WQdL7tp+qBN1pe4wcuF7BOc+a7Iun9lLuCHQP8thfAEsTAjkODs8czzye/NUG6ZRoYytx4wFOUtdnHar3Plt43XyP/WqBL/T4YLLT0TEm/pkymu5cFcqJfKN+TNUmC+zxYRXrf2CeJjuvU6brwEV2a1rAa/t7kynIG6ZaZBws0va/rdeEjOrNAvzMLWgZUY6DrdeEjYh6lWyaAfs8EjXgwSnCPiOihdMtERPRQgntERA8luEdE9FCCe0RED/0vHV6dW2rs42wAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"WRsjFCc-ncqI","colab":{"base_uri":"https://localhost:8080/","height":277},"executionInfo":{"status":"ok","timestamp":1620768717384,"user_tz":240,"elapsed":3562,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"a8a7284f-c18f-456e-dd9a-3a6095ff1a56"},"source":["df.describe()"],"execution_count":277,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>height_m</th>\n","      <th>weight_kg</th>\n","      <th>total_points</th>\n","      <th>hp</th>\n","      <th>attack</th>\n","      <th>defense</th>\n","      <th>sp_attack</th>\n","      <th>sp_defense</th>\n","      <th>speed</th>\n","      <th>catch_rate</th>\n","      <th>base_experience</th>\n","      <th>egg_cycles</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>1044.000000</td>\n","      <td>1044.000000</td>\n","      <td>1044.000000</td>\n","      <td>1044.000000</td>\n","      <td>1044.000000</td>\n","      <td>1044.000000</td>\n","      <td>1044.000000</td>\n","      <td>1044.000000</td>\n","      <td>1044.000000</td>\n","      <td>1044.000000</td>\n","      <td>1044.000000</td>\n","      <td>1044.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>1.279598</td>\n","      <td>71.216571</td>\n","      <td>438.696360</td>\n","      <td>69.890805</td>\n","      <td>80.443487</td>\n","      <td>74.502874</td>\n","      <td>72.981801</td>\n","      <td>72.128352</td>\n","      <td>68.749042</td>\n","      <td>92.919540</td>\n","      <td>155.250000</td>\n","      <td>31.393678</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.385909</td>\n","      <td>132.259911</td>\n","      <td>120.188908</td>\n","      <td>26.061943</td>\n","      <td>32.430642</td>\n","      <td>30.799182</td>\n","      <td>32.721971</td>\n","      <td>27.568146</td>\n","      <td>30.165066</td>\n","      <td>75.916006</td>\n","      <td>80.077143</td>\n","      <td>30.389722</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.100000</td>\n","      <td>0.100000</td>\n","      <td>175.000000</td>\n","      <td>1.000000</td>\n","      <td>5.000000</td>\n","      <td>5.000000</td>\n","      <td>10.000000</td>\n","      <td>20.000000</td>\n","      <td>5.000000</td>\n","      <td>3.000000</td>\n","      <td>36.000000</td>\n","      <td>5.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.600000</td>\n","      <td>9.000000</td>\n","      <td>330.000000</td>\n","      <td>50.000000</td>\n","      <td>55.000000</td>\n","      <td>50.000000</td>\n","      <td>50.000000</td>\n","      <td>50.000000</td>\n","      <td>45.000000</td>\n","      <td>45.000000</td>\n","      <td>67.000000</td>\n","      <td>20.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>1.000000</td>\n","      <td>29.500000</td>\n","      <td>458.000000</td>\n","      <td>68.000000</td>\n","      <td>77.000000</td>\n","      <td>70.000000</td>\n","      <td>65.000000</td>\n","      <td>70.000000</td>\n","      <td>65.000000</td>\n","      <td>60.000000</td>\n","      <td>161.000000</td>\n","      <td>20.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>1.600000</td>\n","      <td>70.500000</td>\n","      <td>515.000000</td>\n","      <td>82.000000</td>\n","      <td>100.000000</td>\n","      <td>90.000000</td>\n","      <td>95.000000</td>\n","      <td>90.000000</td>\n","      <td>90.000000</td>\n","      <td>127.000000</td>\n","      <td>201.500000</td>\n","      <td>25.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>20.000000</td>\n","      <td>999.900000</td>\n","      <td>780.000000</td>\n","      <td>255.000000</td>\n","      <td>190.000000</td>\n","      <td>230.000000</td>\n","      <td>194.000000</td>\n","      <td>230.000000</td>\n","      <td>200.000000</td>\n","      <td>255.000000</td>\n","      <td>608.000000</td>\n","      <td>120.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          height_m    weight_kg  ...  base_experience   egg_cycles\n","count  1044.000000  1044.000000  ...      1044.000000  1044.000000\n","mean      1.279598    71.216571  ...       155.250000    31.393678\n","std       1.385909   132.259911  ...        80.077143    30.389722\n","min       0.100000     0.100000  ...        36.000000     5.000000\n","25%       0.600000     9.000000  ...        67.000000    20.000000\n","50%       1.000000    29.500000  ...       161.000000    20.000000\n","75%       1.600000    70.500000  ...       201.500000    25.000000\n","max      20.000000   999.900000  ...       608.000000   120.000000\n","\n","[8 rows x 12 columns]"]},"metadata":{"tags":[]},"execution_count":277}]},{"cell_type":"code","metadata":{"id":"k57Lwowgnib4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768717384,"user_tz":240,"elapsed":3547,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"6484d00c-6668-4e9b-d233-b6064dc5834a"},"source":["df.info()"],"execution_count":278,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 1044 entries, 0 to 1044\n","Data columns (total 17 columns):\n"," #   Column           Non-Null Count  Dtype  \n","---  ------           --------------  -----  \n"," 0   status           1044 non-null   object \n"," 1   type_1           1044 non-null   object \n"," 2   type_2           552 non-null    object \n"," 3   height_m         1044 non-null   float64\n"," 4   weight_kg        1044 non-null   float64\n"," 5   total_points     1044 non-null   int64  \n"," 6   hp               1044 non-null   int64  \n"," 7   attack           1044 non-null   int64  \n"," 8   defense          1044 non-null   int64  \n"," 9   sp_attack        1044 non-null   int64  \n"," 10  sp_defense       1044 non-null   int64  \n"," 11  speed            1044 non-null   int64  \n"," 12  catch_rate       1044 non-null   int64  \n"," 13  base_experience  1044 non-null   int64  \n"," 14  growth_rate      1044 non-null   object \n"," 15  egg_type_1       1044 non-null   object \n"," 16  egg_cycles       1044 non-null   int64  \n","dtypes: float64(2), int64(10), object(5)\n","memory usage: 146.8+ KB\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"YjyAOAWt8OHT"},"source":["# **Make 3 variations of the dataset: df1, df2, df3**"]},{"cell_type":"markdown","metadata":{"id":"BHLd658H9JNV"},"source":["1.One uses only type_1 and drops type_2<br>2.Second removes all pokemon with a second type<br>3.Third dupes pokemon with two types into two datapoints with the different types (what we did originally) "]},{"cell_type":"markdown","metadata":{"id":"kNMU0a3K9i4d"},"source":["**Dataset #1: of only type_1**"]},{"cell_type":"code","metadata":{"id":"8bSWfkZb9G9H","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768717554,"user_tz":240,"elapsed":3707,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"fb003835-af46-42e9-d325-9557c0c45288"},"source":["df1 = df.drop(labels=['type_2'],axis=1)\n","df1.info() # 1044 entries"],"execution_count":279,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 1044 entries, 0 to 1044\n","Data columns (total 16 columns):\n"," #   Column           Non-Null Count  Dtype  \n","---  ------           --------------  -----  \n"," 0   status           1044 non-null   object \n"," 1   type_1           1044 non-null   object \n"," 2   height_m         1044 non-null   float64\n"," 3   weight_kg        1044 non-null   float64\n"," 4   total_points     1044 non-null   int64  \n"," 5   hp               1044 non-null   int64  \n"," 6   attack           1044 non-null   int64  \n"," 7   defense          1044 non-null   int64  \n"," 8   sp_attack        1044 non-null   int64  \n"," 9   sp_defense       1044 non-null   int64  \n"," 10  speed            1044 non-null   int64  \n"," 11  catch_rate       1044 non-null   int64  \n"," 12  base_experience  1044 non-null   int64  \n"," 13  growth_rate      1044 non-null   object \n"," 14  egg_type_1       1044 non-null   object \n"," 15  egg_cycles       1044 non-null   int64  \n","dtypes: float64(2), int64(10), object(4)\n","memory usage: 138.7+ KB\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"dmTckxAU-bqE"},"source":["**Dataset #2: remove pokemons with second type**"]},{"cell_type":"code","metadata":{"id":"yo5hdXx4-kNq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768717555,"user_tz":240,"elapsed":3703,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"022047f0-629f-417f-b1fc-7bedb943a3be"},"source":["df2_mask = df['type_2'].isnull()\n","df2 = df[df2_mask].drop(labels=['type_2'],axis=1) # also remove type_2 column\n","df2.info() # 492 entries"],"execution_count":280,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 492 entries, 4 to 1041\n","Data columns (total 16 columns):\n"," #   Column           Non-Null Count  Dtype  \n","---  ------           --------------  -----  \n"," 0   status           492 non-null    object \n"," 1   type_1           492 non-null    object \n"," 2   height_m         492 non-null    float64\n"," 3   weight_kg        492 non-null    float64\n"," 4   total_points     492 non-null    int64  \n"," 5   hp               492 non-null    int64  \n"," 6   attack           492 non-null    int64  \n"," 7   defense          492 non-null    int64  \n"," 8   sp_attack        492 non-null    int64  \n"," 9   sp_defense       492 non-null    int64  \n"," 10  speed            492 non-null    int64  \n"," 11  catch_rate       492 non-null    int64  \n"," 12  base_experience  492 non-null    int64  \n"," 13  growth_rate      492 non-null    object \n"," 14  egg_type_1       492 non-null    object \n"," 15  egg_cycles       492 non-null    int64  \n","dtypes: float64(2), int64(10), object(4)\n","memory usage: 65.3+ KB\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"0S6uTkI0_kD3"},"source":["**Dataset #3: dupe pokemons with second type**"]},{"cell_type":"code","metadata":{"id":"0SoflOXv_oWN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768717555,"user_tz":240,"elapsed":3697,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"718c7643-c717-401b-99d8-d7ece2d17b9f"},"source":["without_type2 = df.type_2.isna()\n","df_withtype2 = df[~without_type2]\n","df_withtype2 = df_withtype2.drop(labels=['type_1'], axis = 1)\n","df_withtype2 = df_withtype2.rename({'type_2': 'type_1'}, axis=1)\n","df3 = pd.concat([df, df_withtype2], ignore_index=True)\n","df3 = df3.drop(labels=['type_2'], axis = 1)\n","df3.info() # 1596 entries"],"execution_count":281,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1596 entries, 0 to 1595\n","Data columns (total 16 columns):\n"," #   Column           Non-Null Count  Dtype  \n","---  ------           --------------  -----  \n"," 0   status           1596 non-null   object \n"," 1   type_1           1596 non-null   object \n"," 2   height_m         1596 non-null   float64\n"," 3   weight_kg        1596 non-null   float64\n"," 4   total_points     1596 non-null   int64  \n"," 5   hp               1596 non-null   int64  \n"," 6   attack           1596 non-null   int64  \n"," 7   defense          1596 non-null   int64  \n"," 8   sp_attack        1596 non-null   int64  \n"," 9   sp_defense       1596 non-null   int64  \n"," 10  speed            1596 non-null   int64  \n"," 11  catch_rate       1596 non-null   int64  \n"," 12  base_experience  1596 non-null   int64  \n"," 13  growth_rate      1596 non-null   object \n"," 14  egg_type_1       1596 non-null   object \n"," 15  egg_cycles       1596 non-null   int64  \n","dtypes: float64(2), int64(10), object(4)\n","memory usage: 199.6+ KB\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"fCr_axOTBEBp"},"source":["**Preview type_1 distribution of pokemon.**"]},{"cell_type":"code","metadata":{"id":"xyRt5pRwBurq","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1620768717711,"user_tz":240,"elapsed":3847,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"618bc3a7-5e24-4f54-c87e-161558e16000"},"source":["df1['type_1'].value_counts().sort_index().plot.bar()"],"execution_count":282,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fc541528c50>"]},"metadata":{"tags":[]},"execution_count":282},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"d9BD8JC2BxkS","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1620768718184,"user_tz":240,"elapsed":4313,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"caa449ab-9768-4209-9b67-e3c9a7eba0db"},"source":["df2['type_1'].value_counts().sort_index().plot.bar()"],"execution_count":283,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"cSFtPTToBxv2","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1620768718366,"user_tz":240,"elapsed":4489,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"140d190b-3a72-489f-d42d-b04043efd2e0"},"source":["df3['type_1'].value_counts().sort_index().plot.bar()"],"execution_count":284,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"Xvv4zkV4Co1n"},"source":["# **Make 2 versions of each dataset: A (numerical) and B (catergorical)**"]},{"cell_type":"markdown","metadata":{"id":"IQJz9D1-DF1c"},"source":["There should be 6 datasets total:<br>\n","df1a, df1b:  dataset #1, 1a is numerical and 1b is categorical<br>\n","df2a, df2b:  dataset #2, 2a is numerical and 2b is categorical<br>\n","df3a, df3b:  dataset #3, 3a is numerical and 3b is categorical<br>"]},{"cell_type":"markdown","metadata":{"id":"9vr9wALUD_YY"},"source":["**Create the numerical versions of the 3 datasets by converting categorical features to binary via one hot encoding.**"]},{"cell_type":"code","metadata":{"id":"QD1RZBsHE_CR","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1620768718493,"user_tz":240,"elapsed":4610,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"0270cf34-09c8-4c3e-989a-a7c4e3ed53c7"},"source":["# converting df1 into df1a\n","df_r_Binary1 = df1.loc[: , ['status']].astype('category')\n","df_r_Binary1 = pd.get_dummies(df_r_Binary1)\n","\n","df_r_Binary2 = df1.loc[: , ['egg_type_1']].astype('category')\n","df_r_Binary2 = pd.get_dummies(df_r_Binary2)\n","\n","df_r_Binary3 = df1.loc[: , ['growth_rate']].astype('category')\n","df_r_Binary3 = pd.get_dummies(df_r_Binary3)\n","\n","df1a = df1.drop(['status','egg_type_1','growth_rate'], axis=1).join(df_r_Binary1).join(df_r_Binary2).join(df_r_Binary3)\n","df1a # number of columns increased from 16 --> 38 because 3 categorical columns have been split into binary columns"],"execution_count":285,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>type_1</th>\n","      <th>height_m</th>\n","      <th>weight_kg</th>\n","      <th>total_points</th>\n","      <th>hp</th>\n","      <th>attack</th>\n","      <th>defense</th>\n","      <th>sp_attack</th>\n","      <th>sp_defense</th>\n","      <th>speed</th>\n","      <th>catch_rate</th>\n","      <th>base_experience</th>\n","      <th>egg_cycles</th>\n","      <th>status_Legendary</th>\n","      <th>status_Mythical</th>\n","      <th>status_Normal</th>\n","      <th>status_Sub Legendary</th>\n","      <th>egg_type_1_Amorphous</th>\n","      <th>egg_type_1_Bug</th>\n","      <th>egg_type_1_Ditto</th>\n","      <th>egg_type_1_Dragon</th>\n","      <th>egg_type_1_Fairy</th>\n","      <th>egg_type_1_Field</th>\n","      <th>egg_type_1_Flying</th>\n","      <th>egg_type_1_Grass</th>\n","      <th>egg_type_1_Human-Like</th>\n","      <th>egg_type_1_Mineral</th>\n","      <th>egg_type_1_Monster</th>\n","      <th>egg_type_1_Undiscovered</th>\n","      <th>egg_type_1_Water 1</th>\n","      <th>egg_type_1_Water 2</th>\n","      <th>egg_type_1_Water 3</th>\n","      <th>growth_rate_Erratic</th>\n","      <th>growth_rate_Fast</th>\n","      <th>growth_rate_Fluctuating</th>\n","      <th>growth_rate_Medium Fast</th>\n","      <th>growth_rate_Medium Slow</th>\n","      <th>growth_rate_Slow</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Grass</td>\n","      <td>0.7</td>\n","      <td>6.9</td>\n","      <td>318</td>\n","      <td>45</td>\n","      <td>49</td>\n","      <td>49</td>\n","      <td>65</td>\n","      <td>65</td>\n","      <td>45</td>\n","      <td>45</td>\n","      <td>64</td>\n","      <td>20</td>\n","      <td>0</td>\n","      <td>0</td>\n","      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<td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Fire</td>\n","      <td>0.6</td>\n","      <td>8.5</td>\n","      <td>309</td>\n","      <td>39</td>\n","      <td>52</td>\n","      <td>43</td>\n","      <td>60</td>\n","      <td>50</td>\n","      <td>65</td>\n","      <td>45</td>\n","      <td>62</td>\n","      <td>20</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1040</th>\n","      <td>Ice</td>\n","      <td>2.2</td>\n","      <td>800.0</td>\n","      <td>580</td>\n","      <td>100</td>\n","      <td>145</td>\n","      <td>130</td>\n","      <td>65</td>\n","      <td>110</td>\n","      <td>30</td>\n","      <td>3</td>\n","      <td>290</td>\n","      <td>120</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1041</th>\n","      <td>Ghost</td>\n","      <td>2.0</td>\n","      <td>44.5</td>\n","      <td>580</td>\n","      <td>100</td>\n","      <td>65</td>\n","      <td>60</td>\n","      <td>145</td>\n","      <td>80</td>\n","      <td>130</td>\n","      <td>3</td>\n","      <td>290</td>\n","      <td>120</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1042</th>\n","      <td>Psychic</td>\n","      <td>1.1</td>\n","      <td>7.7</td>\n","      <td>500</td>\n","      <td>100</td>\n","      <td>80</td>\n","      <td>80</td>\n","      <td>80</td>\n","      <td>80</td>\n","      <td>80</td>\n","      <td>3</td>\n","      <td>250</td>\n","      <td>120</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1043</th>\n","      <td>Psychic</td>\n","      <td>2.4</td>\n","      <td>809.1</td>\n","      <td>680</td>\n","      <td>100</td>\n","      <td>165</td>\n","      <td>150</td>\n","      <td>85</td>\n","      <td>130</td>\n","      <td>50</td>\n","      <td>3</td>\n","      <td>340</td>\n","      <td>120</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1044</th>\n","      <td>Psychic</td>\n","      <td>2.4</td>\n","      <td>53.6</td>\n","      <td>680</td>\n","      <td>100</td>\n","      <td>85</td>\n","      <td>80</td>\n","      <td>165</td>\n","      <td>100</td>\n","      <td>150</td>\n","      <td>3</td>\n","      <td>340</td>\n","      <td>120</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1044 rows × 38 columns</p>\n","</div>"],"text/plain":["       type_1  height_m  ...  growth_rate_Medium Slow  growth_rate_Slow\n","0       Grass       0.7  ...                        1                 0\n","1       Grass       1.0  ...                        1                 0\n","2       Grass       2.0  ...                        1                 0\n","3       Grass       2.4  ...                        1                 0\n","4        Fire       0.6  ...                        1                 0\n","...       ...       ...  ...                      ...               ...\n","1040      Ice       2.2  ...                        0                 1\n","1041    Ghost       2.0  ...                        0                 1\n","1042  Psychic       1.1  ...                        0                 1\n","1043  Psychic       2.4  ...                        0                 1\n","1044  Psychic       2.4  ...                        0                 1\n","\n","[1044 rows x 38 columns]"]},"metadata":{"tags":[]},"execution_count":285}]},{"cell_type":"code","metadata":{"id":"2Hg0-ZSBE_Mq","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1620768718609,"user_tz":240,"elapsed":4720,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"dc10cdcc-df81-4598-992b-a92de0f60460"},"source":["# converting df2 into df2a\n","df_r_Binary1 = df2.loc[: , ['status']].astype('category')\n","df_r_Binary1 = pd.get_dummies(df_r_Binary1)\n","df_r_Binary2 = df2.loc[: , ['egg_type_1']].astype('category')\n","df_r_Binary2 = pd.get_dummies(df_r_Binary2)\n","df_r_Binary3 = df2.loc[: , ['growth_rate']].astype('category')\n","df_r_Binary3 = pd.get_dummies(df_r_Binary3)\n","\n","df2a = df2.drop(['status','egg_type_1','growth_rate'], axis=1).join(df_r_Binary1).join(df_r_Binary2).join(df_r_Binary3)\n","df2a # number of columns increased from 16 --> 38 because 3 categorical columns have been split into binary columns"],"execution_count":286,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>type_1</th>\n","      <th>height_m</th>\n","      <th>weight_kg</th>\n","      <th>total_points</th>\n","      <th>hp</th>\n","      <th>attack</th>\n","      <th>defense</th>\n","      <th>sp_attack</th>\n","      <th>sp_defense</th>\n","      <th>speed</th>\n","      <th>catch_rate</th>\n","      <th>base_experience</th>\n","      <th>egg_cycles</th>\n","      <th>status_Legendary</th>\n","      <th>status_Mythical</th>\n","      <th>status_Normal</th>\n","      <th>status_Sub Legendary</th>\n","      <th>egg_type_1_Amorphous</th>\n","      <th>egg_type_1_Bug</th>\n","      <th>egg_type_1_Ditto</th>\n","      <th>egg_type_1_Dragon</th>\n","      <th>egg_type_1_Fairy</th>\n","      <th>egg_type_1_Field</th>\n","      <th>egg_type_1_Flying</th>\n","      <th>egg_type_1_Grass</th>\n","      <th>egg_type_1_Human-Like</th>\n","      <th>egg_type_1_Mineral</th>\n","      <th>egg_type_1_Monster</th>\n","      <th>egg_type_1_Undiscovered</th>\n","      <th>egg_type_1_Water 1</th>\n","      <th>egg_type_1_Water 2</th>\n","      <th>egg_type_1_Water 3</th>\n","      <th>growth_rate_Erratic</th>\n","      <th>growth_rate_Fast</th>\n","      <th>growth_rate_Fluctuating</th>\n","      <th>growth_rate_Medium Fast</th>\n","      <th>growth_rate_Medium Slow</th>\n","      <th>growth_rate_Slow</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>4</th>\n","      <td>Fire</td>\n","      <td>0.6</td>\n","      <td>8.5</td>\n","      <td>309</td>\n","      <td>39</td>\n","      <td>52</td>\n","      <td>43</td>\n","      <td>60</td>\n","      <td>50</td>\n","      <td>65</td>\n","      <td>45</td>\n","      <td>62</td>\n","      <td>20</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Fire</td>\n","      <td>1.1</td>\n","      <td>19.0</td>\n","      <td>405</td>\n","      <td>58</td>\n","      <td>64</td>\n","      <td>58</td>\n","      <td>80</td>\n","      <td>65</td>\n","      <td>80</td>\n","      <td>45</td>\n","      <td>142</td>\n","      <td>20</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Water</td>\n","      <td>0.5</td>\n","      <td>9.0</td>\n","      <td>314</td>\n","      <td>44</td>\n","      <td>48</td>\n","      <td>65</td>\n","      <td>50</td>\n","      <td>64</td>\n","      <td>43</td>\n","      <td>45</td>\n","      <td>63</td>\n","      <td>20</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Water</td>\n","      <td>1.0</td>\n","      <td>22.5</td>\n","      <td>405</td>\n","      <td>59</td>\n","      <td>63</td>\n","      <td>80</td>\n","      <td>65</td>\n","      <td>80</td>\n","      <td>58</td>\n","      <td>45</td>\n","      <td>142</td>\n","      <td>20</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Water</td>\n","      <td>1.6</td>\n","      <td>85.5</td>\n","      <td>530</td>\n","      <td>79</td>\n","      <td>83</td>\n","      <td>100</td>\n","      <td>85</td>\n","      <td>105</td>\n","      <td>78</td>\n","      <td>45</td>\n","      <td>239</td>\n","      <td>20</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      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     <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1038</th>\n","      <td>Electric</td>\n","      <td>1.2</td>\n","      <td>145.0</td>\n","      <td>580</td>\n","      <td>80</td>\n","      <td>100</td>\n","      <td>50</td>\n","      <td>100</td>\n","      <td>50</td>\n","      <td>200</td>\n","      <td>3</td>\n","      <td>290</td>\n","      <td>120</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1039</th>\n","      <td>Dragon</td>\n","      <td>2.1</td>\n","      <td>200.0</td>\n","      <td>580</td>\n","      <td>200</td>\n","      <td>100</td>\n","      <td>50</td>\n","      <td>100</td>\n","      <td>50</td>\n","      <td>80</td>\n","      <td>3</td>\n","      <td>290</td>\n","      <td>120</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1040</th>\n","      <td>Ice</td>\n","      <td>2.2</td>\n","      <td>800.0</td>\n","      <td>580</td>\n","      <td>100</td>\n","      <td>145</td>\n","      <td>130</td>\n","      <td>65</td>\n","      <td>110</td>\n","      <td>30</td>\n","      <td>3</td>\n","      <td>290</td>\n","      <td>120</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1041</th>\n","      <td>Ghost</td>\n","      <td>2.0</td>\n","      <td>44.5</td>\n","      <td>580</td>\n","      <td>100</td>\n","      <td>65</td>\n","      <td>60</td>\n","      <td>145</td>\n","      <td>80</td>\n","      <td>130</td>\n","      <td>3</td>\n","      <td>290</td>\n","      <td>120</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>492 rows × 38 columns</p>\n","</div>"],"text/plain":["        type_1  height_m  ...  growth_rate_Medium Slow  growth_rate_Slow\n","4         Fire       0.6  ...                        1                 0\n","5         Fire       1.1  ...                        1                 0\n","9        Water       0.5  ...                        1                 0\n","10       Water       1.0  ...                        1                 0\n","11       Water       1.6  ...                        1                 0\n","...        ...       ...  ...                      ...               ...\n","1034  Fighting       0.6  ...                        0                 1\n","1038  Electric       1.2  ...                        0                 1\n","1039    Dragon       2.1  ...                        0                 1\n","1040       Ice       2.2  ...                        0                 1\n","1041     Ghost       2.0  ...                        0                 1\n","\n","[492 rows x 38 columns]"]},"metadata":{"tags":[]},"execution_count":286}]},{"cell_type":"code","metadata":{"id":"tlvFazCDE_RK","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1620768718609,"user_tz":240,"elapsed":4714,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"363276e3-b574-4d5f-b7e2-0af656e3a771"},"source":["# converting df3 into df3a\n","df_r_Binary1 = df3.loc[: , ['status']].astype('category')\n","df_r_Binary1 = pd.get_dummies(df_r_Binary1)\n","df_r_Binary2 = df3.loc[: , ['egg_type_1']].astype('category')\n","df_r_Binary2 = pd.get_dummies(df_r_Binary2)\n","df_r_Binary3 = df3.loc[: , ['growth_rate']].astype('category')\n","df_r_Binary3 = pd.get_dummies(df_r_Binary3)\n","\n","df3a = df3.drop(['status','egg_type_1','growth_rate'], axis=1).join(df_r_Binary1).join(df_r_Binary2).join(df_r_Binary3)\n","df3a # number of columns increased from 16 --> 38 because 3 categorical columns have been split into binary columns"],"execution_count":287,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>type_1</th>\n","      <th>height_m</th>\n","      <th>weight_kg</th>\n","      <th>total_points</th>\n","      <th>hp</th>\n","      <th>attack</th>\n","      <th>defense</th>\n","      <th>sp_attack</th>\n","      <th>sp_defense</th>\n","      <th>speed</th>\n","      <th>catch_rate</th>\n","      <th>base_experience</th>\n","      <th>egg_cycles</th>\n","      <th>status_Legendary</th>\n","      <th>status_Mythical</th>\n","      <th>status_Normal</th>\n","      <th>status_Sub Legendary</th>\n","      <th>egg_type_1_Amorphous</th>\n","      <th>egg_type_1_Bug</th>\n","      <th>egg_type_1_Ditto</th>\n","      <th>egg_type_1_Dragon</th>\n","      <th>egg_type_1_Fairy</th>\n","      <th>egg_type_1_Field</th>\n","      <th>egg_type_1_Flying</th>\n","      <th>egg_type_1_Grass</th>\n","      <th>egg_type_1_Human-Like</th>\n","      <th>egg_type_1_Mineral</th>\n","      <th>egg_type_1_Monster</th>\n","      <th>egg_type_1_Undiscovered</th>\n","      <th>egg_type_1_Water 1</th>\n","      <th>egg_type_1_Water 2</th>\n","      <th>egg_type_1_Water 3</th>\n","      <th>growth_rate_Erratic</th>\n","      <th>growth_rate_Fast</th>\n","      <th>growth_rate_Fluctuating</th>\n","      <th>growth_rate_Medium Fast</th>\n","      <th>growth_rate_Medium Slow</th>\n","      <th>growth_rate_Slow</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Grass</td>\n","      <td>0.7</td>\n","      <td>6.9</td>\n","      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<td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1591</th>\n","      <td>Water</td>\n","      <td>1.9</td>\n","      <td>105.0</td>\n","      <td>550</td>\n","      <td>100</td>\n","      <td>130</td>\n","      <td>100</td>\n","      <td>63</td>\n","      <td>60</td>\n","      <td>97</td>\n","      <td>3</td>\n","      <td>275</td>\n","      <td>120</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1592</th>\n","      <td>Grass</td>\n","      <td>1.8</td>\n","      <td>70.0</td>\n","      <td>600</td>\n","      <td>105</td>\n","      <td>120</td>\n","      <td>105</td>\n","      <td>70</td>\n","      <td>95</td>\n","      <td>105</td>\n","      <td>3</td>\n","      <td>300</td>\n","      <td>120</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1593</th>\n","      <td>Grass</td>\n","      <td>1.1</td>\n","      <td>7.7</td>\n","      <td>500</td>\n","      <td>100</td>\n","      <td>80</td>\n","      <td>80</td>\n","      <td>80</td>\n","      <td>80</td>\n","      <td>80</td>\n","      <td>3</td>\n","      <td>250</td>\n","      <td>120</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1594</th>\n","      <td>Ice</td>\n","      <td>2.4</td>\n","      <td>809.1</td>\n","      <td>680</td>\n","      <td>100</td>\n","      <td>165</td>\n","      <td>150</td>\n","      <td>85</td>\n","      <td>130</td>\n","      <td>50</td>\n","      <td>3</td>\n","      <td>340</td>\n","      <td>120</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1595</th>\n","      <td>Ghost</td>\n","      <td>2.4</td>\n","      <td>53.6</td>\n","      <td>680</td>\n","      <td>100</td>\n","      <td>85</td>\n","      <td>80</td>\n","      <td>165</td>\n","      <td>100</td>\n","      <td>150</td>\n","      <td>3</td>\n","      <td>340</td>\n","      <td>120</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1596 rows × 38 columns</p>\n","</div>"],"text/plain":["     type_1  height_m  ...  growth_rate_Medium Slow  growth_rate_Slow\n","0     Grass       0.7  ...                        1                 0\n","1     Grass       1.0  ...                        1                 0\n","2     Grass       2.0  ...                        1                 0\n","3     Grass       2.4  ...                        1                 0\n","4      Fire       0.6  ...                        1                 0\n","...     ...       ...  ...                      ...               ...\n","1591  Water       1.9  ...                        0                 1\n","1592  Grass       1.8  ...                        0                 1\n","1593  Grass       1.1  ...                        0                 1\n","1594    Ice       2.4  ...                        0                 1\n","1595  Ghost       2.4  ...                        0                 1\n","\n","[1596 rows x 38 columns]"]},"metadata":{"tags":[]},"execution_count":287}]},{"cell_type":"markdown","metadata":{"id":"y5XB4_SBIkB8"},"source":["**Create the categorical versions of the 3 datasets by binning numerical values into categorical bins.**"]},{"cell_type":"markdown","metadata":{"id":"mD8qaWR8Vop9"},"source":["# **DF1B**"]},{"cell_type":"markdown","metadata":{"id":"5XOcnt4kOZQJ"},"source":["**Height:**"]},{"cell_type":"code","metadata":{"id":"j8jLvE0qEnXW","executionInfo":{"status":"ok","timestamp":1620768718734,"user_tz":240,"elapsed":4835,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["def check_outliers(x):\n","  q25 = x.quantile(.25)\n","  q75 = x.quantile(.75)\n","  IQR = q75-q25\n","  l = q25 - IQR*2\n","  u = q75 + IQR*2\n","  return l, u"],"execution_count":288,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OLIxnu3REqKZ","executionInfo":{"status":"ok","timestamp":1620768718734,"user_tz":240,"elapsed":4830,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"72c9ca0f-4cd1-4170-9e19-b23e2530bf9c"},"source":["lower, upper = check_outliers(df1['height_m'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":289,"outputs":[{"output_type":"stream","text":["lower = -1.4, upper=3.6\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XRtXB1P7E0-D","executionInfo":{"status":"ok","timestamp":1620768718734,"user_tz":240,"elapsed":4827,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df1['height_m']< lower"],"execution_count":290,"outputs":[]},{"cell_type":"code","metadata":{"id":"qQ_yoaFCE-qm","executionInfo":{"status":"ok","timestamp":1620768718735,"user_tz":240,"elapsed":4826,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df1['height_m']>upper"],"execution_count":291,"outputs":[]},{"cell_type":"code","metadata":{"id":"YN7nVx0GGr05","executionInfo":{"status":"ok","timestamp":1620768718735,"user_tz":240,"elapsed":4825,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df1['new_height_m']= df1['height_m']\n","for i in df1[mask_upper].index:\n","  df1.loc[i,'new_height_m']=upper"],"execution_count":292,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DOkhMa1NI4Y7","executionInfo":{"status":"ok","timestamp":1620768718735,"user_tz":240,"elapsed":4820,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"65cb7cee-7213-4242-842e-e3329c984b97"},"source":["df1['height_m_bin_ef'] = pd.qcut(df1['new_height_m'],4)\n","df1['height_m_bin_ef'].describe()"],"execution_count":293,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count             1044\n","unique               4\n","top       (0.099, 0.6]\n","freq               343\n","Name: height_m_bin_ef, dtype: object"]},"metadata":{"tags":[]},"execution_count":293}]},{"cell_type":"markdown","metadata":{"id":"k56oKzYXOcZ0"},"source":["**Weight**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ca922czOPACu","executionInfo":{"status":"ok","timestamp":1620768718736,"user_tz":240,"elapsed":4815,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"645ddeb4-d8f0-42ce-8f93-f43df4476718"},"source":["lower, upper = check_outliers(df1['weight_kg'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":294,"outputs":[{"output_type":"stream","text":["lower = -114.0, upper=193.5\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XEzSSIvHPIqk","executionInfo":{"status":"ok","timestamp":1620768718865,"user_tz":240,"elapsed":4942,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df1['weight_kg']< lower"],"execution_count":295,"outputs":[]},{"cell_type":"code","metadata":{"id":"KC7tvZhlPOvX","executionInfo":{"status":"ok","timestamp":1620768718867,"user_tz":240,"elapsed":4942,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df1['weight_kg']>upper"],"execution_count":296,"outputs":[]},{"cell_type":"code","metadata":{"id":"tB8JSDqqPjGu","executionInfo":{"status":"ok","timestamp":1620768718868,"user_tz":240,"elapsed":4941,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df1['new_weight_kg']= df1['weight_kg']\n","for i in df1[mask_upper].index:\n","  df1.loc[i,'new_weight_kg']=upper"],"execution_count":297,"outputs":[]},{"cell_type":"code","metadata":{"id":"IyXEfW2cQBHY","executionInfo":{"status":"ok","timestamp":1620768718868,"user_tz":240,"elapsed":4940,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df1['weight_kg_bin_ef'] = pd.qcut(df1['new_weight_kg'],4)"],"execution_count":298,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"99-e0s--Rl7r"},"source":["**HP**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nyNPHkv5STFh","executionInfo":{"status":"ok","timestamp":1620768718868,"user_tz":240,"elapsed":4934,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"61f2fdbd-d98f-49c8-86ca-cd4d2bd41956"},"source":["lower, upper = check_outliers(df1['hp'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":299,"outputs":[{"output_type":"stream","text":["lower = -14.0, upper=146.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"pKyaCaDaSTFi","executionInfo":{"status":"ok","timestamp":1620768718869,"user_tz":240,"elapsed":4932,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df1['hp']< lower"],"execution_count":300,"outputs":[]},{"cell_type":"code","metadata":{"id":"V8Px-p_9STFi","executionInfo":{"status":"ok","timestamp":1620768718988,"user_tz":240,"elapsed":5049,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df1['hp']>upper"],"execution_count":301,"outputs":[]},{"cell_type":"code","metadata":{"id":"KKD7LtxuS0b5","executionInfo":{"status":"ok","timestamp":1620768718988,"user_tz":240,"elapsed":5047,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df1['new_hp']= df1['hp']\n","for i in df1[mask_upper].index:\n","  df1.loc[i,'new_hp']=upper"],"execution_count":302,"outputs":[]},{"cell_type":"code","metadata":{"id":"Xb7lm9saTp_m","executionInfo":{"status":"ok","timestamp":1620768718989,"user_tz":240,"elapsed":5045,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df1['hp_bin_ef'] = pd.qcut(df1['new_hp'],6)"],"execution_count":303,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"VeHJ-gYSV8xW"},"source":["**Attack:**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B8nK5a4_WD4n","executionInfo":{"status":"ok","timestamp":1620768718989,"user_tz":240,"elapsed":5039,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"70c3ea8f-c5c6-419f-c64e-f7bcd2ddc00d"},"source":["lower, upper = check_outliers(df1['attack'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":304,"outputs":[{"output_type":"stream","text":["lower = -35.0, upper=190.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"z7W0hRO-WD4o","executionInfo":{"status":"ok","timestamp":1620768718990,"user_tz":240,"elapsed":5038,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df1['attack']< lower"],"execution_count":305,"outputs":[]},{"cell_type":"code","metadata":{"id":"oqnDtLtIWD4o","executionInfo":{"status":"ok","timestamp":1620768718990,"user_tz":240,"elapsed":5036,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df1['attack']>upper "],"execution_count":306,"outputs":[]},{"cell_type":"code","metadata":{"id":"O1935LJrWD4q","executionInfo":{"status":"ok","timestamp":1620768718990,"user_tz":240,"elapsed":5034,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df1['attack_bin_ef'] = pd.qcut(df1['attack'],6)"],"execution_count":307,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SHgeKfsrXEUu"},"source":["**Defense**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VObesuZwXcYZ","executionInfo":{"status":"ok","timestamp":1620768719132,"user_tz":240,"elapsed":5170,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"69b8a8f7-72b0-4ec5-8be3-8c28001c122c"},"source":["lower, upper = check_outliers(df1['defense'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":308,"outputs":[{"output_type":"stream","text":["lower = -30.0, upper=170.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"siDr7ZWUXcYZ","executionInfo":{"status":"ok","timestamp":1620768719133,"user_tz":240,"elapsed":5170,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df1['defense']< lower"],"execution_count":309,"outputs":[]},{"cell_type":"code","metadata":{"id":"zHh9x6JDXcYZ","executionInfo":{"status":"ok","timestamp":1620768719134,"user_tz":240,"elapsed":5169,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df1['defense']>upper"],"execution_count":310,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ck4eCGJVXcYa","executionInfo":{"status":"ok","timestamp":1620768719134,"user_tz":240,"elapsed":5167,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df1['new_defense']= df1['defense']\n","for i in df1[mask_upper].index:\n","  df1.loc[i,'new_defense']=upper"],"execution_count":311,"outputs":[]},{"cell_type":"code","metadata":{"id":"rHWa69e2XcYb","executionInfo":{"status":"ok","timestamp":1620768719135,"user_tz":240,"elapsed":5166,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df1['defense_bin_ef'] = pd.qcut(df1['new_defense'],6)"],"execution_count":312,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sVmaJ7OHXlk5"},"source":["**sp_attack**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lFVRuCqWX8yI","executionInfo":{"status":"ok","timestamp":1620768719135,"user_tz":240,"elapsed":5161,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"234ea188-34e1-48f3-f997-d0c05cb1c829"},"source":["lower, upper = check_outliers(df1['sp_attack'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":313,"outputs":[{"output_type":"stream","text":["lower = -40.0, upper=185.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"blpaUhFYX8yI","executionInfo":{"status":"ok","timestamp":1620768719281,"user_tz":240,"elapsed":5305,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df1['sp_attack']< lower"],"execution_count":314,"outputs":[]},{"cell_type":"code","metadata":{"id":"B4AzfXTyX8yJ","executionInfo":{"status":"ok","timestamp":1620768719281,"user_tz":240,"elapsed":5303,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df1['sp_attack']>upper"],"execution_count":315,"outputs":[]},{"cell_type":"code","metadata":{"id":"6uZDSRTkX8yJ","executionInfo":{"status":"ok","timestamp":1620768719282,"user_tz":240,"elapsed":5302,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df1['new_sp_attack']= df1['sp_attack']\n","for i in df1[mask_upper].index:\n","  df1.loc[i,'new_sp_attack']=upper"],"execution_count":316,"outputs":[]},{"cell_type":"code","metadata":{"id":"avExPNbcX8yK","executionInfo":{"status":"ok","timestamp":1620768719282,"user_tz":240,"elapsed":5301,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df1['sp_attack_bin_ef'] = pd.qcut(df1['new_sp_attack'],6)"],"execution_count":317,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1UZoT502YKp9"},"source":["**sp_defense**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"l0VKppcZYObG","executionInfo":{"status":"ok","timestamp":1620768719283,"user_tz":240,"elapsed":5296,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"753fc020-1529-4897-fb5c-feb43164fd38"},"source":["lower, upper = check_outliers(df1['sp_defense'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":318,"outputs":[{"output_type":"stream","text":["lower = -30.0, upper=170.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"JW7sw1ofYObG","executionInfo":{"status":"ok","timestamp":1620768719283,"user_tz":240,"elapsed":5294,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df1['sp_defense']< lower"],"execution_count":319,"outputs":[]},{"cell_type":"code","metadata":{"id":"48Yc8SacYObH","executionInfo":{"status":"ok","timestamp":1620768719283,"user_tz":240,"elapsed":5293,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df1['sp_defense']>upper"],"execution_count":320,"outputs":[]},{"cell_type":"code","metadata":{"id":"K7HtmuKhYObH","executionInfo":{"status":"ok","timestamp":1620768719284,"user_tz":240,"elapsed":5292,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df1['new_sp_defense']= df1['sp_defense']\n","for i in df1[mask_upper].index:\n","  df1.loc[i,'new_sp_defense']=upper"],"execution_count":321,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZzSJ_M8DYObI","executionInfo":{"status":"ok","timestamp":1620768719284,"user_tz":240,"elapsed":5290,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df1['sp_defense_bin_ef'] = pd.qcut(df1['new_sp_defense'],6)"],"execution_count":322,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ZZYOXnq-Yl4e"},"source":["**speed**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Mk03bIBVY3Y2","executionInfo":{"status":"ok","timestamp":1620768719463,"user_tz":240,"elapsed":5463,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"355f1e81-9297-4402-c957-80b43f4764ab"},"source":["lower, upper = check_outliers(df1['speed'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":323,"outputs":[{"output_type":"stream","text":["lower = -45.0, upper=180.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"D0dPTEBMY3Y3","executionInfo":{"status":"ok","timestamp":1620768719463,"user_tz":240,"elapsed":5461,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df1['speed']< lower"],"execution_count":324,"outputs":[]},{"cell_type":"code","metadata":{"id":"R5vCUNApY3Y3","executionInfo":{"status":"ok","timestamp":1620768719464,"user_tz":240,"elapsed":5460,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df1['speed']>upper"],"execution_count":325,"outputs":[]},{"cell_type":"code","metadata":{"id":"y8nwtrJwY3Y4","executionInfo":{"status":"ok","timestamp":1620768719464,"user_tz":240,"elapsed":5458,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df1['new_speed']= df1['speed']\n","for i in df1[mask_upper].index:\n","  df1.loc[i,'new_speed']=upper"],"execution_count":326,"outputs":[]},{"cell_type":"code","metadata":{"id":"TlDaZZRWY3Y4","executionInfo":{"status":"ok","timestamp":1620768719464,"user_tz":240,"elapsed":5456,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df1['speed_bin_ef'] = pd.qcut(df1['new_speed'],6)"],"execution_count":327,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pvRG8vxoY4nS"},"source":["**total_points**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"P80qsKkEZRxO","executionInfo":{"status":"ok","timestamp":1620768719465,"user_tz":240,"elapsed":5452,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"75003441-7ea9-4d88-8f84-8f49968a7870"},"source":["lower, upper = check_outliers(df1['total_points'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":328,"outputs":[{"output_type":"stream","text":["lower = -40.0, upper=885.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"T6x0qDuCZRxP","executionInfo":{"status":"ok","timestamp":1620768719465,"user_tz":240,"elapsed":5450,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df1['total_points']< lower"],"execution_count":329,"outputs":[]},{"cell_type":"code","metadata":{"id":"sp-ASuZkZRxQ","executionInfo":{"status":"ok","timestamp":1620768719466,"user_tz":240,"elapsed":5449,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df1['total_points']>upper"],"execution_count":330,"outputs":[]},{"cell_type":"code","metadata":{"id":"kexyV3SMZRxQ","executionInfo":{"status":"ok","timestamp":1620768719637,"user_tz":240,"elapsed":5618,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df1['total_points_bin_ef'] = pd.qcut(df1['total_points'],6)"],"execution_count":331,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"KDa_AjfcZUNU"},"source":["**catch_rate**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LCpgXGXvZjUL","executionInfo":{"status":"ok","timestamp":1620768719637,"user_tz":240,"elapsed":5611,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"7cd42e90-dcda-4435-f9ec-690a67d4caa0"},"source":["lower, upper = check_outliers(df1['catch_rate'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":332,"outputs":[{"output_type":"stream","text":["lower = -119.0, upper=291.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"mqdotrnpZjUL","executionInfo":{"status":"ok","timestamp":1620768719638,"user_tz":240,"elapsed":5610,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df1['catch_rate']< lower"],"execution_count":333,"outputs":[]},{"cell_type":"code","metadata":{"id":"KFryTkBkZjUL","executionInfo":{"status":"ok","timestamp":1620768719638,"user_tz":240,"elapsed":5609,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df1['catch_rate']>upper"],"execution_count":334,"outputs":[]},{"cell_type":"code","metadata":{"id":"FR7S_6bhZjUN","executionInfo":{"status":"ok","timestamp":1620768719638,"user_tz":240,"elapsed":5607,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df1['catch_rate_bin_ef'] = pd.qcut(df1['catch_rate'],4)"],"execution_count":335,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"G_j6J-r1bWCV"},"source":["**base_experience**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QFGr-_Axbr0O","executionInfo":{"status":"ok","timestamp":1620768719639,"user_tz":240,"elapsed":5603,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"3e6d8eaa-ce0f-447c-e21d-ead919394e40"},"source":["lower, upper = check_outliers(df1['base_experience'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":336,"outputs":[{"output_type":"stream","text":["lower = -202.0, upper=470.5\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"O3NlD2yobr0O","executionInfo":{"status":"ok","timestamp":1620768719639,"user_tz":240,"elapsed":5601,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df1['base_experience']< lower"],"execution_count":337,"outputs":[]},{"cell_type":"code","metadata":{"id":"H1MdDBMebr0O","executionInfo":{"status":"ok","timestamp":1620768719639,"user_tz":240,"elapsed":5599,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df1['base_experience']>upper"],"execution_count":338,"outputs":[]},{"cell_type":"code","metadata":{"id":"KgbV6u0tbr0P","executionInfo":{"status":"ok","timestamp":1620768719640,"user_tz":240,"elapsed":5598,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df1['new_base_experience']= df1['base_experience']\n","for i in df1[mask_upper].index:\n","  df1.loc[i,'new_base_experience']=upper"],"execution_count":339,"outputs":[]},{"cell_type":"code","metadata":{"id":"wFlgXmEVbr0P","executionInfo":{"status":"ok","timestamp":1620768719640,"user_tz":240,"elapsed":5596,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df1['base_experience_bin_ef'] = pd.qcut(df1['new_base_experience'],6)"],"execution_count":340,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"z-ZqYbVebtcv"},"source":["**egg_cycles**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8Fe3oo5scSwL","executionInfo":{"status":"ok","timestamp":1620768719640,"user_tz":240,"elapsed":5591,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"5e9f4e3a-acd4-4f9d-8d37-a36cc17945f4"},"source":["lower, upper = check_outliers(df1['egg_cycles'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":341,"outputs":[{"output_type":"stream","text":["lower = 10.0, upper=35.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jImSoUo8cSwL","executionInfo":{"status":"ok","timestamp":1620768719782,"user_tz":240,"elapsed":5731,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df1['egg_cycles']< lower"],"execution_count":342,"outputs":[]},{"cell_type":"code","metadata":{"id":"B6k6lU30cSwL","executionInfo":{"status":"ok","timestamp":1620768719783,"user_tz":240,"elapsed":5730,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df1['egg_cycles']>upper"],"execution_count":343,"outputs":[]},{"cell_type":"code","metadata":{"id":"R_IGKRT9cSwM","executionInfo":{"status":"ok","timestamp":1620768719783,"user_tz":240,"elapsed":5728,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df1['new_egg_cycles']= df1['egg_cycles']\n","for i in df1[mask_upper].index:\n","  df1.loc[i,'new_egg_cycles']=upper\n","for i in df1[mask_lower].index:\n","  df1.loc[i,'new_egg_cycles']=lower"],"execution_count":344,"outputs":[]},{"cell_type":"code","metadata":{"id":"O_uure_xcmEL","executionInfo":{"status":"ok","timestamp":1620768719783,"user_tz":240,"elapsed":5726,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["df1['egg_cycles_bin_ew'] = pd.cut(df1['new_egg_cycles'],3)"],"execution_count":345,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nS1l-20lf_z1"},"source":["**removing unneeded columns**"]},{"cell_type":"code","metadata":{"id":"j88N2gUIgMiw","executionInfo":{"status":"ok","timestamp":1620768719784,"user_tz":240,"elapsed":5725,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["df1b = df1.drop(labels=['height_m', 'weight_kg', 'total_points', 'hp',\n","        'attack', 'defense', 'sp_attack', 'sp_defense', 'speed', 'catch_rate',\n","       'base_experience', 'egg_cycles', 'new_height_m', 'new_weight_kg', 'new_hp',\n","       'new_defense', 'new_sp_attack', 'new_sp_defense', 'new_speed',\n","       'new_base_experience', 'new_egg_cycles'], axis = 1)"],"execution_count":346,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"1SyV-rDRgqwW","executionInfo":{"status":"ok","timestamp":1620768719894,"user_tz":240,"elapsed":5829,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"3cf49af9-a49f-4e4a-b1ac-f5afbfd48900"},"source":["df1b"],"execution_count":347,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>status</th>\n","      <th>type_1</th>\n","      <th>growth_rate</th>\n","      <th>egg_type_1</th>\n","      <th>height_m_bin_ef</th>\n","      <th>weight_kg_bin_ef</th>\n","      <th>hp_bin_ef</th>\n","      <th>attack_bin_ef</th>\n","      <th>defense_bin_ef</th>\n","      <th>sp_attack_bin_ef</th>\n","      <th>sp_defense_bin_ef</th>\n","      <th>speed_bin_ef</th>\n","      <th>total_points_bin_ef</th>\n","      <th>catch_rate_bin_ef</th>\n","      <th>base_experience_bin_ef</th>\n","      <th>egg_cycles_bin_ew</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Normal</td>\n","      <td>Grass</td>\n","      <td>Medium Slow</td>\n","      <td>Grass</td>\n","      <td>(0.6, 1.0]</td>\n","      <td>(0.099, 9.0]</td>\n","      <td>(0.999, 45.0]</td>\n","      <td>(4.999, 50.0]</td>\n","      <td>(45.0, 60.0]</td>\n","      <td>(55.0, 65.0]</td>\n","      <td>(56.0, 70.0]</td>\n","      <td>(39.833, 50.0]</td>\n","      <td>(305.0, 384.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(62.0, 123.667]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Normal</td>\n","      <td>Grass</td>\n","      <td>Medium Slow</td>\n","      <td>Grass</td>\n","      <td>(0.6, 1.0]</td>\n","      <td>(9.0, 29.5]</td>\n","      <td>(59.667, 68.0]</td>\n","      <td>(50.0, 65.0]</td>\n","      <td>(60.0, 70.0]</td>\n","      <td>(65.0, 85.0]</td>\n","      <td>(70.0, 80.0]</td>\n","      <td>(50.0, 65.0]</td>\n","      <td>(384.0, 458.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(123.667, 161.0]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Normal</td>\n","      <td>Grass</td>\n","      <td>Medium Slow</td>\n","      <td>Grass</td>\n","      <td>(1.6, 3.6]</td>\n","      <td>(70.5, 193.5]</td>\n","      <td>(75.0, 91.167]</td>\n","      <td>(77.0, 92.0]</td>\n","      <td>(70.0, 85.0]</td>\n","      <td>(85.0, 105.0]</td>\n","      <td>(80.0, 100.0]</td>\n","      <td>(65.0, 80.333]</td>\n","      <td>(495.0, 540.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(175.0, 240.0]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Normal</td>\n","      <td>Grass</td>\n","      <td>Medium Slow</td>\n","      <td>Grass</td>\n","      <td>(1.6, 3.6]</td>\n","      <td>(70.5, 193.5]</td>\n","      <td>(75.0, 91.167]</td>\n","      <td>(92.0, 113.0]</td>\n","      <td>(100.0, 170.0]</td>\n","      <td>(105.0, 185.0]</td>\n","      <td>(100.0, 170.0]</td>\n","      <td>(65.0, 80.333]</td>\n","      <td>(540.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(240.0, 470.5]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Normal</td>\n","      <td>Fire</td>\n","      <td>Medium Slow</td>\n","      <td>Dragon</td>\n","      <td>(0.099, 0.6]</td>\n","      <td>(0.099, 9.0]</td>\n","      <td>(0.999, 45.0]</td>\n","      <td>(50.0, 65.0]</td>\n","      <td>(4.999, 45.0]</td>\n","      <td>(55.0, 65.0]</td>\n","      <td>(45.0, 56.0]</td>\n","      <td>(50.0, 65.0]</td>\n","      <td>(305.0, 384.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(35.999, 62.0]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1040</th>\n","      <td>Sub Legendary</td>\n","      <td>Ice</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.6, 3.6]</td>\n","      <td>(70.5, 193.5]</td>\n","      <td>(91.167, 146.0]</td>\n","      <td>(113.0, 190.0]</td>\n","      <td>(100.0, 170.0]</td>\n","      <td>(55.0, 65.0]</td>\n","      <td>(100.0, 170.0]</td>\n","      <td>(4.999, 39.833]</td>\n","      <td>(540.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(240.0, 470.5]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1041</th>\n","      <td>Sub Legendary</td>\n","      <td>Ghost</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.6, 3.6]</td>\n","      <td>(29.5, 70.5]</td>\n","      <td>(91.167, 146.0]</td>\n","      <td>(50.0, 65.0]</td>\n","      <td>(45.0, 60.0]</td>\n","      <td>(105.0, 185.0]</td>\n","      <td>(70.0, 80.0]</td>\n","      <td>(100.0, 180.0]</td>\n","      <td>(540.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(240.0, 470.5]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1042</th>\n","      <td>Legendary</td>\n","      <td>Psychic</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.0, 1.6]</td>\n","      <td>(0.099, 9.0]</td>\n","      <td>(91.167, 146.0]</td>\n","      <td>(77.0, 92.0]</td>\n","      <td>(70.0, 85.0]</td>\n","      <td>(65.0, 85.0]</td>\n","      <td>(70.0, 80.0]</td>\n","      <td>(65.0, 80.333]</td>\n","      <td>(495.0, 540.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(240.0, 470.5]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1043</th>\n","      <td>Legendary</td>\n","      <td>Psychic</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.6, 3.6]</td>\n","      <td>(70.5, 193.5]</td>\n","      <td>(91.167, 146.0]</td>\n","      <td>(113.0, 190.0]</td>\n","      <td>(100.0, 170.0]</td>\n","      <td>(65.0, 85.0]</td>\n","      <td>(100.0, 170.0]</td>\n","      <td>(39.833, 50.0]</td>\n","      <td>(540.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(240.0, 470.5]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1044</th>\n","      <td>Legendary</td>\n","      <td>Psychic</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.6, 3.6]</td>\n","      <td>(29.5, 70.5]</td>\n","      <td>(91.167, 146.0]</td>\n","      <td>(77.0, 92.0]</td>\n","      <td>(70.0, 85.0]</td>\n","      <td>(105.0, 185.0]</td>\n","      <td>(80.0, 100.0]</td>\n","      <td>(100.0, 180.0]</td>\n","      <td>(540.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(240.0, 470.5]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1044 rows × 16 columns</p>\n","</div>"],"text/plain":["             status   type_1  ... base_experience_bin_ef egg_cycles_bin_ew\n","0            Normal    Grass  ...        (62.0, 123.667]  (18.333, 26.667]\n","1            Normal    Grass  ...       (123.667, 161.0]  (18.333, 26.667]\n","2            Normal    Grass  ...         (175.0, 240.0]  (18.333, 26.667]\n","3            Normal    Grass  ...         (240.0, 470.5]  (18.333, 26.667]\n","4            Normal     Fire  ...         (35.999, 62.0]  (18.333, 26.667]\n","...             ...      ...  ...                    ...               ...\n","1040  Sub Legendary      Ice  ...         (240.0, 470.5]    (26.667, 35.0]\n","1041  Sub Legendary    Ghost  ...         (240.0, 470.5]    (26.667, 35.0]\n","1042      Legendary  Psychic  ...         (240.0, 470.5]    (26.667, 35.0]\n","1043      Legendary  Psychic  ...         (240.0, 470.5]    (26.667, 35.0]\n","1044      Legendary  Psychic  ...         (240.0, 470.5]    (26.667, 35.0]\n","\n","[1044 rows x 16 columns]"]},"metadata":{"tags":[]},"execution_count":347}]},{"cell_type":"markdown","metadata":{"id":"rFyBI59cV_IC"},"source":["# **DF2B**"]},{"cell_type":"markdown","metadata":{"id":"hXUmJptGn9tq"},"source":["**Height:**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eyuK_hmSn9tz","executionInfo":{"status":"ok","timestamp":1620768720066,"user_tz":240,"elapsed":5990,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"e3b04a56-88c8-4d72-82d8-f36226ca9429"},"source":["lower, upper = check_outliers(df2['height_m'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":348,"outputs":[{"output_type":"stream","text":["lower = -1.2999999999999998, upper=3.1999999999999997\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"uK0-rsrTn9tz","executionInfo":{"status":"ok","timestamp":1620768720067,"user_tz":240,"elapsed":5989,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df2['height_m']< lower"],"execution_count":349,"outputs":[]},{"cell_type":"code","metadata":{"id":"j0nahGi9n9t0","executionInfo":{"status":"ok","timestamp":1620768720067,"user_tz":240,"elapsed":5987,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df2['height_m']>upper"],"execution_count":350,"outputs":[]},{"cell_type":"code","metadata":{"id":"HefYG0AXn9t0","executionInfo":{"status":"ok","timestamp":1620768720067,"user_tz":240,"elapsed":5986,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df2['new_height_m']= df2['height_m']\n","for i in df2[mask_upper].index:\n","  df2.loc[i,'new_height_m']=upper"],"execution_count":351,"outputs":[]},{"cell_type":"code","metadata":{"id":"jJMSFCDan9t0","executionInfo":{"status":"ok","timestamp":1620768720068,"user_tz":240,"elapsed":5985,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["df2['height_m_bin_ef'] = pd.qcut(df2['new_height_m'],4)"],"execution_count":352,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"29O0uEwcn9t1"},"source":["**Weight**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3LnL8phTn9t1","executionInfo":{"status":"ok","timestamp":1620768720068,"user_tz":240,"elapsed":5969,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"bbed0dc2-81d3-46e4-9568-8fcd52db93a2"},"source":["lower, upper = check_outliers(df2['weight_kg'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":353,"outputs":[{"output_type":"stream","text":["lower = -82.325, upper=143.425\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"hwijrPA9n9t2","executionInfo":{"status":"ok","timestamp":1620768720068,"user_tz":240,"elapsed":5968,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df2['weight_kg']< lower"],"execution_count":354,"outputs":[]},{"cell_type":"code","metadata":{"id":"iAWsBEmRn9t2","executionInfo":{"status":"ok","timestamp":1620768720295,"user_tz":240,"elapsed":6193,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df2['weight_kg']>upper"],"execution_count":355,"outputs":[]},{"cell_type":"code","metadata":{"id":"bJ4_fhfin9t2","executionInfo":{"status":"ok","timestamp":1620768720295,"user_tz":240,"elapsed":6191,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df2['new_weight_kg']= df2['weight_kg']\n","for i in df2[mask_upper].index:\n","  df2.loc[i,'new_weight_kg']=upper"],"execution_count":356,"outputs":[]},{"cell_type":"code","metadata":{"id":"srFp8vLZn9t2","executionInfo":{"status":"ok","timestamp":1620768720296,"user_tz":240,"elapsed":6190,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df2['weight_kg_bin_ef'] = pd.qcut(df2['new_weight_kg'],4)"],"execution_count":357,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Nv7CFSJxn9t3"},"source":["**HP**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"844JkIvJn9t3","executionInfo":{"status":"ok","timestamp":1620768720296,"user_tz":240,"elapsed":6174,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"c257d49b-d9bb-40f2-af14-c01188fcba7d"},"source":["lower, upper = check_outliers(df2['hp'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":358,"outputs":[{"output_type":"stream","text":["lower = -8.0, upper=137.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"luQaZ_mun9t3","executionInfo":{"status":"ok","timestamp":1620768720296,"user_tz":240,"elapsed":6172,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df2['hp']< lower"],"execution_count":359,"outputs":[]},{"cell_type":"code","metadata":{"id":"7viJoURCn9t4","executionInfo":{"status":"ok","timestamp":1620768720297,"user_tz":240,"elapsed":6171,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df2['hp']>upper "],"execution_count":360,"outputs":[]},{"cell_type":"code","metadata":{"id":"ioOkfo5Wn9t4","executionInfo":{"status":"ok","timestamp":1620768720297,"user_tz":240,"elapsed":6169,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df2['new_hp']= df2['hp']\n","for i in df2[mask_upper].index:\n","  df2.loc[i,'new_hp']=upper"],"execution_count":361,"outputs":[]},{"cell_type":"code","metadata":{"id":"8f6LcXywn9t4","executionInfo":{"status":"ok","timestamp":1620768720298,"user_tz":240,"elapsed":6168,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df2['hp_bin_ef'] = pd.qcut(df2['new_hp'],6)"],"execution_count":362,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"oxoUCOZNn9t4"},"source":["**Attack:**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"P9mOK657n9t5","executionInfo":{"status":"ok","timestamp":1620768720298,"user_tz":240,"elapsed":6153,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"12535864-4deb-44db-81e7-25478dadade5"},"source":["lower, upper = check_outliers(df2['attack'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":363,"outputs":[{"output_type":"stream","text":["lower = -32.0, upper=180.5\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"K9IqoTiJn9t5","executionInfo":{"status":"ok","timestamp":1620768720483,"user_tz":240,"elapsed":6337,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df2['attack']< lower"],"execution_count":364,"outputs":[]},{"cell_type":"code","metadata":{"id":"9-hT80grn9t6","executionInfo":{"status":"ok","timestamp":1620768720484,"user_tz":240,"elapsed":6336,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df2['attack']>upper"],"execution_count":365,"outputs":[]},{"cell_type":"code","metadata":{"id":"Iwew6wkJn9t6","executionInfo":{"status":"ok","timestamp":1620768720484,"user_tz":240,"elapsed":6334,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df2['attack_bin_ef'] = pd.qcut(df2['attack'],6)"],"execution_count":366,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Sbs2wn0Wn9t6"},"source":["**Defense**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zPXVLC-9n9t7","executionInfo":{"status":"ok","timestamp":1620768720484,"user_tz":240,"elapsed":6319,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"8206c1a5-4b6d-45e3-e0ec-900f52e5ade4"},"source":["lower, upper = check_outliers(df2['defense'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":367,"outputs":[{"output_type":"stream","text":["lower = -11.0, upper=141.5\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"9SBfGr4wn9t7","executionInfo":{"status":"ok","timestamp":1620768720485,"user_tz":240,"elapsed":6319,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df2['defense']< lower"],"execution_count":368,"outputs":[]},{"cell_type":"code","metadata":{"id":"ac1Q7Zmun9t7","executionInfo":{"status":"ok","timestamp":1620768720485,"user_tz":240,"elapsed":6317,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df2['defense']>upper"],"execution_count":369,"outputs":[]},{"cell_type":"code","metadata":{"id":"ja-aSouln9t7","executionInfo":{"status":"ok","timestamp":1620768720485,"user_tz":240,"elapsed":6315,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df2['new_defense']= df2['defense']\n","for i in df2[mask_upper].index:\n","  df2.loc[i,'new_defense']=upper"],"execution_count":370,"outputs":[]},{"cell_type":"code","metadata":{"id":"CoAuMG46n9t8","executionInfo":{"status":"ok","timestamp":1620768720486,"user_tz":240,"elapsed":6314,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df2['defense_bin_ef'] = pd.qcut(df2['new_defense'],6)"],"execution_count":371,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6mHz4W1Xn9t8"},"source":["**sp_attack**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JjjpM7_tn9t8","executionInfo":{"status":"ok","timestamp":1620768720487,"user_tz":240,"elapsed":6301,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"3b1f6d0c-3d49-442e-c04c-948cfccc3efb"},"source":["lower, upper = check_outliers(df2['sp_attack'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":372,"outputs":[{"output_type":"stream","text":["lower = -35.0, upper=165.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Vb35LhRNn9t8","executionInfo":{"status":"ok","timestamp":1620768720487,"user_tz":240,"elapsed":6299,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df2['sp_attack']< lower"],"execution_count":373,"outputs":[]},{"cell_type":"code","metadata":{"id":"XVNUczqqn9t9","executionInfo":{"status":"ok","timestamp":1620768720488,"user_tz":240,"elapsed":6299,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df2['sp_attack']>upper"],"execution_count":374,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZkaCKx10n9t9","executionInfo":{"status":"ok","timestamp":1620768720488,"user_tz":240,"elapsed":6297,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df2['new_sp_attack']= df2['sp_attack']\n","for i in df2[mask_upper].index:\n","  df2.loc[i,'new_sp_attack']=upper"],"execution_count":375,"outputs":[]},{"cell_type":"code","metadata":{"id":"P3Vmh0vnn9t9","executionInfo":{"status":"ok","timestamp":1620768720488,"user_tz":240,"elapsed":6295,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df2['sp_attack_bin_ef'] = pd.qcut(df2['new_sp_attack'],6)"],"execution_count":376,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ykAb0mdKn9t9"},"source":["**sp_defense**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Tw2rmehCn9t-","executionInfo":{"status":"ok","timestamp":1620768720702,"user_tz":240,"elapsed":6495,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"7cae019e-1698-4f3e-d8c1-a8eb25af0be0"},"source":["lower, upper = check_outliers(df2['sp_defense'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":377,"outputs":[{"output_type":"stream","text":["lower = -20.75, upper=155.5\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DL4l3g5qn9t-","executionInfo":{"status":"ok","timestamp":1620768720702,"user_tz":240,"elapsed":6493,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df2['sp_defense']< lower"],"execution_count":378,"outputs":[]},{"cell_type":"code","metadata":{"id":"JW9rKkcHn9t-","executionInfo":{"status":"ok","timestamp":1620768720702,"user_tz":240,"elapsed":6491,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df2['sp_defense']>upper"],"execution_count":379,"outputs":[]},{"cell_type":"code","metadata":{"id":"OODoW-5hn9t-","executionInfo":{"status":"ok","timestamp":1620768720703,"user_tz":240,"elapsed":6490,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df2['new_sp_defense']= df2['sp_defense']\n","for i in df2[mask_upper].index:\n","  df2.loc[i,'new_sp_defense']=upper"],"execution_count":380,"outputs":[]},{"cell_type":"code","metadata":{"id":"j1mOXoKyn9t_","executionInfo":{"status":"ok","timestamp":1620768720703,"user_tz":240,"elapsed":6480,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df2['sp_defense_bin_ef'] = pd.qcut(df2['new_sp_defense'],6)"],"execution_count":381,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"t-ADC3xvn9t_"},"source":["**speed**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ejZTf1YHn9uA","executionInfo":{"status":"ok","timestamp":1620768720704,"user_tz":240,"elapsed":6467,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"30ffd081-8bfb-4de7-b9b7-dce359fdee63"},"source":["lower, upper = check_outliers(df2['speed'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":382,"outputs":[{"output_type":"stream","text":["lower = -36.75, upper=167.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"cOt5WW0zn9uA","executionInfo":{"status":"ok","timestamp":1620768720704,"user_tz":240,"elapsed":6465,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df2['speed']< lower"],"execution_count":383,"outputs":[]},{"cell_type":"code","metadata":{"id":"VdqkQb2wn9uA","executionInfo":{"status":"ok","timestamp":1620768720704,"user_tz":240,"elapsed":6463,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df2['speed']>upper"],"execution_count":384,"outputs":[]},{"cell_type":"code","metadata":{"id":"FiH5kmFdn9uA","executionInfo":{"status":"ok","timestamp":1620768720705,"user_tz":240,"elapsed":6462,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df2['new_speed']= df2['speed']\n","for i in df2[mask_upper].index:\n","  df2.loc[i,'new_speed']=upper"],"execution_count":385,"outputs":[]},{"cell_type":"code","metadata":{"id":"pTtL8-O7n9uB","executionInfo":{"status":"ok","timestamp":1620768720705,"user_tz":240,"elapsed":6461,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df2['speed_bin_ef'] = pd.qcut(df2['new_speed'],6)"],"execution_count":386,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bkf8WV7pn9uB"},"source":["**total_points**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G6R66hfQn9uC","executionInfo":{"status":"ok","timestamp":1620768720855,"user_tz":240,"elapsed":6596,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"184540a3-fca0-490e-f127-3913d4946006"},"source":["lower, upper = check_outliers(df2['total_points'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":387,"outputs":[{"output_type":"stream","text":["lower = -60.0, upper=865.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jzE9hgQ0n9uC","executionInfo":{"status":"ok","timestamp":1620768720855,"user_tz":240,"elapsed":6594,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df2['total_points']< lower"],"execution_count":388,"outputs":[]},{"cell_type":"code","metadata":{"id":"fQ8M8EXTn9uC","executionInfo":{"status":"ok","timestamp":1620768720856,"user_tz":240,"elapsed":6594,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df2['total_points']>upper"],"execution_count":389,"outputs":[]},{"cell_type":"code","metadata":{"id":"6hytJUwln9uD","executionInfo":{"status":"ok","timestamp":1620768720856,"user_tz":240,"elapsed":6592,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df2['total_points_bin_ef'] = pd.qcut(df2['total_points'],6)"],"execution_count":390,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OZBCPy55n9uD"},"source":["**catch_rate**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MXSToVOwn9uG","executionInfo":{"status":"ok","timestamp":1620768720856,"user_tz":240,"elapsed":6577,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"fc2e7da3-dc5d-4fef-b303-d98bf15c7c44"},"source":["lower, upper = check_outliers(df2['catch_rate'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":391,"outputs":[{"output_type":"stream","text":["lower = -245.0, upper=480.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"UIU5KGfDn9uG","executionInfo":{"status":"ok","timestamp":1620768720857,"user_tz":240,"elapsed":6577,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df2['catch_rate']< lower"],"execution_count":392,"outputs":[]},{"cell_type":"code","metadata":{"id":"jIf2EpnDn9uG","executionInfo":{"status":"ok","timestamp":1620768720857,"user_tz":240,"elapsed":6575,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df2['catch_rate']>upper"],"execution_count":393,"outputs":[]},{"cell_type":"code","metadata":{"id":"6R1Pf8oun9uG","executionInfo":{"status":"ok","timestamp":1620768720858,"user_tz":240,"elapsed":6574,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df2['catch_rate_bin_ef'] = pd.qcut(df2['catch_rate'],4)"],"execution_count":394,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9PWYCjh-n9uH"},"source":["**base_experience**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gzCvJYUYn9uH","executionInfo":{"status":"ok","timestamp":1620768720858,"user_tz":240,"elapsed":6560,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"27957773-a815-4b43-b706-b27d451d896c"},"source":["lower, upper = check_outliers(df2['base_experience'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":395,"outputs":[{"output_type":"stream","text":["lower = -161.0, upper=399.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"nB6imGfCn9uI","executionInfo":{"status":"ok","timestamp":1620768720859,"user_tz":240,"elapsed":6559,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df2['base_experience']< lower"],"execution_count":396,"outputs":[]},{"cell_type":"code","metadata":{"id":"sbEM2TPqn9uI","executionInfo":{"status":"ok","timestamp":1620768720859,"user_tz":240,"elapsed":6556,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df2['base_experience']>upper"],"execution_count":397,"outputs":[]},{"cell_type":"code","metadata":{"id":"dZSEfL8zn9uI","executionInfo":{"status":"ok","timestamp":1620768720859,"user_tz":240,"elapsed":6555,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df2['new_base_experience']= df2['base_experience']\n","for i in df2[mask_upper].index:\n","  df2.loc[i,'new_base_experience']=upper"],"execution_count":398,"outputs":[]},{"cell_type":"code","metadata":{"id":"k3UceZ4zn9uI","executionInfo":{"status":"ok","timestamp":1620768721084,"user_tz":240,"elapsed":6778,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df2['base_experience_bin_ef'] = pd.qcut(df2['new_base_experience'],6)"],"execution_count":399,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Xt733MMtn9uJ"},"source":["**egg_cycles**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G5lpqAOYn9uJ","executionInfo":{"status":"ok","timestamp":1620768721084,"user_tz":240,"elapsed":6763,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"ecc8444c-7890-4c7d-a4e2-afa894f2474a"},"source":["lower, upper = check_outliers(df2['egg_cycles'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":400,"outputs":[{"output_type":"stream","text":["lower = 10.0, upper=35.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"PK1wspF7n9uJ","executionInfo":{"status":"ok","timestamp":1620768721084,"user_tz":240,"elapsed":6761,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df2['egg_cycles']< lower"],"execution_count":401,"outputs":[]},{"cell_type":"code","metadata":{"id":"fJI7OuKYn9uK","executionInfo":{"status":"ok","timestamp":1620768721085,"user_tz":240,"elapsed":6761,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df2['egg_cycles']>upper"],"execution_count":402,"outputs":[]},{"cell_type":"code","metadata":{"id":"5FaDULhNn9uK","executionInfo":{"status":"ok","timestamp":1620768721085,"user_tz":240,"elapsed":6759,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df2['new_egg_cycles']= df2['egg_cycles']\n","for i in df2[mask_upper].index:\n","  df2.loc[i,'new_egg_cycles']=upper\n","for i in df2[mask_lower].index:\n","  df2.loc[i,'new_egg_cycles']=lower"],"execution_count":403,"outputs":[]},{"cell_type":"code","metadata":{"id":"k_w3d2imn9uK","executionInfo":{"status":"ok","timestamp":1620768721085,"user_tz":240,"elapsed":6757,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["df2['egg_cycles_bin_ew'] = pd.cut(df2['new_egg_cycles'],3)"],"execution_count":404,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Ba9AK7VUn9uL"},"source":["**removing unneeded columns**"]},{"cell_type":"code","metadata":{"id":"p07ijTP3n9uL","executionInfo":{"status":"ok","timestamp":1620768721265,"user_tz":240,"elapsed":6935,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["df2 = df2.drop(labels=['new_height_m', 'new_weight_kg', 'new_hp',\n","       'new_defense', 'new_sp_attack', 'new_sp_defense', 'new_speed',\n","       'new_base_experience', 'new_egg_cycles'], axis = 1)\n","df2b = df2.drop(labels=['height_m', 'weight_kg', 'total_points', 'hp',\n","        'attack', 'defense', 'sp_attack', 'sp_defense', 'speed', 'catch_rate',\n","       'base_experience', 'egg_cycles'], axis = 1)"],"execution_count":405,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":541},"id":"SBVpGxW7n9uL","executionInfo":{"status":"ok","timestamp":1620768721265,"user_tz":240,"elapsed":6921,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"3c15a30d-dd66-4e70-f05c-79f5af7563ef"},"source":["df2b"],"execution_count":406,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>status</th>\n","      <th>type_1</th>\n","      <th>growth_rate</th>\n","      <th>egg_type_1</th>\n","      <th>height_m_bin_ef</th>\n","      <th>weight_kg_bin_ef</th>\n","      <th>hp_bin_ef</th>\n","      <th>attack_bin_ef</th>\n","      <th>defense_bin_ef</th>\n","      <th>sp_attack_bin_ef</th>\n","      <th>sp_defense_bin_ef</th>\n","      <th>speed_bin_ef</th>\n","      <th>total_points_bin_ef</th>\n","      <th>catch_rate_bin_ef</th>\n","      <th>base_experience_bin_ef</th>\n","      <th>egg_cycles_bin_ew</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>4</th>\n","      <td>Normal</td>\n","      <td>Fire</td>\n","      <td>Medium Slow</td>\n","      <td>Dragon</td>\n","      <td>(0.5, 0.8]</td>\n","      <td>(7.975, 23.4]</td>\n","      <td>(9.999, 45.0]</td>\n","      <td>(46.833, 60.0]</td>\n","      <td>(42.833, 55.0]</td>\n","      <td>(50.0, 62.0]</td>\n","      <td>(41.833, 53.0]</td>\n","      <td>(63.0, 78.0]</td>\n","      <td>(295.0, 330.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(60.0, 68.667]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Normal</td>\n","      <td>Fire</td>\n","      <td>Medium Slow</td>\n","      <td>Dragon</td>\n","      <td>(0.8, 1.4]</td>\n","      <td>(7.975, 23.4]</td>\n","      <td>(55.0, 65.0]</td>\n","      <td>(60.0, 71.5]</td>\n","      <td>(55.0, 64.0]</td>\n","      <td>(75.0, 95.0]</td>\n","      <td>(63.0, 75.0]</td>\n","      <td>(78.0, 95.0]</td>\n","      <td>(330.0, 420.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(68.667, 145.0]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Normal</td>\n","      <td>Water</td>\n","      <td>Medium Slow</td>\n","      <td>Monster</td>\n","      <td>(0.099, 0.5]</td>\n","      <td>(7.975, 23.4]</td>\n","      <td>(9.999, 45.0]</td>\n","      <td>(46.833, 60.0]</td>\n","      <td>(64.0, 75.0]</td>\n","      <td>(40.0, 50.0]</td>\n","      <td>(63.0, 75.0]</td>\n","      <td>(35.0, 50.0]</td>\n","      <td>(295.0, 330.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(60.0, 68.667]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Normal</td>\n","      <td>Water</td>\n","      <td>Medium Slow</td>\n","      <td>Monster</td>\n","      <td>(0.8, 1.4]</td>\n","      <td>(7.975, 23.4]</td>\n","      <td>(55.0, 65.0]</td>\n","      <td>(60.0, 71.5]</td>\n","      <td>(75.0, 95.0]</td>\n","      <td>(62.0, 75.0]</td>\n","      <td>(75.0, 90.0]</td>\n","      <td>(50.0, 63.0]</td>\n","      <td>(330.0, 420.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(68.667, 145.0]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Normal</td>\n","      <td>Water</td>\n","      <td>Medium Slow</td>\n","      <td>Monster</td>\n","      <td>(1.4, 3.2]</td>\n","      <td>(53.125, 143.425]</td>\n","      <td>(73.333, 88.333]</td>\n","      <td>(71.5, 85.0]</td>\n","      <td>(95.0, 141.5]</td>\n","      <td>(75.0, 95.0]</td>\n","      <td>(90.0, 155.5]</td>\n","      <td>(63.0, 78.0]</td>\n","      <td>(525.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(216.167, 399.0]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1034</th>\n","      <td>Sub Legendary</td>\n","      <td>Fighting</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(0.5, 0.8]</td>\n","      <td>(7.975, 23.4]</td>\n","      <td>(55.0, 65.0]</td>\n","      <td>(85.0, 105.0]</td>\n","      <td>(55.0, 64.0]</td>\n","      <td>(50.0, 62.0]</td>\n","      <td>(41.833, 53.0]</td>\n","      <td>(63.0, 78.0]</td>\n","      <td>(330.0, 420.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(68.667, 145.0]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1038</th>\n","      <td>Sub Legendary</td>\n","      <td>Electric</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(0.8, 1.4]</td>\n","      <td>(53.125, 143.425]</td>\n","      <td>(73.333, 88.333]</td>\n","      <td>(85.0, 105.0]</td>\n","      <td>(42.833, 55.0]</td>\n","      <td>(95.0, 165.0]</td>\n","      <td>(41.833, 53.0]</td>\n","      <td>(95.0, 167.0]</td>\n","      <td>(525.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(216.167, 399.0]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1039</th>\n","      <td>Sub Legendary</td>\n","      <td>Dragon</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.4, 3.2]</td>\n","      <td>(53.125, 143.425]</td>\n","      <td>(88.333, 137.0]</td>\n","      <td>(85.0, 105.0]</td>\n","      <td>(42.833, 55.0]</td>\n","      <td>(95.0, 165.0]</td>\n","      <td>(41.833, 53.0]</td>\n","      <td>(78.0, 95.0]</td>\n","      <td>(525.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(216.167, 399.0]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1040</th>\n","      <td>Sub Legendary</td>\n","      <td>Ice</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.4, 3.2]</td>\n","      <td>(53.125, 143.425]</td>\n","      <td>(88.333, 137.0]</td>\n","      <td>(105.0, 180.0]</td>\n","      <td>(95.0, 141.5]</td>\n","      <td>(62.0, 75.0]</td>\n","      <td>(90.0, 155.5]</td>\n","      <td>(4.999, 35.0]</td>\n","      <td>(525.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(216.167, 399.0]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1041</th>\n","      <td>Sub Legendary</td>\n","      <td>Ghost</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.4, 3.2]</td>\n","      <td>(23.4, 53.125]</td>\n","      <td>(88.333, 137.0]</td>\n","      <td>(60.0, 71.5]</td>\n","      <td>(55.0, 64.0]</td>\n","      <td>(95.0, 165.0]</td>\n","      <td>(75.0, 90.0]</td>\n","      <td>(95.0, 167.0]</td>\n","      <td>(525.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(216.167, 399.0]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>492 rows × 16 columns</p>\n","</div>"],"text/plain":["             status    type_1  ... base_experience_bin_ef egg_cycles_bin_ew\n","4            Normal      Fire  ...         (60.0, 68.667]  (18.333, 26.667]\n","5            Normal      Fire  ...        (68.667, 145.0]  (18.333, 26.667]\n","9            Normal     Water  ...         (60.0, 68.667]  (18.333, 26.667]\n","10           Normal     Water  ...        (68.667, 145.0]  (18.333, 26.667]\n","11           Normal     Water  ...       (216.167, 399.0]  (18.333, 26.667]\n","...             ...       ...  ...                    ...               ...\n","1034  Sub Legendary  Fighting  ...        (68.667, 145.0]    (26.667, 35.0]\n","1038  Sub Legendary  Electric  ...       (216.167, 399.0]    (26.667, 35.0]\n","1039  Sub Legendary    Dragon  ...       (216.167, 399.0]    (26.667, 35.0]\n","1040  Sub Legendary       Ice  ...       (216.167, 399.0]    (26.667, 35.0]\n","1041  Sub Legendary     Ghost  ...       (216.167, 399.0]    (26.667, 35.0]\n","\n","[492 rows x 16 columns]"]},"metadata":{"tags":[]},"execution_count":406}]},{"cell_type":"markdown","metadata":{"id":"cvmlc2S-WaRe"},"source":["# **DF3B**"]},{"cell_type":"code","metadata":{"id":"sra_KZT-WcjT","executionInfo":{"status":"ok","timestamp":1620768721266,"user_tz":240,"elapsed":6920,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":[""],"execution_count":406,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SOxmoFiWogoJ"},"source":["**Height:**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FQrk3PF7ogoK","executionInfo":{"status":"ok","timestamp":1620768721266,"user_tz":240,"elapsed":6910,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"e236f091-26a0-4df2-93a4-dc3fb9aa60e4"},"source":["lower, upper = check_outliers(df3['height_m'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":407,"outputs":[{"output_type":"stream","text":["lower = -1.4, upper=3.6\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"sGz_cDL0ogoK","executionInfo":{"status":"ok","timestamp":1620768721266,"user_tz":240,"elapsed":6908,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df3['height_m']< lower"],"execution_count":408,"outputs":[]},{"cell_type":"code","metadata":{"id":"Agb2vKaOogoL","executionInfo":{"status":"ok","timestamp":1620768721267,"user_tz":240,"elapsed":6907,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df3['height_m']>upper"],"execution_count":409,"outputs":[]},{"cell_type":"code","metadata":{"id":"ps27iNRFogoL","executionInfo":{"status":"ok","timestamp":1620768721267,"user_tz":240,"elapsed":6905,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df3['new_height_m']= df3['height_m']\n","for i in df3[mask_upper].index:\n","  df3.loc[i,'new_height_m']=upper"],"execution_count":410,"outputs":[]},{"cell_type":"code","metadata":{"id":"_xDo71G0ogoL","executionInfo":{"status":"ok","timestamp":1620768721405,"user_tz":240,"elapsed":7041,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["df3['height_m_bin_ef'] = pd.qcut(df3['new_height_m'],4)"],"execution_count":411,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"lIzaltOWogoM"},"source":["**Weight**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PgsQY-o_ogoM","executionInfo":{"status":"ok","timestamp":1620768721406,"user_tz":240,"elapsed":7037,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"fcea94ce-eb08-424b-b018-786ca3944204"},"source":["lower, upper = check_outliers(df3['weight_kg'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":412,"outputs":[{"output_type":"stream","text":["lower = -127.6, upper=215.14999999999998\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"xvCaE7MWogoM","executionInfo":{"status":"ok","timestamp":1620768721406,"user_tz":240,"elapsed":7035,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df3['weight_kg']< lower"],"execution_count":413,"outputs":[]},{"cell_type":"code","metadata":{"id":"yoRSkmCoogoM","executionInfo":{"status":"ok","timestamp":1620768721406,"user_tz":240,"elapsed":7033,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df3['weight_kg']>upper"],"execution_count":414,"outputs":[]},{"cell_type":"code","metadata":{"id":"MRTLSfaRogoN","executionInfo":{"status":"ok","timestamp":1620768721407,"user_tz":240,"elapsed":7033,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df3['new_weight_kg']= df3['weight_kg']\n","for i in df3[mask_upper].index:\n","  df3.loc[i,'new_weight_kg']=upper"],"execution_count":415,"outputs":[]},{"cell_type":"code","metadata":{"id":"tr99XeFrogoN","executionInfo":{"status":"ok","timestamp":1620768721407,"user_tz":240,"elapsed":7031,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df3['weight_kg_bin_ef'] = pd.qcut(df3['new_weight_kg'],4)"],"execution_count":416,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-11jSvXPogoN"},"source":["**HP**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-azo90HNogoO","executionInfo":{"status":"ok","timestamp":1620768721407,"user_tz":240,"elapsed":7026,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"8fc67746-f288-4b0c-9503-c6dd3ab7e867"},"source":["lower, upper = check_outliers(df3['hp'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":417,"outputs":[{"output_type":"stream","text":["lower = -8.75, upper=147.5\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"l6N73c2iogoO","executionInfo":{"status":"ok","timestamp":1620768721409,"user_tz":240,"elapsed":7026,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df3['hp']< lower"],"execution_count":418,"outputs":[]},{"cell_type":"code","metadata":{"id":"DJ50_AaiogoO","executionInfo":{"status":"ok","timestamp":1620768721409,"user_tz":240,"elapsed":7024,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df3['hp']>upper"],"execution_count":419,"outputs":[]},{"cell_type":"code","metadata":{"id":"lFkjYHJ-ogoO","executionInfo":{"status":"ok","timestamp":1620768721661,"user_tz":240,"elapsed":7275,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df3['new_hp']= df3['hp']\n","for i in df3[mask_upper].index:\n","  df3.loc[i,'new_hp']=upper"],"execution_count":420,"outputs":[]},{"cell_type":"code","metadata":{"id":"JSN8B9piogoO","executionInfo":{"status":"ok","timestamp":1620768721662,"user_tz":240,"elapsed":7274,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df3['hp_bin_ef'] = pd.qcut(df3['new_hp'],6)"],"execution_count":421,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"we2k4BAbogoP"},"source":["**Attack:**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z2cK-53iogoR","executionInfo":{"status":"ok","timestamp":1620768721662,"user_tz":240,"elapsed":7269,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"1701eea5-6ca6-4463-ec69-a95271b9bc7d"},"source":["lower, upper = check_outliers(df3['attack'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":422,"outputs":[{"output_type":"stream","text":["lower = -23.0, upper=182.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"w_k5mfECogoR","executionInfo":{"status":"ok","timestamp":1620768721662,"user_tz":240,"elapsed":7267,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df3['attack']< lower"],"execution_count":423,"outputs":[]},{"cell_type":"code","metadata":{"id":"par7p_MuogoR","executionInfo":{"status":"ok","timestamp":1620768721663,"user_tz":240,"elapsed":7266,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df3['attack']>upper"],"execution_count":424,"outputs":[]},{"cell_type":"code","metadata":{"id":"rXYqTGnCogoR","executionInfo":{"status":"ok","timestamp":1620768721663,"user_tz":240,"elapsed":7264,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df3['attack_bin_ef'] = pd.qcut(df3['attack'],6)"],"execution_count":425,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"XgilGnExogoS"},"source":["**Defense**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"evveFlr-ogoS","executionInfo":{"status":"ok","timestamp":1620768721663,"user_tz":240,"elapsed":7258,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"e0dc8e28-be1e-4169-df81-a8872f2320d8"},"source":["lower, upper = check_outliers(df3['defense'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":426,"outputs":[{"output_type":"stream","text":["lower = -28.0, upper=177.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"E49FwFX6ogoS","executionInfo":{"status":"ok","timestamp":1620768721664,"user_tz":240,"elapsed":7257,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df3['defense']< lower"],"execution_count":427,"outputs":[]},{"cell_type":"code","metadata":{"id":"kRD9TirKogoS","executionInfo":{"status":"ok","timestamp":1620768721664,"user_tz":240,"elapsed":7255,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df3['defense']>upper"],"execution_count":428,"outputs":[]},{"cell_type":"code","metadata":{"id":"vys50ZjtogoT","executionInfo":{"status":"ok","timestamp":1620768721664,"user_tz":240,"elapsed":7254,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df3['new_defense']= df3['defense']\n","for i in df3[mask_upper].index:\n","  df3.loc[i,'new_defense']=upper"],"execution_count":429,"outputs":[]},{"cell_type":"code","metadata":{"id":"tKo9IXW3ogoT","executionInfo":{"status":"ok","timestamp":1620768721664,"user_tz":240,"elapsed":7252,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df3['defense_bin_ef'] = pd.qcut(df3['new_defense'],6)"],"execution_count":430,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"JlMthzMAogoT"},"source":["**sp_attack**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wM4aOf_-ogoU","executionInfo":{"status":"ok","timestamp":1620768721804,"user_tz":240,"elapsed":7387,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"e9433904-f4a2-4a53-b33b-b7e1d0c6557e"},"source":["lower, upper = check_outliers(df3['sp_attack'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":431,"outputs":[{"output_type":"stream","text":["lower = -40.0, upper=185.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"p0Xj6qyKogoU","executionInfo":{"status":"ok","timestamp":1620768721804,"user_tz":240,"elapsed":7385,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df3['sp_attack']< lower"],"execution_count":432,"outputs":[]},{"cell_type":"code","metadata":{"id":"kpIAwGE7ogoU","executionInfo":{"status":"ok","timestamp":1620768721805,"user_tz":240,"elapsed":7384,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df3['sp_attack']>upper"],"execution_count":433,"outputs":[]},{"cell_type":"code","metadata":{"id":"kQZ2C4WZogoU","executionInfo":{"status":"ok","timestamp":1620768721805,"user_tz":240,"elapsed":7382,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df3['new_sp_attack']= df3['sp_attack']\n","for i in df3[mask_upper].index:\n","  df3.loc[i,'new_sp_attack']=upper"],"execution_count":434,"outputs":[]},{"cell_type":"code","metadata":{"id":"_2e88ztcogoU","executionInfo":{"status":"ok","timestamp":1620768721806,"user_tz":240,"elapsed":7381,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df3['sp_attack_bin_ef'] = pd.qcut(df3['new_sp_attack'],6)"],"execution_count":435,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WM3zRgDKogoV"},"source":["**sp_defense**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Mwre1ahWogoV","executionInfo":{"status":"ok","timestamp":1620768721806,"user_tz":240,"elapsed":7376,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"1c102a0f-a8f3-40f2-ef91-5c457905c81d"},"source":["lower, upper = check_outliers(df3['sp_defense'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":436,"outputs":[{"output_type":"stream","text":["lower = -21.0, upper=164.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"42qdqB6oogoV","executionInfo":{"status":"ok","timestamp":1620768721806,"user_tz":240,"elapsed":7374,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df3['sp_defense']< lower"],"execution_count":437,"outputs":[]},{"cell_type":"code","metadata":{"id":"VhZbEYEtogoV","executionInfo":{"status":"ok","timestamp":1620768721807,"user_tz":240,"elapsed":7373,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df3['sp_defense']>upper"],"execution_count":438,"outputs":[]},{"cell_type":"code","metadata":{"id":"JHcdxMJFogoW","executionInfo":{"status":"ok","timestamp":1620768721807,"user_tz":240,"elapsed":7372,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df3['new_sp_defense']= df3['sp_defense']\n","for i in df3[mask_upper].index:\n","  df3.loc[i,'new_sp_defense']=upper"],"execution_count":439,"outputs":[]},{"cell_type":"code","metadata":{"id":"bWM0o1hiogoW","executionInfo":{"status":"ok","timestamp":1620768721807,"user_tz":240,"elapsed":7370,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df3['sp_defense_bin_ef'] = pd.qcut(df3['new_sp_defense'],6)"],"execution_count":440,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BYkvEP75ogoX"},"source":["**speed**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3kCRCxJHogoY","executionInfo":{"status":"ok","timestamp":1620768721808,"user_tz":240,"elapsed":7365,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"2f1aca96-8ed1-4193-fe43-57cf53626e40"},"source":["lower, upper = check_outliers(df3['speed'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":441,"outputs":[{"output_type":"stream","text":["lower = -47.0, upper=183.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"TC9Xteb0ogoY","executionInfo":{"status":"ok","timestamp":1620768721809,"user_tz":240,"elapsed":7365,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df3['speed']< lower"],"execution_count":442,"outputs":[]},{"cell_type":"code","metadata":{"id":"2IN0G8gdogoY","executionInfo":{"status":"ok","timestamp":1620768721809,"user_tz":240,"elapsed":7363,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df3['speed']>upper"],"execution_count":443,"outputs":[]},{"cell_type":"code","metadata":{"id":"84kqhk3fogoY","executionInfo":{"status":"ok","timestamp":1620768721810,"user_tz":240,"elapsed":7362,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df3['new_speed']= df3['speed']\n","for i in df3[mask_upper].index:\n","  df3.loc[i,'new_speed']=upper"],"execution_count":444,"outputs":[]},{"cell_type":"code","metadata":{"id":"jpXhjdldogoZ","executionInfo":{"status":"ok","timestamp":1620768721929,"user_tz":240,"elapsed":7479,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df3['speed_bin_ef'] = pd.qcut(df3['new_speed'],6)"],"execution_count":445,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GR7r8PCNogoZ"},"source":["**total_points**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dWiJdxpAogoZ","executionInfo":{"status":"ok","timestamp":1620768721929,"user_tz":240,"elapsed":7474,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"a2275032-873e-405d-e8f6-c3649bc8e028"},"source":["lower, upper = check_outliers(df3['total_points'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":446,"outputs":[{"output_type":"stream","text":["lower = -20.0, upper=880.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"cou6K5wIogoZ","executionInfo":{"status":"ok","timestamp":1620768721929,"user_tz":240,"elapsed":7472,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df3['total_points']< lower"],"execution_count":447,"outputs":[]},{"cell_type":"code","metadata":{"id":"jSgiF5NOogoZ","executionInfo":{"status":"ok","timestamp":1620768721929,"user_tz":240,"elapsed":7470,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df3['total_points']>upper"],"execution_count":448,"outputs":[]},{"cell_type":"code","metadata":{"id":"eAMwTPDvogoa","executionInfo":{"status":"ok","timestamp":1620768721930,"user_tz":240,"elapsed":7469,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df3['total_points_bin_ef'] = pd.qcut(df3['total_points'],6)"],"execution_count":449,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"2GfoVLCVogoa"},"source":["**catch_rate**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bkJUD7vCogoa","executionInfo":{"status":"ok","timestamp":1620768721930,"user_tz":240,"elapsed":7464,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"70e5494b-2572-4be6-9880-dd1829c77f37"},"source":["lower, upper = check_outliers(df3['catch_rate'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":450,"outputs":[{"output_type":"stream","text":["lower = -105.0, upper=270.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"dcsA3cAbogob","executionInfo":{"status":"ok","timestamp":1620768722068,"user_tz":240,"elapsed":7600,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df3['catch_rate']< lower"],"execution_count":451,"outputs":[]},{"cell_type":"code","metadata":{"id":"_x8OfE3Bogob","executionInfo":{"status":"ok","timestamp":1620768722069,"user_tz":240,"elapsed":7599,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df3['catch_rate']>upper"],"execution_count":452,"outputs":[]},{"cell_type":"code","metadata":{"id":"JyzS8iumogob","executionInfo":{"status":"ok","timestamp":1620768722069,"user_tz":240,"elapsed":7598,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df3['catch_rate_bin_ef'] = pd.qcut(df3['catch_rate'],4)"],"execution_count":453,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3oowxLG-ogob"},"source":["**base_experience**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dch5ITZxogoc","executionInfo":{"status":"ok","timestamp":1620768722069,"user_tz":240,"elapsed":7593,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"fdc5cb8a-a778-4688-ae48-5700f8fde5e6"},"source":["lower, upper = check_outliers(df3['base_experience'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":454,"outputs":[{"output_type":"stream","text":["lower = -223.0, upper=512.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Wi65f8rAogoc","executionInfo":{"status":"ok","timestamp":1620768722069,"user_tz":240,"elapsed":7590,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df3['base_experience']< lower"],"execution_count":455,"outputs":[]},{"cell_type":"code","metadata":{"id":"UZr97ezfogoc","executionInfo":{"status":"ok","timestamp":1620768722070,"user_tz":240,"elapsed":7590,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df3['base_experience']>upper"],"execution_count":456,"outputs":[]},{"cell_type":"code","metadata":{"id":"MumtN5Crogoc","executionInfo":{"status":"ok","timestamp":1620768722071,"user_tz":240,"elapsed":7589,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df3['new_base_experience']= df3['base_experience']\n","for i in df3[mask_upper].index:\n","  df3.loc[i,'new_base_experience']=upper"],"execution_count":457,"outputs":[]},{"cell_type":"code","metadata":{"id":"PXRsx_yiogod","executionInfo":{"status":"ok","timestamp":1620768722071,"user_tz":240,"elapsed":7587,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#EF binning should be best for this one as well bc of the skewed data\n","df3['base_experience_bin_ef'] = pd.qcut(df3['new_base_experience'],6)"],"execution_count":458,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gAUy8Ivnogod"},"source":["**egg_cycles**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Buc87nIgogod","executionInfo":{"status":"ok","timestamp":1620768722071,"user_tz":240,"elapsed":7582,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"26c75bee-7791-4bdf-8a2a-a07238eccb6b"},"source":["lower, upper = check_outliers(df3['egg_cycles'])\n","print(f'lower = {lower}, upper={upper}')"],"execution_count":459,"outputs":[{"output_type":"stream","text":["lower = 10.0, upper=35.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"HtmBBkaZogoe","executionInfo":{"status":"ok","timestamp":1620768722072,"user_tz":240,"elapsed":7581,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_lower = df3['egg_cycles']< lower"],"execution_count":460,"outputs":[]},{"cell_type":"code","metadata":{"id":"YQIM05USogof","executionInfo":{"status":"ok","timestamp":1620768722211,"user_tz":240,"elapsed":7718,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["mask_upper = df3['egg_cycles']>upper"],"execution_count":461,"outputs":[]},{"cell_type":"code","metadata":{"id":"qTinj8y6ogof","executionInfo":{"status":"ok","timestamp":1620768722314,"user_tz":240,"elapsed":7819,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#let's replace these outliers with upper boundary.\n","df3['new_egg_cycles']= df3['egg_cycles']\n","for i in df3[mask_upper].index:\n","  df3.loc[i,'new_egg_cycles']=upper\n","for i in df3[mask_lower].index:\n","  df3.loc[i,'new_egg_cycles']=lower"],"execution_count":462,"outputs":[]},{"cell_type":"code","metadata":{"id":"Or6VgKCLogof","executionInfo":{"status":"ok","timestamp":1620768722314,"user_tz":240,"elapsed":7818,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["df3['egg_cycles_bin_ew'] = pd.cut(df3['new_egg_cycles'],3)"],"execution_count":463,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"d_M4g-Uaogog"},"source":["**removing unneeded columns**"]},{"cell_type":"code","metadata":{"id":"Y7JSkzw_ogog","executionInfo":{"status":"ok","timestamp":1620768722314,"user_tz":240,"elapsed":7816,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["df3 = df3.drop(labels=['new_height_m', 'new_weight_kg', 'new_hp',\n","       'new_defense', 'new_sp_attack', 'new_sp_defense', 'new_speed',\n","       'new_base_experience', 'new_egg_cycles'], axis = 1)\n","df3b = df3.drop(labels=['height_m', 'weight_kg', 'total_points', 'hp',\n","        'attack', 'defense', 'sp_attack', 'sp_defense', 'speed', 'catch_rate',\n","       'base_experience', 'egg_cycles'], axis = 1)"],"execution_count":464,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"NqYwLgb9ogog","executionInfo":{"status":"ok","timestamp":1620768722436,"user_tz":240,"elapsed":7931,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"f12d3399-a072-46cc-c4c4-6f1e1bd1cde5"},"source":["df3b"],"execution_count":465,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>status</th>\n","      <th>type_1</th>\n","      <th>growth_rate</th>\n","      <th>egg_type_1</th>\n","      <th>height_m_bin_ef</th>\n","      <th>weight_kg_bin_ef</th>\n","      <th>hp_bin_ef</th>\n","      <th>attack_bin_ef</th>\n","      <th>defense_bin_ef</th>\n","      <th>sp_attack_bin_ef</th>\n","      <th>sp_defense_bin_ef</th>\n","      <th>speed_bin_ef</th>\n","      <th>total_points_bin_ef</th>\n","      <th>catch_rate_bin_ef</th>\n","      <th>base_experience_bin_ef</th>\n","      <th>egg_cycles_bin_ew</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Normal</td>\n","      <td>Grass</td>\n","      <td>Medium Slow</td>\n","      <td>Grass</td>\n","      <td>(0.6, 1.0]</td>\n","      <td>(0.099, 9.5]</td>\n","      <td>(0.999, 48.0]</td>\n","      <td>(4.999, 50.0]</td>\n","      <td>(48.0, 60.0]</td>\n","      <td>(55.0, 70.0]</td>\n","      <td>(60.0, 70.0]</td>\n","      <td>(40.0, 55.0]</td>\n","      <td>(309.0, 400.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(62.0, 137.0]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Normal</td>\n","      <td>Grass</td>\n","      <td>Medium Slow</td>\n","      <td>Grass</td>\n","      <td>(0.6, 1.0]</td>\n","      <td>(9.5, 31.5]</td>\n","      <td>(48.0, 60.0]</td>\n","      <td>(50.0, 65.0]</td>\n","      <td>(60.0, 70.0]</td>\n","      <td>(70.0, 85.0]</td>\n","      <td>(70.0, 85.0]</td>\n","      <td>(55.0, 68.0]</td>\n","      <td>(400.0, 470.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(137.0, 165.0]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Normal</td>\n","      <td>Grass</td>\n","      <td>Medium Slow</td>\n","      <td>Grass</td>\n","      <td>(1.6, 3.6]</td>\n","      <td>(78.05, 215.15]</td>\n","      <td>(78.0, 95.0]</td>\n","      <td>(80.0, 95.0]</td>\n","      <td>(70.0, 85.0]</td>\n","      <td>(85.0, 105.0]</td>\n","      <td>(85.0, 100.0]</td>\n","      <td>(68.0, 84.0]</td>\n","      <td>(500.0, 570.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(179.0, 248.333]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Normal</td>\n","      <td>Grass</td>\n","      <td>Medium Slow</td>\n","      <td>Grass</td>\n","      <td>(1.6, 3.6]</td>\n","      <td>(78.05, 215.15]</td>\n","      <td>(78.0, 95.0]</td>\n","      <td>(95.0, 115.0]</td>\n","      <td>(105.0, 177.0]</td>\n","      <td>(105.0, 185.0]</td>\n","      <td>(100.0, 164.0]</td>\n","      <td>(68.0, 84.0]</td>\n","      <td>(570.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(248.333, 512.0]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Normal</td>\n","      <td>Fire</td>\n","      <td>Medium Slow</td>\n","      <td>Dragon</td>\n","      <td>(0.099, 0.6]</td>\n","      <td>(0.099, 9.5]</td>\n","      <td>(0.999, 48.0]</td>\n","      <td>(50.0, 65.0]</td>\n","      <td>(4.999, 48.0]</td>\n","      <td>(55.0, 70.0]</td>\n","      <td>(45.0, 60.0]</td>\n","      <td>(55.0, 68.0]</td>\n","      <td>(174.999, 309.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(35.999, 62.0]</td>\n","      <td>(18.333, 26.667]</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1591</th>\n","      <td>Sub Legendary</td>\n","      <td>Water</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.6, 3.6]</td>\n","      <td>(78.05, 215.15]</td>\n","      <td>(95.0, 147.5]</td>\n","      <td>(115.0, 190.0]</td>\n","      <td>(85.0, 105.0]</td>\n","      <td>(55.0, 70.0]</td>\n","      <td>(45.0, 60.0]</td>\n","      <td>(84.0, 100.0]</td>\n","      <td>(500.0, 570.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(248.333, 512.0]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1592</th>\n","      <td>Mythical</td>\n","      <td>Grass</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.6, 3.6]</td>\n","      <td>(31.5, 78.05]</td>\n","      <td>(95.0, 147.5]</td>\n","      <td>(115.0, 190.0]</td>\n","      <td>(85.0, 105.0]</td>\n","      <td>(55.0, 70.0]</td>\n","      <td>(85.0, 100.0]</td>\n","      <td>(100.0, 183.0]</td>\n","      <td>(570.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(248.333, 512.0]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1593</th>\n","      <td>Legendary</td>\n","      <td>Grass</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.0, 1.6]</td>\n","      <td>(0.099, 9.5]</td>\n","      <td>(95.0, 147.5]</td>\n","      <td>(65.0, 80.0]</td>\n","      <td>(70.0, 85.0]</td>\n","      <td>(70.0, 85.0]</td>\n","      <td>(70.0, 85.0]</td>\n","      <td>(68.0, 84.0]</td>\n","      <td>(470.0, 500.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(248.333, 512.0]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1594</th>\n","      <td>Legendary</td>\n","      <td>Ice</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.6, 3.6]</td>\n","      <td>(78.05, 215.15]</td>\n","      <td>(95.0, 147.5]</td>\n","      <td>(115.0, 190.0]</td>\n","      <td>(105.0, 177.0]</td>\n","      <td>(70.0, 85.0]</td>\n","      <td>(100.0, 164.0]</td>\n","      <td>(40.0, 55.0]</td>\n","      <td>(570.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(248.333, 512.0]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1595</th>\n","      <td>Legendary</td>\n","      <td>Ghost</td>\n","      <td>Slow</td>\n","      <td>Undiscovered</td>\n","      <td>(1.6, 3.6]</td>\n","      <td>(31.5, 78.05]</td>\n","      <td>(95.0, 147.5]</td>\n","      <td>(80.0, 95.0]</td>\n","      <td>(70.0, 85.0]</td>\n","      <td>(105.0, 185.0]</td>\n","      <td>(85.0, 100.0]</td>\n","      <td>(100.0, 183.0]</td>\n","      <td>(570.0, 780.0]</td>\n","      <td>(2.999, 45.0]</td>\n","      <td>(248.333, 512.0]</td>\n","      <td>(26.667, 35.0]</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1596 rows × 16 columns</p>\n","</div>"],"text/plain":["             status type_1  ... base_experience_bin_ef egg_cycles_bin_ew\n","0            Normal  Grass  ...          (62.0, 137.0]  (18.333, 26.667]\n","1            Normal  Grass  ...         (137.0, 165.0]  (18.333, 26.667]\n","2            Normal  Grass  ...       (179.0, 248.333]  (18.333, 26.667]\n","3            Normal  Grass  ...       (248.333, 512.0]  (18.333, 26.667]\n","4            Normal   Fire  ...         (35.999, 62.0]  (18.333, 26.667]\n","...             ...    ...  ...                    ...               ...\n","1591  Sub Legendary  Water  ...       (248.333, 512.0]    (26.667, 35.0]\n","1592       Mythical  Grass  ...       (248.333, 512.0]    (26.667, 35.0]\n","1593      Legendary  Grass  ...       (248.333, 512.0]    (26.667, 35.0]\n","1594      Legendary    Ice  ...       (248.333, 512.0]    (26.667, 35.0]\n","1595      Legendary  Ghost  ...       (248.333, 512.0]    (26.667, 35.0]\n","\n","[1596 rows x 16 columns]"]},"metadata":{"tags":[]},"execution_count":465}]},{"cell_type":"markdown","metadata":{"id":"kppFNYp0gbWy"},"source":["# **Decision Tree**"]},{"cell_type":"code","metadata":{"id":"kx8PZNzbggJX","executionInfo":{"status":"ok","timestamp":1620768722436,"user_tz":240,"elapsed":7920,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["from sklearn.tree import DecisionTreeClassifier\n","from sklearn.model_selection import cross_val_score\n","from sklearn.model_selection import cross_val_predict\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import recall_score\n","from sklearn.metrics import precision_score\n","from sklearn.externals import joblib\n","from sklearn.tree import export_graphviz\n","import graphviz"],"execution_count":466,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"hEyNUHKqg8O1"},"source":["### Process data set for tree training"]},{"cell_type":"code","metadata":{"id":"PWXuH1jyhBon","executionInfo":{"status":"ok","timestamp":1620768722437,"user_tz":240,"elapsed":7919,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#this takes out the class label (type_1) from dataset x\n","x1a = df1a.iloc[:, 1:38]  \n","x2a = df2a.iloc[:, 1:38]\n","x3a = df3a.iloc[:, 1:38]"],"execution_count":467,"outputs":[]},{"cell_type":"code","metadata":{"id":"mFaxBFYcWueG","executionInfo":{"status":"ok","timestamp":1620768722437,"user_tz":240,"elapsed":7917,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["x3b = df3b.iloc[:,2:16]"],"execution_count":468,"outputs":[]},{"cell_type":"code","metadata":{"id":"8X1FDY2EkdAl","executionInfo":{"status":"ok","timestamp":1620768722437,"user_tz":240,"elapsed":7915,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#create dictionaries for each data set\n","pokemon1a = {}\n","pokemon1a['feature_names_1a'] = x1a.columns.values\n","\n","pokemon2a = {}\n","pokemon2a['feature_names_2a'] = x2a.columns.values\n","\n","pokemon3a = {}\n","pokemon3a['feature_names_3a'] = x3a.columns.values"],"execution_count":469,"outputs":[]},{"cell_type":"code","metadata":{"id":"8HemQKQ-XHOM","executionInfo":{"status":"ok","timestamp":1620768722569,"user_tz":240,"elapsed":8046,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["pokemon3b = {}\n","pokemon3b['feature_names_3b'] = x3b.columns.values"],"execution_count":470,"outputs":[]},{"cell_type":"code","metadata":{"id":"nOYGHuJHkwMC","executionInfo":{"status":"ok","timestamp":1620768722570,"user_tz":240,"elapsed":8045,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["pokemon1a['data_1a'] = x1a.values\n","pokemon2a['data_2a'] = x2a.values\n","pokemon3a['data_3a'] = x3a.values\n"],"execution_count":471,"outputs":[]},{"cell_type":"code","metadata":{"id":"pOEU3PvyXUYy","executionInfo":{"status":"ok","timestamp":1620768722570,"user_tz":240,"elapsed":8043,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["pokemon3b['data_3b'] = x3b.values"],"execution_count":472,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"UnGmjip6k4te","executionInfo":{"status":"ok","timestamp":1620768722707,"user_tz":240,"elapsed":8165,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"f8cb6b3d-4417-4244-ac37-18ecd7a2d8d7"},"source":["#create y data set = class label\n","y1a = df1a.iloc[:,0:1]\n","y1a"],"execution_count":473,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>type_1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Fire</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1040</th>\n","      <td>Ice</td>\n","    </tr>\n","    <tr>\n","      <th>1041</th>\n","      <td>Ghost</td>\n","    </tr>\n","    <tr>\n","      <th>1042</th>\n","      <td>Psychic</td>\n","    </tr>\n","    <tr>\n","      <th>1043</th>\n","      <td>Psychic</td>\n","    </tr>\n","    <tr>\n","      <th>1044</th>\n","      <td>Psychic</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1044 rows × 1 columns</p>\n","</div>"],"text/plain":["       type_1\n","0       Grass\n","1       Grass\n","2       Grass\n","3       Grass\n","4        Fire\n","...       ...\n","1040      Ice\n","1041    Ghost\n","1042  Psychic\n","1043  Psychic\n","1044  Psychic\n","\n","[1044 rows x 1 columns]"]},"metadata":{"tags":[]},"execution_count":473}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"kI9AjgRZJqzY","executionInfo":{"status":"ok","timestamp":1620768722708,"user_tz":240,"elapsed":8153,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"cae76380-e165-4f9c-c482-2c70a788824a"},"source":["y2a = df2a.iloc[:,0:1]\n","y2a"],"execution_count":474,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>type_1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>4</th>\n","      <td>Fire</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Fire</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Water</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Water</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Water</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1034</th>\n","      <td>Fighting</td>\n","    </tr>\n","    <tr>\n","      <th>1038</th>\n","      <td>Electric</td>\n","    </tr>\n","    <tr>\n","      <th>1039</th>\n","      <td>Dragon</td>\n","    </tr>\n","    <tr>\n","      <th>1040</th>\n","      <td>Ice</td>\n","    </tr>\n","    <tr>\n","      <th>1041</th>\n","      <td>Ghost</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>492 rows × 1 columns</p>\n","</div>"],"text/plain":["        type_1\n","4         Fire\n","5         Fire\n","9        Water\n","10       Water\n","11       Water\n","...        ...\n","1034  Fighting\n","1038  Electric\n","1039    Dragon\n","1040       Ice\n","1041     Ghost\n","\n","[492 rows x 1 columns]"]},"metadata":{"tags":[]},"execution_count":474}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"fv5lCLhhJzPN","executionInfo":{"status":"ok","timestamp":1620768722708,"user_tz":240,"elapsed":8140,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"74daed11-006f-4f24-a19b-37dd3a2a599a"},"source":["y3a = df3a.iloc[:,0:1]\n","y3a"],"execution_count":475,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>type_1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Fire</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1591</th>\n","      <td>Water</td>\n","    </tr>\n","    <tr>\n","      <th>1592</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>1593</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>1594</th>\n","      <td>Ice</td>\n","    </tr>\n","    <tr>\n","      <th>1595</th>\n","      <td>Ghost</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1596 rows × 1 columns</p>\n","</div>"],"text/plain":["     type_1\n","0     Grass\n","1     Grass\n","2     Grass\n","3     Grass\n","4      Fire\n","...     ...\n","1591  Water\n","1592  Grass\n","1593  Grass\n","1594    Ice\n","1595  Ghost\n","\n","[1596 rows x 1 columns]"]},"metadata":{"tags":[]},"execution_count":475}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"F-fvNljYXaTQ","executionInfo":{"status":"ok","timestamp":1620768722708,"user_tz":240,"elapsed":8127,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"69d2c176-59ba-432e-f00c-55b654a4ec1d"},"source":["y3b = df3b.iloc[:,1:2]\n","y3b"],"execution_count":476,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>type_1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Fire</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1591</th>\n","      <td>Water</td>\n","    </tr>\n","    <tr>\n","      <th>1592</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>1593</th>\n","      <td>Grass</td>\n","    </tr>\n","    <tr>\n","      <th>1594</th>\n","      <td>Ice</td>\n","    </tr>\n","    <tr>\n","      <th>1595</th>\n","      <td>Ghost</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1596 rows × 1 columns</p>\n","</div>"],"text/plain":["     type_1\n","0     Grass\n","1     Grass\n","2     Grass\n","3     Grass\n","4      Fire\n","...     ...\n","1591  Water\n","1592  Grass\n","1593  Grass\n","1594    Ice\n","1595  Ghost\n","\n","[1596 rows x 1 columns]"]},"metadata":{"tags":[]},"execution_count":476}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IY8OlkkilMuB","executionInfo":{"status":"ok","timestamp":1620768722709,"user_tz":240,"elapsed":8114,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"c5c63dc0-684e-4d49-aa64-78ae7aeaf036"},"source":["# set target_names \n","pokemon1a['target_names_1a'] = y1a['type_1'].unique()\n","pokemon1a['target_names_1a']   #shows class label\n"],"execution_count":477,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['Grass', 'Fire', 'Water', 'Bug', 'Normal', 'Dark', 'Poison',\n","       'Electric', 'Ground', 'Ice', 'Fairy', 'Steel', 'Fighting',\n","       'Psychic', 'Rock', 'Ghost', 'Dragon', 'Flying'], dtype=object)"]},"metadata":{"tags":[]},"execution_count":477}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i1OfbQ2wKS3h","executionInfo":{"status":"ok","timestamp":1620768722940,"user_tz":240,"elapsed":8331,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"d0b19d81-c02e-4156-ec1d-e86828a87f44"},"source":["pokemon2a['target_names_2a'] = y2a['type_1'].unique()\n","pokemon2a['target_names_2a']"],"execution_count":478,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['Fire', 'Water', 'Bug', 'Normal', 'Poison', 'Electric', 'Ground',\n","       'Fairy', 'Ice', 'Dark', 'Steel', 'Fighting', 'Psychic', 'Grass',\n","       'Dragon', 'Rock', 'Ghost', 'Flying'], dtype=object)"]},"metadata":{"tags":[]},"execution_count":478}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MtNj0oz9K3ws","executionInfo":{"status":"ok","timestamp":1620768722940,"user_tz":240,"elapsed":8317,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"e48123da-64e2-4404-e671-775b7a141961"},"source":["pokemon3a['target_names_3a'] = y3a['type_1'].unique()\n","pokemon3a['target_names_3a']"],"execution_count":479,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['Grass', 'Fire', 'Water', 'Bug', 'Normal', 'Dark', 'Poison',\n","       'Electric', 'Ground', 'Ice', 'Fairy', 'Steel', 'Fighting',\n","       'Psychic', 'Rock', 'Ghost', 'Dragon', 'Flying'], dtype=object)"]},"metadata":{"tags":[]},"execution_count":479}]},{"cell_type":"code","metadata":{"id":"b98zSNIuXrIG","executionInfo":{"status":"ok","timestamp":1620768722940,"user_tz":240,"elapsed":8316,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["pokemon3b['target_names_3b'] = y3b['type_1'].unique()"],"execution_count":480,"outputs":[]},{"cell_type":"code","metadata":{"id":"s6-TlHHmldwL","executionInfo":{"status":"ok","timestamp":1620768722941,"user_tz":240,"elapsed":8315,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#shows class label for each row\n","pokemon1a['target_1a'] = y1a['type_1'].values\n","pokemon2a['target_2a'] = y2a['type_1'].values\n","pokemon3a['target_3a'] = y3a['type_1'].values"],"execution_count":481,"outputs":[]},{"cell_type":"code","metadata":{"id":"ue8tZ20vX1V1","executionInfo":{"status":"ok","timestamp":1620768722941,"user_tz":240,"elapsed":8313,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#pokemon3b['target_3b'] = y3b['type_1'].values()"],"execution_count":482,"outputs":[]},{"cell_type":"code","metadata":{"id":"nohzplbQl3Vm","executionInfo":{"status":"ok","timestamp":1620768722941,"user_tz":240,"elapsed":8311,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#split datasets into training and testing parts \n","x1a_train, x1a_test, y1a_train, y1a_test = train_test_split( pokemon1a['data_1a'], pokemon1a['target_1a'],\n","                                                    test_size=0.2, random_state=1,\n","                                                    stratify = pokemon1a['target_1a'])\n","\n","x2a_train, x2a_test, y2a_train, y2a_test = train_test_split( pokemon2a['data_2a'], pokemon2a['target_2a'],\n","                                                    test_size=0.3, random_state=1,\n","                                                    stratify = pokemon2a['target_2a'])\n","\n","x3a_train, x3a_test, y3a_train, y3a_test = train_test_split( pokemon3a['data_3a'], pokemon3a['target_3a'],\n","                                                    test_size=0.3, random_state=1,\n","                                                    stratify = pokemon3a['target_3a'])"],"execution_count":483,"outputs":[]},{"cell_type":"code","metadata":{"id":"wtB1uyaBX8op","executionInfo":{"status":"ok","timestamp":1620768722942,"user_tz":240,"elapsed":8310,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#x3b_train, x3b_test, y3b_train, y3b_test = train_test_split( pokemon3b['data_3b'], pokemon3b['target_3b'],\n","                                                    #test_size=0.3, random_state=1,\n","                                                    #stratify = pokemon3b['target_3b'])"],"execution_count":484,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sE5E747umlw-","executionInfo":{"status":"ok","timestamp":1620768722942,"user_tz":240,"elapsed":8296,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"73beac88-69ec-4ed4-daac-3494629040bc"},"source":["x1a_train.shape, y1a_train.shape, x1a_test.shape, y1a_test.shape    #730 train, 314 test, 37 columns for x"],"execution_count":485,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((835, 37), (835,), (209, 37), (209,))"]},"metadata":{"tags":[]},"execution_count":485}]},{"cell_type":"markdown","metadata":{"id":"sI4SSMNEm39a"},"source":["# Build Decision Tree"]},{"cell_type":"markdown","metadata":{"id":"jqMM8V2hoj_q"},"source":["Split Method \n","\n","1st round of building - Original Trees with no limits"]},{"cell_type":"markdown","metadata":{"id":"wClGepVh1pw5"},"source":["df1"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"msOaMiDPm7Zw","executionInfo":{"status":"ok","timestamp":1620768723076,"user_tz":240,"elapsed":8416,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"3c07002c-1889-4b5b-a4ca-30642368a16d"},"source":["#fit data\n","tree1a_entropy = DecisionTreeClassifier(criterion='entropy')\n","tree1a_entropy.fit(x1a_train, y1a_train)"],"execution_count":486,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n","                       max_depth=None, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=1, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":486}]},{"cell_type":"code","metadata":{"id":"vra0P8eanROF","executionInfo":{"status":"ok","timestamp":1620768723077,"user_tz":240,"elapsed":8415,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["dot_data = export_graphviz(tree1a_entropy, out_file = None,\n","                          feature_names = pokemon1a['feature_names_1a'],\n","                          class_names = pokemon1a['target_names_1a'],\n","                          filled=True)\n","#draw graph\n","graph_1a_original = graphviz.Source(dot_data)\n","#graph"],"execution_count":487,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mkIrxfUdnotL","executionInfo":{"status":"ok","timestamp":1620768723077,"user_tz":240,"elapsed":8401,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"581867ba-692c-4b6f-d414-ad576a85debb"},"source":["#find out which features are most important = egg_cycles\n","tree1a_entropy.feature_importances_"],"execution_count":488,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.02990152, 0.05106922, 0.01969654, 0.06896857, 0.0662078 ,\n","       0.04975082, 0.08790085, 0.04558632, 0.06060051, 0.03492274,\n","       0.01476269, 0.11513311, 0.        , 0.00253828, 0.        ,\n","       0.        , 0.04133132, 0.0820561 , 0.        , 0.01941837,\n","       0.02145385, 0.02363874, 0.01494998, 0.03130071, 0.01827237,\n","       0.02531668, 0.        , 0.00297356, 0.        , 0.        ,\n","       0.00098664, 0.00147604, 0.00765831, 0.00208873, 0.0272414 ,\n","       0.02587113, 0.00692713])"]},"metadata":{"tags":[]},"execution_count":488}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JnQVrifPn4sx","executionInfo":{"status":"ok","timestamp":1620768723220,"user_tz":240,"elapsed":8530,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"08d6c62f-c7df-4d23-883e-6dbe92462511"},"source":["#evaluate tree performance with test dataset - mean accuracy\n","original_tree1a_score = tree1a_entropy.score(x1a_test, y1a_test)\n","original_tree1a_score"],"execution_count":489,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.48325358851674644"]},"metadata":{"tags":[]},"execution_count":489}]},{"cell_type":"markdown","metadata":{"id":"o-XuYs-HoVnN"},"source":["2nd Round of building - set limits for # tree leaves and depth - df1"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Oaruc2PgozeM","executionInfo":{"status":"ok","timestamp":1620768723220,"user_tz":240,"elapsed":8516,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"884e52ce-acc5-4b9a-ae2a-63293dc2cc09"},"source":["#to avoid overfitting - use max_depth=3, min_samples_leaf = 5 as an initial tree depth to get a feel\n","\n","\n","#1a\n","tree1a_entropy_3 = DecisionTreeClassifier(max_depth=3, min_samples_leaf=5, criterion='entropy')\n","tree1a_entropy_3.fit(x1a_train, y1a_train)"],"execution_count":490,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n","                       max_depth=3, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=5, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":490}]},{"cell_type":"code","metadata":{"id":"79KOEfXSpIgQ","executionInfo":{"status":"ok","timestamp":1620768723220,"user_tz":240,"elapsed":8514,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["dot_data= export_graphviz(tree1a_entropy_3, out_file=None,\n","                         feature_names = pokemon1a['feature_names_1a'],\n","                         class_names = pokemon1a['target_names_1a'],\n","                         filled=True, rounded=True,\n","                         special_characters=True)\n","\n","graph_1a_3 = graphviz.Source(dot_data)\n","#graph"],"execution_count":491,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oZNKedrfp3XW","executionInfo":{"status":"ok","timestamp":1620768723221,"user_tz":240,"elapsed":8502,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"c6748eee-464b-4f10-a80b-5c2106e69c33"},"source":["depth3_tree1a_score = tree1a_entropy_3.score(x1a_test, y1a_test)\n","depth3_tree1a_score"],"execution_count":492,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.28708133971291866"]},"metadata":{"tags":[]},"execution_count":492}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YICOUtYYrLmg","executionInfo":{"status":"ok","timestamp":1620768723221,"user_tz":240,"elapsed":8489,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"8a666144-518a-40ec-96c9-93d54f801210"},"source":["#depth of 3 caused accuracy to go down, so try another setting\n","tree1a_entropy_5 = DecisionTreeClassifier(max_depth=5, min_samples_leaf=5, criterion='entropy')\n","tree1a_entropy_5.fit(x1a_train, y1a_train)"],"execution_count":493,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n","                       max_depth=5, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=5, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":493}]},{"cell_type":"code","metadata":{"id":"FjDMW2Q3sVbt","executionInfo":{"status":"ok","timestamp":1620768723360,"user_tz":240,"elapsed":8626,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["dot_data= export_graphviz(tree1a_entropy_5, out_file=None,\n","                         feature_names = pokemon1a['feature_names_1a'],\n","                         class_names = pokemon1a['target_names_1a'],\n","                         filled=True, rounded=True,\n","                         special_characters=True)\n","\n","graph_1a_5 = graphviz.Source(dot_data)\n","#graph"],"execution_count":494,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4A3MeXWwsZSm","executionInfo":{"status":"ok","timestamp":1620768723361,"user_tz":240,"elapsed":8614,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"49262cac-a24b-4728-cdfc-b79865e77476"},"source":["depth5_tree1a_score = tree1a_entropy_5.score(x1a_test, y1a_test)\n","depth5_tree1a_score"],"execution_count":495,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.3492822966507177"]},"metadata":{"tags":[]},"execution_count":495}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NZ_ktyedqDbM","executionInfo":{"status":"ok","timestamp":1620768723361,"user_tz":240,"elapsed":8601,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"2613f5d5-de73-4948-eb3a-447644adcd48"},"source":["#depth of 5 caused accuracy to increase, so increase a little more\n","tree1a_entropy_7 = DecisionTreeClassifier(max_depth=7, min_samples_leaf=5, criterion='entropy')\n","tree1a_entropy_7.fit(x1a_train, y1a_train)"],"execution_count":496,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n","                       max_depth=7, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=5, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":496}]},{"cell_type":"code","metadata":{"id":"yf_EefbTrIOS","executionInfo":{"status":"ok","timestamp":1620768723361,"user_tz":240,"elapsed":8599,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["dot_data= export_graphviz(tree1a_entropy_7, out_file=None,\n","                         feature_names = pokemon1a['feature_names_1a'],\n","                         class_names = pokemon1a['target_names_1a'],\n","                         filled=True, rounded=True,\n","                         special_characters=True)\n","\n","graph_1a_7 = graphviz.Source(dot_data)\n","#graph"],"execution_count":497,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yir5UpUErJXT","executionInfo":{"status":"ok","timestamp":1620768723543,"user_tz":240,"elapsed":8768,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"9b6b59ba-a568-4473-c14b-453ef90aa508"},"source":["depth7_tree1a_score = tree1a_entropy_7.score(x1a_test, y1a_test)\n","depth7_tree1a_score"],"execution_count":498,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.41148325358851673"]},"metadata":{"tags":[]},"execution_count":498}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dNwTqdwbs9o8","executionInfo":{"status":"ok","timestamp":1620768723544,"user_tz":240,"elapsed":8755,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"826b9b3d-fe15-4c53-fc31-2f59d285b31e"},"source":["print(f\"original tree 1a score = {original_tree1a_score} with depth as {tree1a_entropy.tree_.max_depth}\\n\\\n","depth three tree 1a score = {depth3_tree1a_score}\\n\\\n","depth five tree 1a score = {depth5_tree1a_score}\\n\\\n","depth seven tree 1a score = {depth7_tree1a_score}\")"],"execution_count":499,"outputs":[{"output_type":"stream","text":["original tree 1a score = 0.48325358851674644 with depth as 17\n","depth three tree 1a score = 0.28708133971291866\n","depth five tree 1a score = 0.3492822966507177\n","depth seven tree 1a score = 0.41148325358851673\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"T8onCG3-tLYp"},"source":["**with depth seven, there is about the same accuracy as original with much less depth (makes it easier to visualize), so we will go with this tree for df1a**"]},{"cell_type":"markdown","metadata":{"id":"PjgYHav814dd"},"source":["1st round of building for df2"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"biWwHrGI1NOG","executionInfo":{"status":"ok","timestamp":1620768723545,"user_tz":240,"elapsed":8743,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"0b2f6a67-71d7-451e-dbc0-ca7903e39841"},"source":["tree2a_entropy = DecisionTreeClassifier(criterion='entropy')\n","tree2a_entropy.fit(x2a_train, y2a_train)"],"execution_count":500,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n","                       max_depth=None, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=1, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":500}]},{"cell_type":"code","metadata":{"id":"DQFs5rRiymNB","executionInfo":{"status":"ok","timestamp":1620768723545,"user_tz":240,"elapsed":8741,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["\n","dot_data = export_graphviz(tree2a_entropy, out_file = None,\n","                          feature_names = pokemon2a['feature_names_2a'],\n","                          class_names = pokemon2a['target_names_2a'],\n","                          filled=True)\n","#draw graph\n","graph_2a_original = graphviz.Source(dot_data)\n","#graph"],"execution_count":501,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sd2HhUHQy3hO","executionInfo":{"status":"ok","timestamp":1620768723755,"user_tz":240,"elapsed":8937,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"c144ffbf-f656-4889-c3a5-76f902bd5925"},"source":["original_tree2a_score = tree2a_entropy.score(x2a_test, y2a_test)\n","original_tree2a_score"],"execution_count":502,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.39864864864864863"]},"metadata":{"tags":[]},"execution_count":502}]},{"cell_type":"markdown","metadata":{"id":"pXrquz_J1_08"},"source":["Second round of building df2"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y3AxvEq7zVr0","executionInfo":{"status":"ok","timestamp":1620768723755,"user_tz":240,"elapsed":8923,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"dabbf603-ed7f-44a4-8ed0-ca9ce5c7ff09"},"source":["tree2a_entropy_3 = DecisionTreeClassifier(max_depth=3, min_samples_leaf=5, criterion='entropy')\n","tree2a_entropy_3.fit(x2a_train, y2a_train)"],"execution_count":503,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n","                       max_depth=3, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=5, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":503}]},{"cell_type":"code","metadata":{"id":"V2w0_qu30h8f","executionInfo":{"status":"ok","timestamp":1620768723755,"user_tz":240,"elapsed":8921,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["dot_data= export_graphviz(tree2a_entropy_3, out_file=None,\n","                         feature_names = pokemon2a['feature_names_2a'],\n","                         class_names = pokemon2a['target_names_2a'],\n","                         filled=True, rounded=True,\n","                         special_characters=True)\n","\n","graph_2a_3 = graphviz.Source(dot_data)\n","#graph"],"execution_count":504,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9YAN56Qf0mor","executionInfo":{"status":"ok","timestamp":1620768723756,"user_tz":240,"elapsed":8908,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"50e22347-3eb5-46a6-eeb8-ad9fe7623fbb"},"source":["depth3_tree2a_score = tree2a_entropy_3.score(x2a_test, y2a_test)\n","depth3_tree2a_score"],"execution_count":505,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.28378378378378377"]},"metadata":{"tags":[]},"execution_count":505}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jiEOxn3Z2IdG","executionInfo":{"status":"ok","timestamp":1620768723756,"user_tz":240,"elapsed":8894,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"a3649385-f0fd-44b1-816b-0a6e8fa80bc6"},"source":["#score dropped, so increase depth\n","tree2a_entropy_8 = DecisionTreeClassifier(max_depth=8, min_samples_leaf=5, criterion='entropy')\n","tree2a_entropy_8.fit(x2a_train, y2a_train)"],"execution_count":506,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n","                       max_depth=8, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=5, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":506}]},{"cell_type":"code","metadata":{"id":"pdc37QED2j52","executionInfo":{"status":"ok","timestamp":1620768723757,"user_tz":240,"elapsed":8884,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["dot_data= export_graphviz(tree2a_entropy_8, out_file=None,\n","                         feature_names = pokemon2a['feature_names_2a'],\n","                         class_names = pokemon2a['target_names_2a'],\n","                         filled=True, rounded=True,\n","                         special_characters=True)\n","\n","graph_2a_8  = graphviz.Source(dot_data)"],"execution_count":507,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uNTiWWHy2ki2","executionInfo":{"status":"ok","timestamp":1620768723757,"user_tz":240,"elapsed":8874,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"906f935d-e043-4644-dfb6-6adc74b2ceca"},"source":["depth8_tree2a_score = tree2a_entropy_8.score(x2a_test, y2a_test)\n","depth8_tree2a_score"],"execution_count":508,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.39864864864864863"]},"metadata":{"tags":[]},"execution_count":508}]},{"cell_type":"code","metadata":{"id":"r91EFlOU2HJB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768723757,"user_tz":240,"elapsed":8861,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"57b9381c-0647-4fea-c672-895c87e76f4f"},"source":["print(f\"original tree 2a score = {original_tree2a_score} with depth as {tree2a_entropy.tree_.max_depth}\\n\\\n","depth three tree 2a score = {depth3_tree2a_score}\\n\\\n","depth eight tree 2a score = {depth8_tree2a_score}\")"],"execution_count":509,"outputs":[{"output_type":"stream","text":["original tree 2a score = 0.39864864864864863 with depth as 16\n","depth three tree 2a score = 0.28378378378378377\n","depth eight tree 2a score = 0.39864864864864863\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CTZePj-S7GA_"},"source":["**with depth eight, there is better accuracy than original with much less depth (makes it easier to visualize), so we will go with this tree for df2a**"]},{"cell_type":"markdown","metadata":{"id":"O-SjDWOe7aEs"},"source":["1st round of building for df3"]},{"cell_type":"code","metadata":{"id":"FN6qPMbT1bB8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768723934,"user_tz":240,"elapsed":9028,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"345a3e71-3b41-4b67-a914-d7cbf1af123d"},"source":["tree3a_entropy = DecisionTreeClassifier(criterion='entropy')\n","tree3a_entropy.fit(x3a_train, y3a_train)"],"execution_count":510,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n","                       max_depth=None, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=1, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":510}]},{"cell_type":"code","metadata":{"id":"Q_Go5zidyvj7","executionInfo":{"status":"ok","timestamp":1620768724062,"user_tz":240,"elapsed":9153,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["\n","dot_data = export_graphviz(tree3a_entropy, out_file = None,\n","                          feature_names = pokemon3a['feature_names_3a'],\n","                          class_names = pokemon3a['target_names_3a'],\n","                          filled=True)\n","#draw graph\n","graph_3a_original = graphviz.Source(dot_data)\n","#graph"],"execution_count":511,"outputs":[]},{"cell_type":"code","metadata":{"id":"VfTBvrs7y8TL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768724062,"user_tz":240,"elapsed":9135,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"36954ce8-20bb-4137-fcfa-cd96f08bc0f1"},"source":["original_tree3a_score = tree3a_entropy.score(x3a_test, y3a_test)\n","original_tree3a_score"],"execution_count":512,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.15031315240083507"]},"metadata":{"tags":[]},"execution_count":512}]},{"cell_type":"markdown","metadata":{"id":"5W13hWgv7j2u"},"source":["2nd round of building for df3"]},{"cell_type":"code","metadata":{"id":"xJj6ukeo7hIF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768724063,"user_tz":240,"elapsed":9127,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"8ae28e91-fd46-4c83-e9dd-33e2a5aaeb7e"},"source":["tree3a_entropy_3 = DecisionTreeClassifier(max_depth=3, min_samples_leaf=5, criterion='entropy')\n","tree3a_entropy_3.fit(x3a_train, y3a_train)"],"execution_count":513,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n","                       max_depth=3, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=5, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":513}]},{"cell_type":"code","metadata":{"id":"7nJGcHfb7w5N","executionInfo":{"status":"ok","timestamp":1620768724064,"user_tz":240,"elapsed":9126,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["dot_data= export_graphviz(tree3a_entropy_3, out_file=None,\n","                         feature_names = pokemon3a['feature_names_3a'],\n","                         class_names = pokemon3a['target_names_3a'],\n","                         filled=True, rounded=True,\n","                         special_characters=True)\n","\n","graph_3a_entropy3 = graphviz.Source(dot_data)\n","#graph"],"execution_count":514,"outputs":[]},{"cell_type":"code","metadata":{"id":"e3nF_vBw7zNI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768724237,"user_tz":240,"elapsed":9289,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"b27f7b70-689f-492f-a499-cf3df7446464"},"source":["depth3_tree3a_score = tree3a_entropy_3.score(x3a_test, y3a_test)\n","depth3_tree3a_score"],"execution_count":515,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.20876826722338204"]},"metadata":{"tags":[]},"execution_count":515}]},{"cell_type":"code","metadata":{"id":"g6NDUybY8K0O","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768724237,"user_tz":240,"elapsed":9279,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"915bc20a-ec24-42ab-de5a-4594830244b4"},"source":["#score increased, so increase again to see if goes up\n","tree3a_entropy_6 = DecisionTreeClassifier(max_depth=6, min_samples_leaf=5, criterion='entropy')\n","tree3a_entropy_6.fit(x3a_train, y3a_train)"],"execution_count":516,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n","                       max_depth=6, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=5, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":516}]},{"cell_type":"code","metadata":{"id":"eWCA407G83EM","executionInfo":{"status":"ok","timestamp":1620768724238,"user_tz":240,"elapsed":9279,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["dot_data= export_graphviz(tree3a_entropy_6, out_file=None,\n","                         feature_names = pokemon3a['feature_names_3a'],\n","                         class_names = pokemon3a['target_names_3a'],\n","                         filled=True, rounded=True,\n","                         special_characters=True)\n","\n","graph_3a_entropy6 = graphviz.Source(dot_data)\n","#graph"],"execution_count":517,"outputs":[]},{"cell_type":"code","metadata":{"id":"qAGSWPyq88an","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768724238,"user_tz":240,"elapsed":9268,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"4b55057f-5823-4da1-ac8f-d6c3761d8ae6"},"source":["depth6_tree3a_score = tree3a_entropy_6.score(x3a_test, y3a_test)\n","depth6_tree3a_score"],"execution_count":518,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.2588726513569937"]},"metadata":{"tags":[]},"execution_count":518}]},{"cell_type":"code","metadata":{"id":"ymfywJpG9WmL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768724238,"user_tz":240,"elapsed":9258,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"30f84372-f01e-45c5-f3a4-234af546810d"},"source":["print(f\"original tree 3a score = {original_tree3a_score} with depth as {tree3a_entropy.tree_.max_depth}\\n\\\n","depth three tree 3a score = {depth3_tree3a_score}\\n\\\n","depth six tree 2a score = {depth6_tree3a_score}\")"],"execution_count":519,"outputs":[{"output_type":"stream","text":["original tree 3a score = 0.15031315240083507 with depth as 20\n","depth three tree 3a score = 0.20876826722338204\n","depth six tree 2a score = 0.2588726513569937\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"DdgwkbmB9in2"},"source":["**with depth six, there is better accuracy than original with much less depth (makes it easier to visualize), so we will go with this tree for df3a**"]},{"cell_type":"markdown","metadata":{"id":"JyfjRPmutbpw"},"source":["## Use Tree to Predict Class Label "]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":227},"id":"k-xzuUFixrbG","executionInfo":{"status":"ok","timestamp":1620768724529,"user_tz":240,"elapsed":9539,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"021a081a-b745-48e1-c701-26f99d3accc0"},"source":["#predict which class\n","\n","\n","y1a_predict= tree1a_entropy_7.predict(x1a_test)\n","y1a_predict_p = tree1a_entropy_7.predict_proba(x1a_test)\n","test_df1a = pd.DataFrame(x1a_test, columns=pokemon1a['feature_names_1a'])\n","test_df1a['type_1']=y1a_test\n","test_df1a['pre']=y1a_predict\n","test_df1a['error']=np.where(test_df1a['type_1']!=test_df1a['pre'],1,0)#mark error cases\n","p1a = pd.DataFrame(y1a_predict_p, index=test_df1a.index, columns = tree1a_entropy_7.classes_)\n","\n","\n","test_df1a=test_df1a.join(p1a)\n","\n","\n","for i in test_df1a.index:\n","    c = test_df1a.loc[i, 'pre']\n","    test_df1a.loc[i,'p_score']=test_df1a.loc[i,c]\n","\n","test_df1a.head()"],"execution_count":520,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>height_m</th>\n","      <th>weight_kg</th>\n","      <th>total_points</th>\n","      <th>hp</th>\n","      <th>attack</th>\n","      <th>defense</th>\n","      <th>sp_attack</th>\n","      <th>sp_defense</th>\n","      <th>speed</th>\n","      <th>catch_rate</th>\n","      <th>base_experience</th>\n","      <th>egg_cycles</th>\n","      <th>status_Legendary</th>\n","      <th>status_Mythical</th>\n","      <th>status_Normal</th>\n","      <th>status_Sub Legendary</th>\n","      <th>egg_type_1_Amorphous</th>\n","      <th>egg_type_1_Bug</th>\n","      <th>egg_type_1_Ditto</th>\n","      <th>egg_type_1_Dragon</th>\n","      <th>egg_type_1_Fairy</th>\n","      <th>egg_type_1_Field</th>\n","      <th>egg_type_1_Flying</th>\n","      <th>egg_type_1_Grass</th>\n","      <th>egg_type_1_Human-Like</th>\n","      <th>egg_type_1_Mineral</th>\n","      <th>egg_type_1_Monster</th>\n","      <th>egg_type_1_Undiscovered</th>\n","      <th>egg_type_1_Water 1</th>\n","      <th>egg_type_1_Water 2</th>\n","      <th>egg_type_1_Water 3</th>\n","      <th>growth_rate_Erratic</th>\n","      <th>growth_rate_Fast</th>\n","      <th>growth_rate_Fluctuating</th>\n","      <th>growth_rate_Medium Fast</th>\n","      <th>growth_rate_Medium Slow</th>\n","      <th>growth_rate_Slow</th>\n","      <th>type_1</th>\n","      <th>pre</th>\n","      <th>error</th>\n","      <th>Bug</th>\n","      <th>Dark</th>\n","      <th>Dragon</th>\n","      <th>Electric</th>\n","      <th>Fairy</th>\n","      <th>Fighting</th>\n","      <th>Fire</th>\n","      <th>Flying</th>\n","      <th>Ghost</th>\n","      <th>Grass</th>\n","      <th>Ground</th>\n","      <th>Ice</th>\n","      <th>Normal</th>\n","      <th>Poison</th>\n","      <th>Psychic</th>\n","      <th>Rock</th>\n","      <th>Steel</th>\n","      <th>Water</th>\n","      <th>p_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>40.5</td>\n","      <td>360.0</td>\n","      <td>84.0</td>\n","      <td>71.0</td>\n","      <td>43.0</td>\n","      <td>71.0</td>\n","      <td>43.0</td>\n","      <td>48.0</td>\n","      <td>120.0</td>\n","      <td>126.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>Normal</td>\n","      <td>Fire</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.050633</td>\n","      <td>0.000000</td>\n","      <td>0.075949</td>\n","      <td>0.000000</td>\n","      <td>0.012658</td>\n","      <td>0.303797</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.050633</td>\n","      <td>0.012658</td>\n","      <td>0.063291</td>\n","      <td>0.101266</td>\n","      <td>0.012658</td>\n","      <td>0.113924</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.202532</td>\n","      <td>0.303797</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.4</td>\n","      <td>53.6</td>\n","      <td>680.0</td>\n","      <td>100.0</td>\n","      <td>85.0</td>\n","      <td>80.0</td>\n","      <td>165.0</td>\n","      <td>100.0</td>\n","      <td>150.0</td>\n","      <td>3.0</td>\n","      <td>340.0</td>\n","      <td>120.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Psychic</td>\n","      <td>Psychic</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.105263</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.052632</td>\n","      <td>0.105263</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.052632</td>\n","      <td>0.000000</td>\n","      <td>0.421053</td>\n","      <td>0.052632</td>\n","      <td>0.105263</td>\n","      <td>0.105263</td>\n","      <td>0.421053</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2.3</td>\n","      <td>150.0</td>\n","      <td>505.0</td>\n","      <td>90.0</td>\n","      <td>100.0</td>\n","      <td>90.0</td>\n","      <td>90.0</td>\n","      <td>80.0</td>\n","      <td>55.0</td>\n","      <td>45.0</td>\n","      <td>177.0</td>\n","      <td>35.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Electric</td>\n","      <td>Water</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.076923</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.384615</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.461538</td>\n","      <td>0.461538</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.9</td>\n","      <td>205.0</td>\n","      <td>580.0</td>\n","      <td>80.0</td>\n","      <td>75.0</td>\n","      <td>150.0</td>\n","      <td>75.0</td>\n","      <td>150.0</td>\n","      <td>50.0</td>\n","      <td>3.0</td>\n","      <td>261.0</td>\n","      <td>80.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Steel</td>\n","      <td>Steel</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.153846</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.230769</td>\n","      <td>0.538462</td>\n","      <td>0.000000</td>\n","      <td>0.538462</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.4</td>\n","      <td>1.2</td>\n","      <td>348.0</td>\n","      <td>90.0</td>\n","      <td>50.0</td>\n","      <td>34.0</td>\n","      <td>60.0</td>\n","      <td>44.0</td>\n","      <td>70.0</td>\n","      <td>125.0</td>\n","      <td>70.0</td>\n","      <td>30.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Ghost</td>\n","      <td>Fire</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.200000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.000000</td>\n","      <td>0.200000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   height_m  weight_kg  total_points  ...     Steel     Water   p_score\n","0       1.0       40.5         360.0  ...  0.000000  0.202532  0.303797\n","1       2.4       53.6         680.0  ...  0.105263  0.105263  0.421053\n","2       2.3      150.0         505.0  ...  0.000000  0.461538  0.461538\n","3       1.9      205.0         580.0  ...  0.538462  0.000000  0.538462\n","4       0.4        1.2         348.0  ...  0.000000  0.200000  0.200000\n","\n","[5 rows x 59 columns]"]},"metadata":{"tags":[]},"execution_count":520}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RAdm-tl-ZasF","executionInfo":{"status":"ok","timestamp":1620768724530,"user_tz":240,"elapsed":9530,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"c5f09820-eec8-4573-aaf4-5d57e137b0e8"},"source":["#accuracy tests\n","from sklearn.metrics import confusion_matrix\n","confusion_matrix(y1a_test, y1a_predict)"],"execution_count":521,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[16,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","         0,  0],\n","       [ 0,  0,  1,  0,  0,  0,  3,  0,  0,  0,  0,  0,  3,  0,  0,  0,\n","         0,  2],\n","       [ 0,  0,  7,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  0,\n","         0,  0],\n","       [ 0,  0,  0,  1,  1,  0,  5,  0,  0,  0,  0,  0,  3,  0,  0,  0,\n","         1,  2],\n","       [ 0,  0,  0,  0,  2,  1,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0,\n","         0,  0],\n","       [ 0,  0,  0,  0,  0,  2,  0,  0,  0,  0,  0,  0,  1,  0,  0,  1,\n","         1,  4],\n","       [ 0,  0,  1,  1,  0,  0,  5,  0,  0,  0,  0,  0,  2,  0,  1,  0,\n","         0,  3],\n","       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","         0,  2],\n","       [ 0,  0,  1,  0,  0,  0,  3,  0,  4,  0,  0,  0,  0,  0,  1,  0,\n","         0,  0],\n","       [ 0,  0,  0,  0,  2,  0,  1,  0,  0, 12,  0,  0,  1,  0,  1,  1,\n","         0,  0],\n","       [ 3,  0,  1,  0,  0,  1,  0,  0,  0,  0,  0,  0,  2,  0,  0,  1,\n","         0,  0],\n","       [ 0,  0,  0,  0,  1,  1,  1,  0,  0,  0,  0,  0,  3,  0,  0,  0,\n","         1,  0],\n","       [ 0,  0,  0,  1,  2,  0,  5,  0,  0,  0,  0,  0,  6,  0,  1,  0,\n","         0,  8],\n","       [ 0,  0,  0,  1,  0,  0,  1,  0,  0,  0,  1,  0,  0,  0,  1,  0,\n","         1,  3],\n","       [ 0,  0,  2,  2,  1,  1,  3,  0,  2,  0,  0,  0,  0,  0,  3,  0,\n","         0,  2],\n","       [ 0,  0,  0,  0,  0,  2,  0,  0,  0,  0,  0,  0,  1,  0,  0,  6,\n","         1,  2],\n","       [ 0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0,  2,  0,  0,  0,\n","         4,  0],\n","       [ 0,  0,  1,  2,  0,  0,  1,  0,  0,  1,  0,  0,  2,  0,  2,  0,\n","         0, 18]])"]},"metadata":{"tags":[]},"execution_count":521}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z2CGgMMQZW-Y","executionInfo":{"status":"ok","timestamp":1620768724530,"user_tz":240,"elapsed":9519,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"502a6a2e-39b8-49ec-cfb2-a1124ed9be5c"},"source":["from sklearn.metrics import precision_score, recall_score, accuracy_score\n","p0 = precision_score(y1a_test, y1a_predict, labels=[0],average='micro')\n","p1 = precision_score(y1a_test,y1a_predict,labels=[1],average='micro')\n","p2 = precision_score(y1a_test,y1a_predict,labels=[2],average='micro')\n","\n","print(f\"class 0 precision = {p0}\\n\\\n","class 1 precision = {p1}\\n\\\n","class 3 precision = {p2}\")"],"execution_count":522,"outputs":[{"output_type":"stream","text":["class 0 precision = 0.0\n","class 1 precision = 0.0\n","class 3 precision = 0.0\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/numpy/lib/arraysetops.py:576: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n","  mask &= (ar1 != a)\n","/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XPMT_ceFZfrp","executionInfo":{"status":"ok","timestamp":1620768724530,"user_tz":240,"elapsed":9506,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"f4dec718-54b2-409c-fdc1-5f96e242a2ae"},"source":["a0= accuracy_score(y1a_test,y1a_predict, normalize=True)\n","\n","print(f\"overall accuracy = {a0}\")"],"execution_count":523,"outputs":[{"output_type":"stream","text":["overall accuracy = 0.41148325358851673\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-p0WTsQyZkk9","executionInfo":{"status":"ok","timestamp":1620768724531,"user_tz":240,"elapsed":9496,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"9df5b6af-3428-4c33-ed32-962aaf1fb05d"},"source":["r0= recall_score(y1a_test,y1a_predict, labels=[0],average='micro')\n","r1 = recall_score(y1a_test,y1a_predict, labels=[1],average='micro')\n","r2 = recall_score(y1a_test, y1a_predict, labels=[2], average='micro')\n","print(f\"class 0 recall = {r0}\\n\\\n","class 1 recall = {r1}\\n\\\n","class 2 recall = {r2}\")"],"execution_count":524,"outputs":[{"output_type":"stream","text":["class 0 recall = 0.0\n","class 1 recall = 0.0\n","class 2 recall = 0.0\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/numpy/lib/arraysetops.py:576: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n","  mask &= (ar1 != a)\n","/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"ctJgcrhCLPtE","colab":{"base_uri":"https://localhost:8080/","height":658},"executionInfo":{"status":"ok","timestamp":1620768724764,"user_tz":240,"elapsed":9718,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"9395c65e-7dc8-45a1-9735-13749ee605e7"},"source":["y2a_predict= tree2a_entropy_8.predict(x2a_test)\n","y2a_predict_p = tree2a_entropy_8.predict_proba(x2a_test)\n","test_df2a = pd.DataFrame(x2a_test, columns=pokemon2a['feature_names_2a'])\n","test_df2a['type_1']=y2a_test\n","test_df2a['pre']=y2a_predict\n","test_df2a['error']=np.where(test_df2a['type_1']!=test_df2a['pre'],1,0)#mark error cases\n","p2a = pd.DataFrame(y2a_predict_p, index=test_df2a.index, columns = tree2a_entropy_8.classes_)\n","\n","test_df2a = test_df2a.join(p2a)\n","\n","for i in test_df2a.index:\n","    c = test_df2a.loc[i, 'pre']\n","    test_df2a.loc[i,'p_score']=test_df2a.loc[i,c]\n","\n","test_df2a.head(20)"],"execution_count":525,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>height_m</th>\n","      <th>weight_kg</th>\n","      <th>total_points</th>\n","      <th>hp</th>\n","      <th>attack</th>\n","      <th>defense</th>\n","      <th>sp_attack</th>\n","      <th>sp_defense</th>\n","      <th>speed</th>\n","      <th>catch_rate</th>\n","      <th>base_experience</th>\n","      <th>egg_cycles</th>\n","      <th>status_Legendary</th>\n","      <th>status_Mythical</th>\n","      <th>status_Normal</th>\n","      <th>status_Sub Legendary</th>\n","      <th>egg_type_1_Amorphous</th>\n","      <th>egg_type_1_Bug</th>\n","      <th>egg_type_1_Ditto</th>\n","      <th>egg_type_1_Dragon</th>\n","      <th>egg_type_1_Fairy</th>\n","      <th>egg_type_1_Field</th>\n","      <th>egg_type_1_Flying</th>\n","      <th>egg_type_1_Grass</th>\n","      <th>egg_type_1_Human-Like</th>\n","      <th>egg_type_1_Mineral</th>\n","      <th>egg_type_1_Monster</th>\n","      <th>egg_type_1_Undiscovered</th>\n","      <th>egg_type_1_Water 1</th>\n","      <th>egg_type_1_Water 2</th>\n","      <th>egg_type_1_Water 3</th>\n","      <th>growth_rate_Erratic</th>\n","      <th>growth_rate_Fast</th>\n","      <th>growth_rate_Fluctuating</th>\n","      <th>growth_rate_Medium Fast</th>\n","      <th>growth_rate_Medium Slow</th>\n","      <th>growth_rate_Slow</th>\n","      <th>type_1</th>\n","      <th>pre</th>\n","      <th>error</th>\n","      <th>Bug</th>\n","      <th>Dark</th>\n","      <th>Dragon</th>\n","      <th>Electric</th>\n","      <th>Fairy</th>\n","      <th>Fighting</th>\n","      <th>Fire</th>\n","      <th>Flying</th>\n","      <th>Ghost</th>\n","      <th>Grass</th>\n","      <th>Ground</th>\n","      <th>Ice</th>\n","      <th>Normal</th>\n","      <th>Poison</th>\n","      <th>Psychic</th>\n","      <th>Rock</th>\n","      <th>Steel</th>\n","      <th>Water</th>\n","      <th>p_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.3</td>\n","      <td>7.0</td>\n","      <td>314.0</td>\n","      <td>41.0</td>\n","      <td>56.0</td>\n","      <td>40.0</td>\n","      <td>62.0</td>\n","      <td>44.0</td>\n","      <td>71.0</td>\n","      <td>45.0</td>\n","      <td>63.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>Water</td>\n","      <td>Water</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.071429</td>\n","      <td>0.107143</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.035714</td>\n","      <td>0.053571</td>\n","      <td>0.107143</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.482143</td>\n","      <td>0.482143</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.3</td>\n","      <td>60.0</td>\n","      <td>475.0</td>\n","      <td>55.0</td>\n","      <td>130.0</td>\n","      <td>115.0</td>\n","      <td>50.0</td>\n","      <td>50.0</td>\n","      <td>75.0</td>\n","      <td>60.0</td>\n","      <td>166.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Water</td>\n","      <td>Water</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.071429</td>\n","      <td>0.107143</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.035714</td>\n","      <td>0.053571</td>\n","      <td>0.107143</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.482143</td>\n","      <td>0.482143</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.6</td>\n","      <td>8.0</td>\n","      <td>348.0</td>\n","      <td>67.0</td>\n","      <td>82.0</td>\n","      <td>62.0</td>\n","      <td>46.0</td>\n","      <td>48.0</td>\n","      <td>43.0</td>\n","      <td>220.0</td>\n","      <td>70.0</td>\n","      <td>25.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Fighting</td>\n","      <td>Normal</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.833333</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.166667</td>\n","      <td>0.000000</td>\n","      <td>0.833333</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.2</td>\n","      <td>0.9</td>\n","      <td>371.0</td>\n","      <td>54.0</td>\n","      <td>45.0</td>\n","      <td>47.0</td>\n","      <td>75.0</td>\n","      <td>98.0</td>\n","      <td>52.0</td>\n","      <td>120.0</td>\n","      <td>130.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Fairy</td>\n","      <td>Water</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.071429</td>\n","      <td>0.107143</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.035714</td>\n","      <td>0.053571</td>\n","      <td>0.107143</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.482143</td>\n","      <td>0.482143</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.3</td>\n","      <td>1.0</td>\n","      <td>290.0</td>\n","      <td>45.0</td>\n","      <td>30.0</td>\n","      <td>40.0</td>\n","      <td>105.0</td>\n","      <td>50.0</td>\n","      <td>20.0</td>\n","      <td>200.0</td>\n","      <td>58.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>Psychic</td>\n","      <td>Ghost</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.000000</td>\n","      <td>0.846154</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.846154</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1.1</td>\n","      <td>10.0</td>\n","      <td>552.0</td>\n","      <td>78.0</td>\n","      <td>65.0</td>\n","      <td>68.0</td>\n","      <td>112.0</td>\n","      <td>154.0</td>\n","      <td>75.0</td>\n","      <td>45.0</td>\n","      <td>248.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Fairy</td>\n","      <td>Water</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.071429</td>\n","      <td>0.107143</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.035714</td>\n","      <td>0.053571</td>\n","      <td>0.107143</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.482143</td>\n","      <td>0.482143</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.7</td>\n","      <td>18.0</td>\n","      <td>390.0</td>\n","      <td>60.0</td>\n","      <td>45.0</td>\n","      <td>70.0</td>\n","      <td>75.0</td>\n","      <td>85.0</td>\n","      <td>55.0</td>\n","      <td>100.0</td>\n","      <td>137.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>Psychic</td>\n","      <td>Psychic</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.250000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.125000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.625000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.625000</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.6</td>\n","      <td>12.5</td>\n","      <td>305.0</td>\n","      <td>75.0</td>\n","      <td>80.0</td>\n","      <td>55.0</td>\n","      <td>25.0</td>\n","      <td>35.0</td>\n","      <td>35.0</td>\n","      <td>180.0</td>\n","      <td>61.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>Fighting</td>\n","      <td>Fighting</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.600000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.400000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.600000</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.5</td>\n","      <td>33.5</td>\n","      <td>330.0</td>\n","      <td>90.0</td>\n","      <td>60.0</td>\n","      <td>60.0</td>\n","      <td>40.0</td>\n","      <td>40.0</td>\n","      <td>40.0</td>\n","      <td>120.0</td>\n","      <td>66.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Ground</td>\n","      <td>Fire</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.333333</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.166667</td>\n","      <td>0.166667</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.333333</td>\n","      <td>0.333333</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0.2</td>\n","      <td>0.3</td>\n","      <td>275.0</td>\n","      <td>35.0</td>\n","      <td>55.0</td>\n","      <td>40.0</td>\n","      <td>45.0</td>\n","      <td>40.0</td>\n","      <td>60.0</td>\n","      <td>190.0</td>\n","      <td>55.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Electric</td>\n","      <td>Ghost</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.000000</td>\n","      <td>0.846154</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.846154</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>3.8</td>\n","      <td>65.5</td>\n","      <td>510.0</td>\n","      <td>72.0</td>\n","      <td>107.0</td>\n","      <td>125.0</td>\n","      <td>65.0</td>\n","      <td>70.0</td>\n","      <td>71.0</td>\n","      <td>120.0</td>\n","      <td>179.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Ground</td>\n","      <td>Water</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.111111</td>\n","      <td>0.111111</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.222222</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.555556</td>\n","      <td>0.555556</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>0.7</td>\n","      <td>14.0</td>\n","      <td>420.0</td>\n","      <td>70.0</td>\n","      <td>85.0</td>\n","      <td>70.0</td>\n","      <td>55.0</td>\n","      <td>60.0</td>\n","      <td>80.0</td>\n","      <td>45.0</td>\n","      <td>147.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>Grass</td>\n","      <td>Grass</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.800000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.200000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.800000</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>0.1</td>\n","      <td>0.1</td>\n","      <td>303.0</td>\n","      <td>44.0</td>\n","      <td>38.0</td>\n","      <td>39.0</td>\n","      <td>61.0</td>\n","      <td>79.0</td>\n","      <td>42.0</td>\n","      <td>225.0</td>\n","      <td>61.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Fairy</td>\n","      <td>Water</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.071429</td>\n","      <td>0.107143</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.035714</td>\n","      <td>0.053571</td>\n","      <td>0.107143</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.482143</td>\n","      <td>0.482143</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>0.8</td>\n","      <td>24.5</td>\n","      <td>525.0</td>\n","      <td>65.0</td>\n","      <td>65.0</td>\n","      <td>60.0</td>\n","      <td>110.0</td>\n","      <td>95.0</td>\n","      <td>130.0</td>\n","      <td>45.0</td>\n","      <td>184.0</td>\n","      <td>35.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Electric</td>\n","      <td>Dark</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.333333</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.166667</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.166667</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.333333</td>\n","      <td>0.333333</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>0.4</td>\n","      <td>7.6</td>\n","      <td>310.0</td>\n","      <td>50.0</td>\n","      <td>70.0</td>\n","      <td>50.0</td>\n","      <td>50.0</td>\n","      <td>50.0</td>\n","      <td>40.0</td>\n","      <td>45.0</td>\n","      <td>62.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>Water</td>\n","      <td>Water</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.071429</td>\n","      <td>0.107143</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.035714</td>\n","      <td>0.053571</td>\n","      <td>0.107143</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.482143</td>\n","      <td>0.482143</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>1.1</td>\n","      <td>19.0</td>\n","      <td>405.0</td>\n","      <td>58.0</td>\n","      <td>64.0</td>\n","      <td>58.0</td>\n","      <td>80.0</td>\n","      <td>65.0</td>\n","      <td>80.0</td>\n","      <td>45.0</td>\n","      <td>142.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>Fire</td>\n","      <td>Water</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.111111</td>\n","      <td>0.111111</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.222222</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.555556</td>\n","      <td>0.555556</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>1.7</td>\n","      <td>60.8</td>\n","      <td>600.0</td>\n","      <td>50.0</td>\n","      <td>70.0</td>\n","      <td>160.0</td>\n","      <td>70.0</td>\n","      <td>160.0</td>\n","      <td>90.0</td>\n","      <td>3.0</td>\n","      <td>270.0</td>\n","      <td>120.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Psychic</td>\n","      <td>Psychic</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.027027</td>\n","      <td>0.0</td>\n","      <td>0.135135</td>\n","      <td>0.081081</td>\n","      <td>0.054054</td>\n","      <td>0.054054</td>\n","      <td>0.027027</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.027027</td>\n","      <td>0.027027</td>\n","      <td>0.135135</td>\n","      <td>0.054054</td>\n","      <td>0.297297</td>\n","      <td>0.000000</td>\n","      <td>0.027027</td>\n","      <td>0.054054</td>\n","      <td>0.297297</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>0.6</td>\n","      <td>1.0</td>\n","      <td>340.0</td>\n","      <td>40.0</td>\n","      <td>65.0</td>\n","      <td>95.0</td>\n","      <td>60.0</td>\n","      <td>45.0</td>\n","      <td>35.0</td>\n","      <td>190.0</td>\n","      <td>68.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Poison</td>\n","      <td>Ghost</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.000000</td>\n","      <td>0.846154</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.846154</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>0.5</td>\n","      <td>1.0</td>\n","      <td>280.0</td>\n","      <td>41.0</td>\n","      <td>63.0</td>\n","      <td>40.0</td>\n","      <td>40.0</td>\n","      <td>30.0</td>\n","      <td>66.0</td>\n","      <td>255.0</td>\n","      <td>56.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Water</td>\n","      <td>Water</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.071429</td>\n","      <td>0.107143</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.035714</td>\n","      <td>0.053571</td>\n","      <td>0.107143</td>\n","      <td>0.000000</td>\n","      <td>0.035714</td>\n","      <td>0.017857</td>\n","      <td>0.000000</td>\n","      <td>0.482143</td>\n","      <td>0.482143</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>1.2</td>\n","      <td>30.0</td>\n","      <td>500.0</td>\n","      <td>105.0</td>\n","      <td>105.0</td>\n","      <td>75.0</td>\n","      <td>65.0</td>\n","      <td>100.0</td>\n","      <td>50.0</td>\n","      <td>75.0</td>\n","      <td>175.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Poison</td>\n","      <td>Psychic</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.153846</td>\n","      <td>0.076923</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.076923</td>\n","      <td>0.153846</td>\n","      <td>0.307692</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.307692</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    height_m  weight_kg  total_points  ...     Steel     Water   p_score\n","0        0.3        7.0         314.0  ...  0.000000  0.482143  0.482143\n","1        1.3       60.0         475.0  ...  0.000000  0.482143  0.482143\n","2        0.6        8.0         348.0  ...  0.166667  0.000000  0.833333\n","3        0.2        0.9         371.0  ...  0.000000  0.482143  0.482143\n","4        0.3        1.0         290.0  ...  0.000000  0.000000  0.846154\n","5        1.1       10.0         552.0  ...  0.000000  0.482143  0.482143\n","6        0.7       18.0         390.0  ...  0.000000  0.000000  0.625000\n","7        0.6       12.5         305.0  ...  0.000000  0.000000  0.600000\n","8        0.5       33.5         330.0  ...  0.000000  0.333333  0.333333\n","9        0.2        0.3         275.0  ...  0.000000  0.000000  0.846154\n","10       3.8       65.5         510.0  ...  0.000000  0.555556  0.555556\n","11       0.7       14.0         420.0  ...  0.000000  0.000000  0.800000\n","12       0.1        0.1         303.0  ...  0.000000  0.482143  0.482143\n","13       0.8       24.5         525.0  ...  0.000000  0.333333  0.333333\n","14       0.4        7.6         310.0  ...  0.000000  0.482143  0.482143\n","15       1.1       19.0         405.0  ...  0.000000  0.555556  0.555556\n","16       1.7       60.8         600.0  ...  0.027027  0.054054  0.297297\n","17       0.6        1.0         340.0  ...  0.000000  0.000000  0.846154\n","18       0.5        1.0         280.0  ...  0.000000  0.482143  0.482143\n","19       1.2       30.0         500.0  ...  0.000000  0.076923  0.307692\n","\n","[20 rows x 59 columns]"]},"metadata":{"tags":[]},"execution_count":525}]},{"cell_type":"code","metadata":{"id":"A2M0NB-yRtob","colab":{"base_uri":"https://localhost:8080/","height":227},"executionInfo":{"status":"ok","timestamp":1620768725137,"user_tz":240,"elapsed":10081,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"d5b55e64-069f-46dd-ff6f-d19caec346fb"},"source":["y3a_predict= tree3a_entropy_6.predict(x3a_test)\n","y3a_predict_p = tree3a_entropy_6.predict_proba(x3a_test)\n","test_df3a = pd.DataFrame(x3a_test, columns=pokemon3a['feature_names_3a'])\n","test_df3a['type_1']=y3a_test\n","test_df3a['pre']=y3a_predict\n","test_df3a['error']=np.where(test_df3a['type_1']!=test_df3a['pre'],1,0)#mark error cases\n","p3a = pd.DataFrame(y3a_predict_p, index=test_df3a.index, columns = tree3a_entropy_6.classes_)\n","\n","test_df3a = test_df3a.join(p3a)\n","\n","for i in test_df3a.index:\n","    c = test_df3a.loc[i, 'pre']\n","    test_df3a.loc[i,'p_score']=test_df3a.loc[i,c]\n","\n","test_df3a.head()"],"execution_count":526,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>height_m</th>\n","      <th>weight_kg</th>\n","      <th>total_points</th>\n","      <th>hp</th>\n","      <th>attack</th>\n","      <th>defense</th>\n","      <th>sp_attack</th>\n","      <th>sp_defense</th>\n","      <th>speed</th>\n","      <th>catch_rate</th>\n","      <th>base_experience</th>\n","      <th>egg_cycles</th>\n","      <th>status_Legendary</th>\n","      <th>status_Mythical</th>\n","      <th>status_Normal</th>\n","      <th>status_Sub Legendary</th>\n","      <th>egg_type_1_Amorphous</th>\n","      <th>egg_type_1_Bug</th>\n","      <th>egg_type_1_Ditto</th>\n","      <th>egg_type_1_Dragon</th>\n","      <th>egg_type_1_Fairy</th>\n","      <th>egg_type_1_Field</th>\n","      <th>egg_type_1_Flying</th>\n","      <th>egg_type_1_Grass</th>\n","      <th>egg_type_1_Human-Like</th>\n","      <th>egg_type_1_Mineral</th>\n","      <th>egg_type_1_Monster</th>\n","      <th>egg_type_1_Undiscovered</th>\n","      <th>egg_type_1_Water 1</th>\n","      <th>egg_type_1_Water 2</th>\n","      <th>egg_type_1_Water 3</th>\n","      <th>growth_rate_Erratic</th>\n","      <th>growth_rate_Fast</th>\n","      <th>growth_rate_Fluctuating</th>\n","      <th>growth_rate_Medium Fast</th>\n","      <th>growth_rate_Medium Slow</th>\n","      <th>growth_rate_Slow</th>\n","      <th>type_1</th>\n","      <th>pre</th>\n","      <th>error</th>\n","      <th>Bug</th>\n","      <th>Dark</th>\n","      <th>Dragon</th>\n","      <th>Electric</th>\n","      <th>Fairy</th>\n","      <th>Fighting</th>\n","      <th>Fire</th>\n","      <th>Flying</th>\n","      <th>Ghost</th>\n","      <th>Grass</th>\n","      <th>Ground</th>\n","      <th>Ice</th>\n","      <th>Normal</th>\n","      <th>Poison</th>\n","      <th>Psychic</th>\n","      <th>Rock</th>\n","      <th>Steel</th>\n","      <th>Water</th>\n","      <th>p_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>10.8</td>\n","      <td>265.0</td>\n","      <td>40.0</td>\n","      <td>20.0</td>\n","      <td>30.0</td>\n","      <td>40.0</td>\n","      <td>80.0</td>\n","      <td>55.0</td>\n","      <td>255.0</td>\n","      <td>53.0</td>\n","      <td>15.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Bug</td>\n","      <td>Poison</td>\n","      <td>1</td>\n","      <td>0.285714</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.142857</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.571429</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.571429</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.5</td>\n","      <td>920.0</td>\n","      <td>500.0</td>\n","      <td>100.0</td>\n","      <td>125.0</td>\n","      <td>100.0</td>\n","      <td>55.0</td>\n","      <td>85.0</td>\n","      <td>35.0</td>\n","      <td>60.0</td>\n","      <td>175.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Ground</td>\n","      <td>Ground</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.400000</td>\n","      <td>0.200000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.300000</td>\n","      <td>0.000000</td>\n","      <td>0.100000</td>\n","      <td>0.400000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>35.0</td>\n","      <td>495.0</td>\n","      <td>70.0</td>\n","      <td>60.0</td>\n","      <td>125.0</td>\n","      <td>115.0</td>\n","      <td>70.0</td>\n","      <td>55.0</td>\n","      <td>45.0</td>\n","      <td>173.0</td>\n","      <td>30.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Water</td>\n","      <td>Psychic</td>\n","      <td>1</td>\n","      <td>0.020833</td>\n","      <td>0.036458</td>\n","      <td>0.098958</td>\n","      <td>0.020833</td>\n","      <td>0.020833</td>\n","      <td>0.036458</td>\n","      <td>0.026042</td>\n","      <td>0.078125</td>\n","      <td>0.052083</td>\n","      <td>0.041667</td>\n","      <td>0.036458</td>\n","      <td>0.041667</td>\n","      <td>0.057292</td>\n","      <td>0.010417</td>\n","      <td>0.145833</td>\n","      <td>0.109375</td>\n","      <td>0.093750</td>\n","      <td>0.072917</td>\n","      <td>0.145833</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.1</td>\n","      <td>110.5</td>\n","      <td>420.0</td>\n","      <td>65.0</td>\n","      <td>95.0</td>\n","      <td>100.0</td>\n","      <td>60.0</td>\n","      <td>50.0</td>\n","      <td>50.0</td>\n","      <td>45.0</td>\n","      <td>147.0</td>\n","      <td>40.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Dragon</td>\n","      <td>Dragon</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.653846</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.038462</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.115385</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.076923</td>\n","      <td>0.038462</td>\n","      <td>0.000000</td>\n","      <td>0.653846</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.6</td>\n","      <td>11.2</td>\n","      <td>280.0</td>\n","      <td>30.0</td>\n","      <td>40.0</td>\n","      <td>55.0</td>\n","      <td>40.0</td>\n","      <td>55.0</td>\n","      <td>60.0</td>\n","      <td>180.0</td>\n","      <td>56.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Fighting</td>\n","      <td>Water</td>\n","      <td>1</td>\n","      <td>0.002710</td>\n","      <td>0.040650</td>\n","      <td>0.000000</td>\n","      <td>0.084011</td>\n","      <td>0.092141</td>\n","      <td>0.067751</td>\n","      <td>0.029810</td>\n","      <td>0.018970</td>\n","      <td>0.100271</td>\n","      <td>0.043360</td>\n","      <td>0.046070</td>\n","      <td>0.029810</td>\n","      <td>0.032520</td>\n","      <td>0.065041</td>\n","      <td>0.102981</td>\n","      <td>0.048780</td>\n","      <td>0.046070</td>\n","      <td>0.149051</td>\n","      <td>0.149051</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   height_m  weight_kg  total_points  ...     Steel     Water   p_score\n","0       1.0       10.8         265.0  ...  0.000000  0.000000  0.571429\n","1       2.5      920.0         500.0  ...  0.000000  0.100000  0.400000\n","2       1.0       35.0         495.0  ...  0.093750  0.072917  0.145833\n","3       1.1      110.5         420.0  ...  0.038462  0.000000  0.653846\n","4       0.6       11.2         280.0  ...  0.046070  0.149051  0.149051\n","\n","[5 rows x 59 columns]"]},"metadata":{"tags":[]},"execution_count":526}]},{"cell_type":"markdown","metadata":{"id":"k_af18-Uy4Cw"},"source":["# Build with 10 fold cross validation "]},{"cell_type":"code","metadata":{"id":"sc4LbsgdzB9o","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768725300,"user_tz":240,"elapsed":10234,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"e8df9bbb-c91e-4892-98f7-d54553fceafc"},"source":["#perform cross validation\n","tree1a_entropy_cross = DecisionTreeClassifier(max_depth=3, min_samples_leaf=5, criterion='entropy')\n","scores = cross_val_score(tree1a_entropy_cross, pokemon1a['data_1a'], pokemon1a['target_1a'], cv=10, scoring='accuracy')\n","scores"],"execution_count":527,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:667: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.\n","  % (min_groups, self.n_splits)), UserWarning)\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["array([0.24761905, 0.25714286, 0.25714286, 0.2952381 , 0.25      ,\n","       0.22115385, 0.29807692, 0.23076923, 0.27884615, 0.21153846])"]},"metadata":{"tags":[]},"execution_count":527}]},{"cell_type":"code","metadata":{"id":"S-YRMjk6liYV","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768725301,"user_tz":240,"elapsed":10225,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"1d196aa9-dfb4-4092-e55d-ac21ce593938"},"source":["#mean and 95% confidence level\n","print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"],"execution_count":528,"outputs":[{"output_type":"stream","text":["Accuracy: 0.25 (+/- 0.06)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"l9zYn5krzNkQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768725301,"user_tz":240,"elapsed":10214,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"977f9252-2a50-40fc-a5fc-c5962aa81749"},"source":["#fit model to whole data set and show tree\n","\n","tree1a_entropy_cross.fit(pokemon1a['data_1a'], pokemon1a['target_1a'])"],"execution_count":529,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n","                       max_depth=3, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=5, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":529}]},{"cell_type":"code","metadata":{"id":"FYqsOTl-zZaY","executionInfo":{"status":"ok","timestamp":1620768725302,"user_tz":240,"elapsed":10214,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["dot_data= export_graphviz(tree1a_entropy_cross, out_file=None,\n","                         feature_names = pokemon1a['feature_names_1a'],\n","                         class_names = pokemon1a['target_names_1a'],\n","                         filled=True, rounded=True,\n","                         special_characters=True)\n","graph_1a_cross = graphviz.Source(dot_data)\n","#graph.render('Pokemon_entropy_cross')\n","#graph"],"execution_count":530,"outputs":[]},{"cell_type":"code","metadata":{"id":"p9yQDeG8zmFa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620768725302,"user_tz":240,"elapsed":10204,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"60a068ce-d595-482a-9c5f-a9e4395c589a"},"source":["#score\n","tree1a_entropy_cross.score(x1a_test, y1a_test)"],"execution_count":531,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.31100478468899523"]},"metadata":{"tags":[]},"execution_count":531}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1QtST24YbOMh","executionInfo":{"status":"ok","timestamp":1620768725431,"user_tz":240,"elapsed":10323,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"b96175f2-fe48-4148-d25e-8e2952d6bb6a"},"source":["#prediction\n","y1a_train_pred = cross_val_predict(tree1a_entropy_cross, pokemon1a['data_1a'], pokemon1a['target_1a'], cv=10)\n"],"execution_count":532,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:667: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.\n","  % (min_groups, self.n_splits)), UserWarning)\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dlinmBa9bgX7","executionInfo":{"status":"ok","timestamp":1620768725432,"user_tz":240,"elapsed":10314,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"6d57398a-960b-4e2f-8931-10b751b75cd8"},"source":["p0 = precision_score(pokemon1a['target_1a'], y1a_train_pred, labels=[0],average='micro')\n","p1 = precision_score(pokemon1a['target_1a'], y1a_train_pred,labels=[1],average='micro')\n","p2 = precision_score(pokemon1a['target_1a'], y1a_train_pred,labels=[2],average='micro', zero_division='warn')\n","\n","print(f\"class 0 precision = {p0}\\n\\\n","class 1 precision = {p1}\\n\\\n","class 3 precision = {p2}\")"],"execution_count":533,"outputs":[{"output_type":"stream","text":["class 0 precision = 0.0\n","class 1 precision = 0.0\n","class 3 precision = 0.0\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/numpy/lib/arraysetops.py:576: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n","  mask &= (ar1 != a)\n","/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"c-PRUVz90Ey1"},"source":["# Build with Gini Tree"]},{"cell_type":"markdown","metadata":{"id":"Ck1X7hbU_yHG"},"source":["build for df1"]},{"cell_type":"code","metadata":{"id":"2v6sh8whzwJh","executionInfo":{"status":"ok","timestamp":1620768725756,"user_tz":240,"elapsed":10636,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#Gini function is used. \n","tree1a_gini = DecisionTreeClassifier() #default criterion is gini\n","tree1a_gini.fit(pokemon1a['data_1a'], pokemon1a['target_1a'])\n","\n","dot_data = export_graphviz(tree1a_gini, out_file=None, \n","                          feature_names = pokemon1a['feature_names_1a'],\n","                         class_names = pokemon1a['target_names_1a'],\n","                          filled=True)\n","graph = graphviz.Source(dot_data)\n","#graph"],"execution_count":534,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"j32dNENN0Z2k","executionInfo":{"status":"ok","timestamp":1620768725757,"user_tz":240,"elapsed":10628,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"bc480646-9668-4cfa-ed58-e4a50647e712"},"source":["#evaluate tree performance with test dataset - mean accuracy\n","tree1a_gini.score(x1a_test, y1a_test)"],"execution_count":535,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{"tags":[]},"execution_count":535}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":227},"id":"JvQnJNtMS5Em","executionInfo":{"status":"ok","timestamp":1620768725910,"user_tz":240,"elapsed":10771,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"9b830b55-7310-46dd-fc00-7ef71ade37a0"},"source":["#predict class label\n","\n","y1a_gini_predict= tree1a_gini.predict(x1a_test)\n","y1a_predict_gini_p = tree1a_gini.predict_proba(x1a_test)\n","test_df1a_gini = pd.DataFrame(x1a_test, columns=pokemon1a['feature_names_1a'])\n","test_df1a_gini['class']=y1a_test\n","test_df1a_gini['pre']=y1a_predict\n","test_df1a_gini['error']=np.where(test_df1a_gini['class']!=test_df1a_gini['pre'],1,0)\n","p1a_gini = pd.DataFrame(y1a_predict_gini_p, index=test_df1a_gini.index, columns = tree1a_entropy.classes_)\n","\n","test_df1a_gini=test_df1a_gini.join(p1a_gini)\n","\n","for i in test_df1a_gini.index:\n","    c = test_df1a_gini.loc[i, 'pre']\n","    test_df1a_gini.loc[i,'p_score']=test_df1a_gini.loc[i,c]\n","\n","test_df1a_gini.head()"],"execution_count":536,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>height_m</th>\n","      <th>weight_kg</th>\n","      <th>total_points</th>\n","      <th>hp</th>\n","      <th>attack</th>\n","      <th>defense</th>\n","      <th>sp_attack</th>\n","      <th>sp_defense</th>\n","      <th>speed</th>\n","      <th>catch_rate</th>\n","      <th>base_experience</th>\n","      <th>egg_cycles</th>\n","      <th>status_Legendary</th>\n","      <th>status_Mythical</th>\n","      <th>status_Normal</th>\n","      <th>status_Sub Legendary</th>\n","      <th>egg_type_1_Amorphous</th>\n","      <th>egg_type_1_Bug</th>\n","      <th>egg_type_1_Ditto</th>\n","      <th>egg_type_1_Dragon</th>\n","      <th>egg_type_1_Fairy</th>\n","      <th>egg_type_1_Field</th>\n","      <th>egg_type_1_Flying</th>\n","      <th>egg_type_1_Grass</th>\n","      <th>egg_type_1_Human-Like</th>\n","      <th>egg_type_1_Mineral</th>\n","      <th>egg_type_1_Monster</th>\n","      <th>egg_type_1_Undiscovered</th>\n","      <th>egg_type_1_Water 1</th>\n","      <th>egg_type_1_Water 2</th>\n","      <th>egg_type_1_Water 3</th>\n","      <th>growth_rate_Erratic</th>\n","      <th>growth_rate_Fast</th>\n","      <th>growth_rate_Fluctuating</th>\n","      <th>growth_rate_Medium Fast</th>\n","      <th>growth_rate_Medium Slow</th>\n","      <th>growth_rate_Slow</th>\n","      <th>class</th>\n","      <th>pre</th>\n","      <th>error</th>\n","      <th>Bug</th>\n","      <th>Dark</th>\n","      <th>Dragon</th>\n","      <th>Electric</th>\n","      <th>Fairy</th>\n","      <th>Fighting</th>\n","      <th>Fire</th>\n","      <th>Flying</th>\n","      <th>Ghost</th>\n","      <th>Grass</th>\n","      <th>Ground</th>\n","      <th>Ice</th>\n","      <th>Normal</th>\n","      <th>Poison</th>\n","      <th>Psychic</th>\n","      <th>Rock</th>\n","      <th>Steel</th>\n","      <th>Water</th>\n","      <th>p_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>40.5</td>\n","      <td>360.0</td>\n","      <td>84.0</td>\n","      <td>71.0</td>\n","      <td>43.0</td>\n","      <td>71.0</td>\n","      <td>43.0</td>\n","      <td>48.0</td>\n","      <td>120.0</td>\n","      <td>126.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>Normal</td>\n","      <td>Fire</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.4</td>\n","      <td>53.6</td>\n","      <td>680.0</td>\n","      <td>100.0</td>\n","      <td>85.0</td>\n","      <td>80.0</td>\n","      <td>165.0</td>\n","      <td>100.0</td>\n","      <td>150.0</td>\n","      <td>3.0</td>\n","      <td>340.0</td>\n","      <td>120.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Psychic</td>\n","      <td>Psychic</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2.3</td>\n","      <td>150.0</td>\n","      <td>505.0</td>\n","      <td>90.0</td>\n","      <td>100.0</td>\n","      <td>90.0</td>\n","      <td>90.0</td>\n","      <td>80.0</td>\n","      <td>55.0</td>\n","      <td>45.0</td>\n","      <td>177.0</td>\n","      <td>35.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Electric</td>\n","      <td>Water</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.9</td>\n","      <td>205.0</td>\n","      <td>580.0</td>\n","      <td>80.0</td>\n","      <td>75.0</td>\n","      <td>150.0</td>\n","      <td>75.0</td>\n","      <td>150.0</td>\n","      <td>50.0</td>\n","      <td>3.0</td>\n","      <td>261.0</td>\n","      <td>80.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Steel</td>\n","      <td>Steel</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.4</td>\n","      <td>1.2</td>\n","      <td>348.0</td>\n","      <td>90.0</td>\n","      <td>50.0</td>\n","      <td>34.0</td>\n","      <td>60.0</td>\n","      <td>44.0</td>\n","      <td>70.0</td>\n","      <td>125.0</td>\n","      <td>70.0</td>\n","      <td>30.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Ghost</td>\n","      <td>Fire</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   height_m  weight_kg  total_points     hp  ...  Rock  Steel  Water  p_score\n","0       1.0       40.5         360.0   84.0  ...   0.0    0.0    0.0      0.0\n","1       2.4       53.6         680.0  100.0  ...   0.0    0.0    0.0      1.0\n","2       2.3      150.0         505.0   90.0  ...   0.0    0.0    0.0      0.0\n","3       1.9      205.0         580.0   80.0  ...   0.0    1.0    0.0      1.0\n","4       0.4        1.2         348.0   90.0  ...   0.0    0.0    0.0      0.0\n","\n","[5 rows x 59 columns]"]},"metadata":{"tags":[]},"execution_count":536}]},{"cell_type":"markdown","metadata":{"id":"0ujB0ZJCAl1a"},"source":["build for df2"]},{"cell_type":"code","metadata":{"id":"SO7gqj9uQdrX","executionInfo":{"status":"ok","timestamp":1620768725910,"user_tz":240,"elapsed":10769,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#Gini function is used. \n","tree2a_gini = DecisionTreeClassifier(max_depth=7, min_samples_leaf=5) #default criterion is gini\n","tree2a_gini.fit(pokemon2a['data_2a'], pokemon2a['target_2a'])\n","\n","dot_data = export_graphviz(tree2a_gini, out_file=None, \n","                          feature_names = pokemon2a['feature_names_2a'],\n","                         class_names = pokemon2a['target_names_2a'],\n","                          filled=True)\n","graph = graphviz.Source(dot_data)\n","#graph"],"execution_count":537,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DQ3vacQMQ1Vj","executionInfo":{"status":"ok","timestamp":1620768725911,"user_tz":240,"elapsed":10760,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"a7a92ed6-a6ab-47d2-c010-7b3c7c814fda"},"source":["#evaluate tree performance with test dataset - mean accuracy\n","tree2a_gini.score(x2a_test, y2a_test)"],"execution_count":538,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.3581081081081081"]},"metadata":{"tags":[]},"execution_count":538}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":227},"id":"10p75ufcT7-0","executionInfo":{"status":"ok","timestamp":1620768726165,"user_tz":240,"elapsed":11005,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"bd191281-ca51-4f79-efac-d487ca7e970e"},"source":["#predict class label\n","\n","y2a_gini_predict= tree2a_gini.predict(x2a_test)\n","y2a_predict_gini_p = tree2a_gini.predict_proba(x2a_test)\n","test_df2a_gini = pd.DataFrame(x2a_test, columns=pokemon2a['feature_names_2a'])\n","test_df2a_gini['class']=y2a_test\n","test_df2a_gini['pre']=y2a_predict\n","test_df2a_gini['error']=np.where(test_df2a_gini['class']!=test_df2a_gini['pre'],1,0)\n","p2a_gini = pd.DataFrame(y2a_predict_gini_p, index=test_df2a_gini.index, columns = tree2a_entropy.classes_)\n","\n","test_df2a_gini=test_df2a_gini.join(p2a_gini)\n","\n","for i in test_df2a_gini.index:\n","    c = test_df2a_gini.loc[i, 'pre']\n","    test_df2a_gini.loc[i,'p_score']=test_df2a_gini.loc[i,c]\n","\n","test_df2a_gini.head()"],"execution_count":539,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>height_m</th>\n","      <th>weight_kg</th>\n","      <th>total_points</th>\n","      <th>hp</th>\n","      <th>attack</th>\n","      <th>defense</th>\n","      <th>sp_attack</th>\n","      <th>sp_defense</th>\n","      <th>speed</th>\n","      <th>catch_rate</th>\n","      <th>base_experience</th>\n","      <th>egg_cycles</th>\n","      <th>status_Legendary</th>\n","      <th>status_Mythical</th>\n","      <th>status_Normal</th>\n","      <th>status_Sub Legendary</th>\n","      <th>egg_type_1_Amorphous</th>\n","      <th>egg_type_1_Bug</th>\n","      <th>egg_type_1_Ditto</th>\n","      <th>egg_type_1_Dragon</th>\n","      <th>egg_type_1_Fairy</th>\n","      <th>egg_type_1_Field</th>\n","      <th>egg_type_1_Flying</th>\n","      <th>egg_type_1_Grass</th>\n","      <th>egg_type_1_Human-Like</th>\n","      <th>egg_type_1_Mineral</th>\n","      <th>egg_type_1_Monster</th>\n","      <th>egg_type_1_Undiscovered</th>\n","      <th>egg_type_1_Water 1</th>\n","      <th>egg_type_1_Water 2</th>\n","      <th>egg_type_1_Water 3</th>\n","      <th>growth_rate_Erratic</th>\n","      <th>growth_rate_Fast</th>\n","      <th>growth_rate_Fluctuating</th>\n","      <th>growth_rate_Medium Fast</th>\n","      <th>growth_rate_Medium Slow</th>\n","      <th>growth_rate_Slow</th>\n","      <th>class</th>\n","      <th>pre</th>\n","      <th>error</th>\n","      <th>Bug</th>\n","      <th>Dark</th>\n","      <th>Dragon</th>\n","      <th>Electric</th>\n","      <th>Fairy</th>\n","      <th>Fighting</th>\n","      <th>Fire</th>\n","      <th>Flying</th>\n","      <th>Ghost</th>\n","      <th>Grass</th>\n","      <th>Ground</th>\n","      <th>Ice</th>\n","      <th>Normal</th>\n","      <th>Poison</th>\n","      <th>Psychic</th>\n","      <th>Rock</th>\n","      <th>Steel</th>\n","      <th>Water</th>\n","      <th>p_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.3</td>\n","      <td>7.0</td>\n","      <td>314.0</td>\n","      <td>41.0</td>\n","      <td>56.0</td>\n","      <td>40.0</td>\n","      <td>62.0</td>\n","      <td>44.0</td>\n","      <td>71.0</td>\n","      <td>45.0</td>\n","      <td>63.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>Water</td>\n","      <td>Water</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.3</td>\n","      <td>60.0</td>\n","      <td>475.0</td>\n","      <td>55.0</td>\n","      <td>130.0</td>\n","      <td>115.0</td>\n","      <td>50.0</td>\n","      <td>50.0</td>\n","      <td>75.0</td>\n","      <td>60.0</td>\n","      <td>166.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Water</td>\n","      <td>Water</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.005650</td>\n","      <td>0.00565</td>\n","      <td>0.101695</td>\n","      <td>0.096045</td>\n","      <td>0.033898</td>\n","      <td>0.022599</td>\n","      <td>0.022599</td>\n","      <td>0.073446</td>\n","      <td>0.022599</td>\n","      <td>0.016949</td>\n","      <td>0.067797</td>\n","      <td>0.118644</td>\n","      <td>0.056497</td>\n","      <td>0.141243</td>\n","      <td>0.062147</td>\n","      <td>0.039548</td>\n","      <td>0.112994</td>\n","      <td>0.112994</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.6</td>\n","      <td>8.0</td>\n","      <td>348.0</td>\n","      <td>67.0</td>\n","      <td>82.0</td>\n","      <td>62.0</td>\n","      <td>46.0</td>\n","      <td>48.0</td>\n","      <td>43.0</td>\n","      <td>220.0</td>\n","      <td>70.0</td>\n","      <td>25.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Fighting</td>\n","      <td>Normal</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.069892</td>\n","      <td>0.00000</td>\n","      <td>0.075269</td>\n","      <td>0.005376</td>\n","      <td>0.043011</td>\n","      <td>0.139785</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.075269</td>\n","      <td>0.059140</td>\n","      <td>0.043011</td>\n","      <td>0.268817</td>\n","      <td>0.016129</td>\n","      <td>0.048387</td>\n","      <td>0.026882</td>\n","      <td>0.021505</td>\n","      <td>0.107527</td>\n","      <td>0.268817</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.2</td>\n","      <td>0.9</td>\n","      <td>371.0</td>\n","      <td>54.0</td>\n","      <td>45.0</td>\n","      <td>47.0</td>\n","      <td>75.0</td>\n","      <td>98.0</td>\n","      <td>52.0</td>\n","      <td>120.0</td>\n","      <td>130.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Fairy</td>\n","      <td>Water</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.005650</td>\n","      <td>0.00565</td>\n","      <td>0.101695</td>\n","      <td>0.096045</td>\n","      <td>0.033898</td>\n","      <td>0.022599</td>\n","      <td>0.022599</td>\n","      <td>0.073446</td>\n","      <td>0.022599</td>\n","      <td>0.016949</td>\n","      <td>0.067797</td>\n","      <td>0.118644</td>\n","      <td>0.056497</td>\n","      <td>0.141243</td>\n","      <td>0.062147</td>\n","      <td>0.039548</td>\n","      <td>0.112994</td>\n","      <td>0.112994</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.3</td>\n","      <td>1.0</td>\n","      <td>290.0</td>\n","      <td>45.0</td>\n","      <td>30.0</td>\n","      <td>40.0</td>\n","      <td>105.0</td>\n","      <td>50.0</td>\n","      <td>20.0</td>\n","      <td>200.0</td>\n","      <td>58.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>Psychic</td>\n","      <td>Ghost</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.005650</td>\n","      <td>0.00565</td>\n","      <td>0.101695</td>\n","      <td>0.096045</td>\n","      <td>0.033898</td>\n","      <td>0.022599</td>\n","      <td>0.022599</td>\n","      <td>0.073446</td>\n","      <td>0.022599</td>\n","      <td>0.016949</td>\n","      <td>0.067797</td>\n","      <td>0.118644</td>\n","      <td>0.056497</td>\n","      <td>0.141243</td>\n","      <td>0.062147</td>\n","      <td>0.039548</td>\n","      <td>0.112994</td>\n","      <td>0.073446</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   height_m  weight_kg  total_points  ...     Steel     Water   p_score\n","0       0.3        7.0         314.0  ...  0.000000  1.000000  1.000000\n","1       1.3       60.0         475.0  ...  0.039548  0.112994  0.112994\n","2       0.6        8.0         348.0  ...  0.021505  0.107527  0.268817\n","3       0.2        0.9         371.0  ...  0.039548  0.112994  0.112994\n","4       0.3        1.0         290.0  ...  0.039548  0.112994  0.073446\n","\n","[5 rows x 59 columns]"]},"metadata":{"tags":[]},"execution_count":539}]},{"cell_type":"markdown","metadata":{"id":"y9QrX3chAnyX"},"source":["build for df3"]},{"cell_type":"code","metadata":{"id":"_-M7wlVGQpof","executionInfo":{"status":"ok","timestamp":1620768726166,"user_tz":240,"elapsed":11004,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}}},"source":["#Gini function is used. \n","tree3a_gini = DecisionTreeClassifier(max_depth=6, min_samples_leaf=5) #default criterion is gini\n","tree3a_gini.fit(pokemon3a['data_3a'], pokemon3a['target_3a'])\n","\n","dot_data = export_graphviz(tree3a_gini, out_file=None, \n","                          feature_names = pokemon3a['feature_names_3a'],\n","                         class_names = pokemon3a['target_names_3a'],\n","                          filled=True)\n","graph = graphviz.Source(dot_data)\n","#graph"],"execution_count":540,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UcIYeVNSQyQI","executionInfo":{"status":"ok","timestamp":1620768726166,"user_tz":240,"elapsed":10995,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"bb43931e-13bf-412a-da6a-e5e926be65fa"},"source":["#evaluate tree performance with test dataset - mean accuracy\n","tree3a_gini.score(x3a_test, y3a_test)"],"execution_count":541,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.2881002087682672"]},"metadata":{"tags":[]},"execution_count":541}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":227},"id":"wnNerg3JUeoD","executionInfo":{"status":"ok","timestamp":1620768726522,"user_tz":240,"elapsed":11341,"user":{"displayName":"Abigail Fontana","photoUrl":"","userId":"02274675902049585192"}},"outputId":"c20c243d-23a1-41ff-fe7e-506eb8d2b565"},"source":["#predict class label\n","\n","y3a_gini_predict= tree3a_gini.predict(x3a_test)\n","y3a_predict_gini_p = tree3a_gini.predict_proba(x3a_test)\n","test_df3a_gini = pd.DataFrame(x3a_test, columns=pokemon3a['feature_names_3a'])\n","test_df3a_gini['class']=y3a_test\n","test_df3a_gini['pre']=y3a_predict\n","test_df3a_gini['error']=np.where(test_df3a_gini['class']!=test_df3a_gini['pre'],1,0)\n","p3a_gini = pd.DataFrame(y3a_predict_gini_p, index=test_df3a_gini.index, columns = tree3a_entropy.classes_)\n","\n","test_df3a_gini=test_df3a_gini.join(p3a_gini)\n","\n","for i in test_df3a_gini.index:\n","    c = test_df3a_gini.loc[i, 'pre']\n","    test_df3a_gini.loc[i,'p_score']=test_df3a_gini.loc[i,c]\n","\n","test_df3a_gini.head()"],"execution_count":542,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>height_m</th>\n","      <th>weight_kg</th>\n","      <th>total_points</th>\n","      <th>hp</th>\n","      <th>attack</th>\n","      <th>defense</th>\n","      <th>sp_attack</th>\n","      <th>sp_defense</th>\n","      <th>speed</th>\n","      <th>catch_rate</th>\n","      <th>base_experience</th>\n","      <th>egg_cycles</th>\n","      <th>status_Legendary</th>\n","      <th>status_Mythical</th>\n","      <th>status_Normal</th>\n","      <th>status_Sub Legendary</th>\n","      <th>egg_type_1_Amorphous</th>\n","      <th>egg_type_1_Bug</th>\n","      <th>egg_type_1_Ditto</th>\n","      <th>egg_type_1_Dragon</th>\n","      <th>egg_type_1_Fairy</th>\n","      <th>egg_type_1_Field</th>\n","      <th>egg_type_1_Flying</th>\n","      <th>egg_type_1_Grass</th>\n","      <th>egg_type_1_Human-Like</th>\n","      <th>egg_type_1_Mineral</th>\n","      <th>egg_type_1_Monster</th>\n","      <th>egg_type_1_Undiscovered</th>\n","      <th>egg_type_1_Water 1</th>\n","      <th>egg_type_1_Water 2</th>\n","      <th>egg_type_1_Water 3</th>\n","      <th>growth_rate_Erratic</th>\n","      <th>growth_rate_Fast</th>\n","      <th>growth_rate_Fluctuating</th>\n","      <th>growth_rate_Medium Fast</th>\n","      <th>growth_rate_Medium Slow</th>\n","      <th>growth_rate_Slow</th>\n","      <th>class</th>\n","      <th>pre</th>\n","      <th>error</th>\n","      <th>Bug</th>\n","      <th>Dark</th>\n","      <th>Dragon</th>\n","      <th>Electric</th>\n","      <th>Fairy</th>\n","      <th>Fighting</th>\n","      <th>Fire</th>\n","      <th>Flying</th>\n","      <th>Ghost</th>\n","      <th>Grass</th>\n","      <th>Ground</th>\n","      <th>Ice</th>\n","      <th>Normal</th>\n","      <th>Poison</th>\n","      <th>Psychic</th>\n","      <th>Rock</th>\n","      <th>Steel</th>\n","      <th>Water</th>\n","      <th>p_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>10.8</td>\n","      <td>265.0</td>\n","      <td>40.0</td>\n","      <td>20.0</td>\n","      <td>30.0</td>\n","      <td>40.0</td>\n","      <td>80.0</td>\n","      <td>55.0</td>\n","      <td>255.0</td>\n","      <td>53.0</td>\n","      <td>15.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Bug</td>\n","      <td>Poison</td>\n","      <td>1</td>\n","      <td>0.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.5</td>\n","      <td>920.0</td>\n","      <td>500.0</td>\n","      <td>100.0</td>\n","      <td>125.0</td>\n","      <td>100.0</td>\n","      <td>55.0</td>\n","      <td>85.0</td>\n","      <td>35.0</td>\n","      <td>60.0</td>\n","      <td>175.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Ground</td>\n","      <td>Ground</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>0.074713</td>\n","      <td>0.000000</td>\n","      <td>0.040230</td>\n","      <td>0.000000</td>\n","      <td>0.063218</td>\n","      <td>0.103448</td>\n","      <td>0.017241</td>\n","      <td>0.000000</td>\n","      <td>0.080460</td>\n","      <td>0.149425</td>\n","      <td>0.109195</td>\n","      <td>0.178161</td>\n","      <td>0.028736</td>\n","      <td>0.017241</td>\n","      <td>0.017241</td>\n","      <td>0.045977</td>\n","      <td>0.074713</td>\n","      <td>0.149425</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>35.0</td>\n","      <td>495.0</td>\n","      <td>70.0</td>\n","      <td>60.0</td>\n","      <td>125.0</td>\n","      <td>115.0</td>\n","      <td>70.0</td>\n","      <td>55.0</td>\n","      <td>45.0</td>\n","      <td>173.0</td>\n","      <td>30.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Water</td>\n","      <td>Psychic</td>\n","      <td>1</td>\n","      <td>0.000000</td>\n","      <td>0.074074</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.018519</td>\n","      <td>0.000000</td>\n","      <td>0.018519</td>\n","      <td>0.037037</td>\n","      <td>0.000000</td>\n","      <td>0.037037</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.037037</td>\n","      <td>0.037037</td>\n","      <td>0.148148</td>\n","      <td>0.000000</td>\n","      <td>0.592593</td>\n","      <td>0.037037</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.1</td>\n","      <td>110.5</td>\n","      <td>420.0</td>\n","      <td>65.0</td>\n","      <td>95.0</td>\n","      <td>100.0</td>\n","      <td>60.0</td>\n","      <td>50.0</td>\n","      <td>50.0</td>\n","      <td>45.0</td>\n","      <td>147.0</td>\n","      <td>40.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Dragon</td>\n","      <td>Dragon</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>0.071429</td>\n","      <td>0.642857</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.047619</td>\n","      <td>0.000000</td>\n","      <td>0.071429</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.095238</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.047619</td>\n","      <td>0.023810</td>\n","      <td>0.000000</td>\n","      <td>0.642857</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.6</td>\n","      <td>11.2</td>\n","      <td>280.0</td>\n","      <td>30.0</td>\n","      <td>40.0</td>\n","      <td>55.0</td>\n","      <td>40.0</td>\n","      <td>55.0</td>\n","      <td>60.0</td>\n","      <td>180.0</td>\n","      <td>56.0</td>\n","      <td>20.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Fighting</td>\n","      <td>Water</td>\n","      <td>1</td>\n","      <td>0.008021</td>\n","      <td>0.033422</td>\n","      <td>0.037433</td>\n","      <td>0.066845</td>\n","      <td>0.070856</td>\n","      <td>0.058824</td>\n","      <td>0.029412</td>\n","      <td>0.040107</td>\n","      <td>0.085561</td>\n","      <td>0.045455</td>\n","      <td>0.045455</td>\n","      <td>0.040107</td>\n","      <td>0.040107</td>\n","      <td>0.045455</td>\n","      <td>0.127005</td>\n","      <td>0.066845</td>\n","      <td>0.064171</td>\n","      <td>0.094920</td>\n","      <td>0.094920</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   height_m  weight_kg  total_points  ...     Steel     Water   p_score\n","0       1.0       10.8         265.0  ...  0.000000  0.000000  0.000000\n","1       2.5      920.0         500.0  ...  0.045977  0.074713  0.149425\n","2       1.0       35.0         495.0  ...  0.000000  0.592593  0.037037\n","3       1.1      110.5         420.0  ...  0.023810  0.000000  0.642857\n","4       0.6       11.2         280.0  ...  0.064171  0.094920  0.094920\n","\n","[5 rows x 59 columns]"]},"metadata":{"tags":[]},"execution_count":542}]}]}