micro_projects/Decision Tree Classification ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('../datasets/machine_learning_a-z/P14-K-Nearest-Neighbors/K_Nearest_Neighbors/Social_Network_Ads.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:,[2,3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "                       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=False,\n",
       "                       random_state=0, splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True False  True False False  True  True  True  True  True  True  True\n",
      "  True False  True  True  True  True  True False  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True False  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True False  True  True\n",
      "  True  True  True  True  True  True  True  True  True False  True  True\n",
      "  True False  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True]\n"
     ]
    }
   ],
   "source": [
    "print(y_test == y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[62,  6],\n",
       "       [ 3, 29]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_set, y_set = X_train, y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X1, X2 = np.meshgrid(\n",
    "np.arange(start = X_set[:,0].min() -1, stop=X_set[:, 0].max()+1, step = 0.01),\n",
    "np.arange(start = X_set[:,1].min() -1, stop=X_set[:, 1].max()+1, step = 0.01)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wajeeh-ros/.conda/envs/python3/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: The following kwargs were not used by contour: 'figsize'\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(X1 ,X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "            alpha = 0.75, cmap = ListedColormap(('red', 'green')),\n",
    "            figsize=(20,20))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j,0], X_set[y_set == j , 1],\n",
    "               c = ListedColormap(('red','green'))(i), label = j)\n",
    "plt.title('Decision Tree Classifier (Training Set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wajeeh-ros/.conda/envs/python3/lib/python3.7/site-packages/ipykernel_launcher.py:11: UserWarning: The following kwargs were not used by contour: 'figsize'\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "\n",
    "X1, X2 = np.meshgrid(\n",
    "np.arange(start = X_set[:,0].min() -1, stop=X_set[:, 0].max()+1, step = 0.01),\n",
    "np.arange(start = X_set[:,1].min() -1, stop=X_set[:, 1].max()+1, step = 0.01)\n",
    ")\n",
    "\n",
    "plt.contourf(X1 ,X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "            alpha = 0.75, cmap = ListedColormap(('red', 'green')),\n",
    "            figsize=(20,20))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j,0], X_set[y_set == j , 1],\n",
    "               c = ListedColormap(('red','green'))(i), label = j)\n",
    "plt.title('Decision Tree Classifier Test Set (Training Set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}