customerChurn/analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mtl\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "mtl.rcParams['figure.figsize'] = (12, 4)\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/BankCustomers.csv')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber            int64\n",
       "CustomerId           int64\n",
       "Surname             object\n",
       "CreditScore          int64\n",
       "Geography           object\n",
       "Gender              object\n",
       "Age                  int64\n",
       "Tenure               int64\n",
       "Balance            float64\n",
       "NumOfProducts        int64\n",
       "HasCrCard            int64\n",
       "IsActiveMember       int64\n",
       "EstimatedSalary    float64\n",
       "Exited               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RowNumber',\n",
       " 'CustomerId',\n",
       " 'CreditScore',\n",
       " 'Age',\n",
       " 'Tenure',\n",
       " 'Balance',\n",
       " 'NumOfProducts',\n",
       " 'HasCrCard',\n",
       " 'IsActiveMember',\n",
       " 'EstimatedSalary',\n",
       " 'Exited']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_features = [feature for feature in df.columns if df[feature].dtype != 'O']\n",
    "numerical_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  1,  8,  7,  4,  6,  3, 10,  5,  9,  0], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Tenure'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Tenure', 'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'Exited']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# discrete features\n",
    "discrete_features = [feature for feature in numerical_features if len(df[feature].unique())<25]\n",
    "discrete_features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Surname', 'Geography', 'Gender']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features = [feature for feature in df.columns if df[feature].dtype == 'O']\n",
    "categorical_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **EDA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France     5014\n",
       "Germany    2509\n",
       "Spain      2477\n",
       "Name: Geography, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Geography'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **1. Observation:** People of germany are more likely to leave the company."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Geography'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['Geography'])['Exited'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **2. Observation:** Female are more likely to leave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8MsnjST6c5P8m+bVFDgTHwxP3YA5V9VeSvCFJJbmzu7+04JEAgFUkkuEYquqlz7a/uw+fqFkAThZV9Z/zLNced/elJ3Ac+L65JhmO7Z4s/aKf/dbJ09ud5LxFDAWwxv2zRQ8AK8EnyQAAMPBJMsyhqk5PsjnJaU+vdfcfLW4igLVtuqPFP01yfp75u9O/wnFSEMmwjKr6u0nenWRjlp4e9dNJ/jjJzy9wLIC17t8muSHJbyX5uSR/O+6qxUnE/6ywvHcnuTDJQ939c0kuyPQEKQCO6QXdfWeWLu18qLt/I8lbFzwTzM0nybC873b3d6sqVXVqd3+5ql6x6KEA1rjHq+qHknylqq5NcjDJCxc8E8xNJMPyDlTVS5J8PMkdVfVokocWOhHA2vfuJD+SpSft3ZSlS9SuWuhEcBzc3QKOQ1W9PsmLk3yqu59Y9DwAwOoQyTCH6e4WZ2XmX1+6+97FTQSwNlXV9mfb72EinCxcbgHLqKqbkrwjyf4kT03LHXe3ADian0nySJIPJ/lCnvlAJjhp+CQZllFVe5O8yuUVAMurqnVJ3pTkyiSvTvKJJB/u7j0LHQyOk1vAwfK+mOQlix4C4GTQ3U9296e6+6os3Vd+X5LPTHe4gJOGT5JhGVW1Jcl/ylIsP/70uuvqAI6uqk7N0j2Rr0yyKcn2JNu6++Ai54LjIZJhGVW1J8mtSf4kf3FNcrr7Dxc2FMAaVVX/PslfTbIjye3d/cUFjwTfF5EMy6iqXd194aLnADgZVNVTSb41bc5GRiXp7n7RiZ8Kjp9IhmVU1T/P0mUW2/PMyy3cAg4AnqdEMiyjqj59lOXubreAA4DnKZEMAAADt4CDZVTVj1XVB6rqk9P2+VV19aLnAgBWj0iG5f27JDuT/OVp+38m+bVFDQMArD6RDMs7o7s/mun2b919JMmTix0JAFhNIhmW962qelmmWxlV1U8neWyxIwEAq2n9ogeAk8B7snT7t5dX1WeTbEhy+WJHAgBWk7tbwDFU1dnd/fD0en2SV2TpZvh7u/t7Cx0OAFhVLreAY/v4zOuPdPee7v6iQAaA5z+RDMdWM6/PW9gUAMAJJ5Lh2PoYrwGA5znXJMMxVNWTSb6VpU+UX5Dk20/vytJjqV+0qNkAgNUlkgEAYOByCwAAGIhkAAAYiGSABauqH6uqD1XV/qq6p6r+uKr+5gq8789W1X9ZiRkBftCIZIAFqqrK0j25/6i7z+vuv5bkiiQbFzCLp7ACTEQywGL9fJInuvt3nl7o7oe6+19V1bqqen9V7aqqB6rq7yV//gnxZ6rqY1X15ar6vSm2U1Vbp7V7k/ytp9+zqn60qrZV1d1VdV9VXTatv6OqtlfVXUnuPKE/OcAa5lMDgMV6ZZJ7j7Hv6iSPdfeFVXVqks9W1X+d9l0wnfu/knw2yWuraneSf52l8N6X5CMz7/XeJHd199+pqpckubuq/tu076eSvLq7D6/gzwVwUhPJAGtIVd2S5K8neSLJQ0leXVWXT7tfnGTztO/u7j4wnXN/kk1J/izJ17r7K9P6f0hyzXTuJUkurapfn7ZPS3L29PoOgQzwTCIZYLH2JPmlpze6+1er6owku5M8nORd3b1z9oSq+tkkj88sPZnlf59Xkl/q7r3De12cpYfmADDDNckAi3VXktOq6p0zaz8y/b0zyTur6oeTpKp+oqp+9Fne68tJNlXVy6ftK2f27Uzyrplrly9YkekBnqdEMsAC9dJjT38xyeur6mtVdXeSDyb5h0n+TZIHk9xbVV9Mcmue5RPj7v5uli6v+MT0xb1vzOy+KckPJ3mgqvZM2wAcg8dSAwDAwCfJAAAwEMkAADAQyQAAMBDJAAAwEMkAADAQyQAAMBDJAAAw+P9jvJ3Oq4YmYAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['Gender'])['Exited'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\dataScienceAi\\githubRepo\\playground\\.venv\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Geography', ylabel='Exited'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('Geography', 'Exited', data=df, hue='Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\dataScienceAi\\githubRepo\\playground\\.venv\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Tenure', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Tenure', data=df, hue='Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\dataScienceAi\\githubRepo\\playground\\.venv\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='NumOfProducts', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('NumOfProducts', data=df, hue='Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['RowNumber', 'CustomerId', 'Surname'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619    France  Female   42       2       0.00              1   \n",
       "1          608     Spain  Female   41       1   83807.86              1   \n",
       "2          502    France  Female   42       8  159660.80              3   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3) CreditScore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Categorical Encoding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "geography_encode = LabelEncoder()\n",
    "gender_encode = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Geography'] = geography_encode.fit_transform(df['Geography'])\n",
    "df['Gender'] = gender_encode.fit_transform(df['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619          0       0   42       2       0.00              1   \n",
       "1          608          2       0   41       1   83807.86              1   \n",
       "2          502          0       0   42       8  159660.80              3   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CreditScore          int64\n",
       "Geography            int32\n",
       "Gender               int32\n",
       "Age                  int64\n",
       "Tenure               int64\n",
       "Balance            float64\n",
       "NumOfProducts        int64\n",
       "HasCrCard            int64\n",
       "IsActiveMember       int64\n",
       "EstimatedSalary    float64\n",
       "Exited               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619          0       0   42       2       0.00              1   \n",
       "1          608          2       0   41       1   83807.86              1   \n",
       "2          502          0       0   42       8  159660.80              3   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  \n",
       "0          1               1        101348.88  \n",
       "1          0               1        112542.58  \n",
       "2          1               0        113931.57  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(['Exited'], axis=1)\n",
    "X.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['Exited']\n",
    "y.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (6700, 10)\n",
      "X_test: (3300, 10)\n",
      "y_train (6700,)\n",
      "y_test: (3300,)\n"
     ]
    }
   ],
   "source": [
    "print(f'X_train: {X_train.shape}')\n",
    "print(f'X_test: {X_test.shape}')\n",
    "print(f'y_train {y_train.shape}')\n",
    "print(f'y_test: {y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.681053</td>\n",
       "      <td>0.306591</td>\n",
       "      <td>-1.095988</td>\n",
       "      <td>-0.946079</td>\n",
       "      <td>-0.004426</td>\n",
       "      <td>0.473978</td>\n",
       "      <td>-0.911583</td>\n",
       "      <td>-1.547768</td>\n",
       "      <td>-1.030670</td>\n",
       "      <td>1.528261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.315279</td>\n",
       "      <td>0.306591</td>\n",
       "      <td>0.912419</td>\n",
       "      <td>-0.183251</td>\n",
       "      <td>1.032908</td>\n",
       "      <td>-0.052994</td>\n",
       "      <td>0.807737</td>\n",
       "      <td>0.646092</td>\n",
       "      <td>0.970243</td>\n",
       "      <td>-1.300657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.088246</td>\n",
       "      <td>0.306591</td>\n",
       "      <td>0.912419</td>\n",
       "      <td>0.960993</td>\n",
       "      <td>-0.350204</td>\n",
       "      <td>0.708442</td>\n",
       "      <td>-0.911583</td>\n",
       "      <td>0.646092</td>\n",
       "      <td>-1.030670</td>\n",
       "      <td>-1.309740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Geography    Gender       Age    Tenure   Balance  \\\n",
       "0     1.681053   0.306591 -1.095988 -0.946079 -0.004426  0.473978   \n",
       "1     0.315279   0.306591  0.912419 -0.183251  1.032908 -0.052994   \n",
       "2    -0.088246   0.306591  0.912419  0.960993 -0.350204  0.708442   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \n",
       "0      -0.911583  -1.547768       -1.030670         1.528261  \n",
       "1       0.807737   0.646092        0.970243        -1.300657  \n",
       "2      -0.911583   0.646092       -1.030670        -1.309740  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm = pd.DataFrame(X_train, columns=df.drop(['Exited'], axis=1).columns)\n",
    "df_norm.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8371    1\n",
       "5027    0\n",
       "9234    1\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.32622142, -0.90188624, -1.09598752,  0.29351742, -1.04175968,\n",
       "        -1.22584767, -0.91158349,  0.64609167,  0.97024255,  0.02188649]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Model Building**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.5, max_depth=1, random_state=0)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "clf = GradientBoostingClassifier(\n",
    "    n_estimators=100, learning_rate=0.5, max_depth=1, random_state=0)\n",
    "\n",
    "clf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8645454545454545"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6252    0\n",
       "4684    0\n",
       "1731    0\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true = y_test\n",
    "y_true[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **API**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619          0       0   42       2       0.00              1   \n",
       "1          608          2       0   41       1   83807.86              1   \n",
       "2          502          0       0   42       8  159660.80              3   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.68105307,  0.30659057, -1.09598752, -0.94607926, -0.00442596,\n",
       "        0.47397787, -0.91158349, -1.54776799, -1.03067011,  1.52826085])"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.1300000e+02, 1.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "        0.0000000e+00, 1.8797688e+05],\n",
       "       [6.8100000e+02, 1.0000000e+00, 1.0000000e+00, ..., 1.0000000e+00,\n",
       "        1.0000000e+00, 2.5292530e+04],\n",
       "       [6.4200000e+02, 1.0000000e+00, 1.0000000e+00, ..., 1.0000000e+00,\n",
       "        0.0000000e+00, 2.4770220e+04],\n",
       "       ...,\n",
       "       [7.3500000e+02, 0.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "        0.0000000e+00, 9.2220120e+04],\n",
       "       [6.6700000e+02, 0.0000000e+00, 1.0000000e+00, ..., 1.0000000e+00,\n",
       "        0.0000000e+00, 9.7508040e+04],\n",
       "       [6.9700000e+02, 1.0000000e+00, 1.0000000e+00, ..., 1.0000000e+00,\n",
       "        1.0000000e+00, 5.3581140e+04]])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inverseScaler = scaler.inverse_transform(X_train)\n",
    "inverseScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>813.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106059.40</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187976.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>681.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>73179.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25292.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>642.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120688.61</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24770.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>612.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>117989.76</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54129.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>586.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6975.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>6695</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>69712.74</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>69381.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6696</th>\n",
       "      <td>682.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>706.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6697</th>\n",
       "      <td>735.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92220.12</td>\n",
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       "    <tr>\n",
       "      <th>6698</th>\n",
       "      <td>667.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>190227.46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97508.04</td>\n",
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       "    <tr>\n",
       "      <th>6699</th>\n",
       "      <td>697.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>147910.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53581.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6700 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore  Geography  Gender   Age  Tenure    Balance  NumOfProducts  \\\n",
       "0           813.0        1.0     0.0  29.0     5.0  106059.40            1.0   \n",
       "1           681.0        1.0     1.0  37.0     8.0   73179.34            2.0   \n",
       "2           642.0        1.0     1.0  49.0     4.0  120688.61            1.0   \n",
       "3           612.0        0.0     0.0  31.0     8.0  117989.76            1.0   \n",
       "4           586.0        0.0     1.0  33.0     9.0       0.00            1.0   \n",
       "...           ...        ...     ...   ...     ...        ...            ...   \n",
       "6695        768.0        0.0     1.0  54.0     8.0   69712.74            1.0   \n",
       "6696        682.0        0.0     0.0  58.0     1.0       0.00            1.0   \n",
       "6697        735.0        0.0     0.0  38.0     1.0       0.00            3.0   \n",
       "6698        667.0        0.0     1.0  43.0     8.0  190227.46            1.0   \n",
       "6699        697.0        1.0     1.0  51.0     1.0  147910.30            1.0   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  \n",
       "0           0.0             0.0        187976.88  \n",
       "1           1.0             1.0         25292.53  \n",
       "2           1.0             0.0         24770.22  \n",
       "3           1.0             1.0         54129.86  \n",
       "4           1.0             0.0          6975.02  \n",
       "...         ...             ...              ...  \n",
       "6695        1.0             1.0         69381.05  \n",
       "6696        1.0             1.0           706.50  \n",
       "6697        0.0             0.0         92220.12  \n",
       "6698        1.0             0.0         97508.04  \n",
       "6699        1.0             1.0         53581.14  \n",
       "\n",
       "[6700 rows x 10 columns]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reverse_encode = pd.DataFrame(inverseScaler, columns=df.columns[:-1])\n",
    "df_reverse_encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>619</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619          0       0   42       2       0.00              1   \n",
       "1          608          2       0   41       1   83807.86              1   \n",
       "2          502          0       0   42       8  159660.80              3   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  "
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender1 = gender_encode.transform(['Male'])\n",
    "gender1[0]\n",
    "\n",
    "geography1 = geography_encode.transform(['Spain'])\n",
    "geography1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   608,      2,      1,     41,      1,  83807,      1,      0,\n",
       "            1, 113931])"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testArray = np.array([608,geography1[0],gender1[0],41,1,83807,1,0,1,113931])\n",
    "testArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\dataScienceAi\\githubRepo\\playground\\.venv\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.44003595,  1.51506738,  0.91241915,  0.19816383, -1.38753759,\n",
       "         0.11733624, -0.91158349, -1.54776799,  0.97024255,  0.24067699]])"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = scaler.transform([testArray])\n",
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "modelPath = './data/trainedModel.sav'\n",
    "pickle.dump(clf, open(modelPath, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = pickle.load(open(modelPath, 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Performance Metrics**\n",
    "\n",
    "In this problem recall  is important as **True Negative(Type 2 error)** is important here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.96      0.92      2657\n",
      "           1       0.74      0.47      0.58       643\n",
      "\n",
      "    accuracy                           0.86      3300\n",
      "   macro avg       0.81      0.72      0.75      3300\n",
      "weighted avg       0.85      0.86      0.85      3300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_true=y_true, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[2548  109]\n",
      " [ 338  305]]\n",
      "Normalized confusion matrix\n",
      "[[0.95897629 0.04102371]\n",
      " [0.52566096 0.47433904]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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xEDgPmJSGc1NZk0p52j4YmB4RrwBIupGspeu5ZpdqZ/r12Yhdt+/LlGkz2HO3rfn6l/Zl5PDBPPX865x16W0sWrKM9bt25jvHfI7Pn3IZp3z1s6uXnT1vEZfdcB//ueM83l++gvsff4H7H3+hgnvTcc1duITNNukBQO+NuzN34RIAPj5gc/5+/zPstfu2TJk2gzfmLOTNue+w6cbdKxlu+9dKZ+0R8XAzazugkfkDOLmReYmIMcCYfLddytP2vFqvJI2WNFnS5KhbVsJwWt/6XTtz3UUncOavb2XJu+8z5taH2P3wc9jnqAt5a/5ifn7q5wE4ffQhXDHuX7y7bMUay/fo1pXh++7CwBFns+PBP2a9dTvz5YM/WYldsRxZrSd7feqoz7FoyXvsc+QFXHnTg+y6XV9qq+DXMZXWijXPiql4g1G67eBKgJr1Nm31FrFS6VRbw9iLvs4td07m7/c/A8C8VFsBGPvXR7jpkhMBGLTzloz4zEB+9q3D6NGtK6tWBcuXf8DchUt47c0FqxuY7rj/GQbv2p+b/zmp/DvUwW26UTfmzF/EZpv0YM78RfTq2Q2A7ht05fKzjwYgIthtxNlsufnGlQy13ZOgxp0hN6upVq2qcNlPjuKlGXP43Z//tbqs98bdeStdDzt06G48/9/sNrPhoy9dPc/pXx/Ou8uWc9UtE/nEzlsyaJf+dO2yDsuWf8B+n9yep55/vaz7YZlh++7CuL8/zmnHHsi4vz/OwfvtCsCiJe/Rdd3OdF6nE9f99VH22n1bum/QtcLRtndtv1aZj1Imz0nAAEn9yZLmSODIEm6vbIbstjUjD9mTaS/PYuKfspsIzrt8PF84aBC7bNeXiOD12Qs57Rfjml3PlGmvMf6+p3jghtNZuXIVU1+cydjbHynHLnRox//4jzwy5WUWvLOUnQ85izNGD+e0UZ/juDPHcMP4x+i32Ub88YKvAVmD0Uk/ux4hdti6D5f95KgKR18dqiB3ouz6aYlWnrV6XQrUAmMi4vzm5q9Zb9Posv2XSxaPtb63J/220iFYAfbecxBTpkyuaOpad7PtYstRl+U170u/HDYln5vkK6Gk1zwjYgLZrQFmZhlVR82z4g1GZtaxCDcYmZkVxcnTzKxQPm03Myuc8DOMzMyK4Ps8zcyKUgW508nTzMrMP880Myucr3mamRWpCnKnk6eZlZ9rnmZmRaiC3OnkaWZlJtc8zcwKJuTWdjOzYlRBxdPJ08zKz6ftZmaFcscgZmaF803yZmZFcvI0MyuCW9vNzArla55mZoWT+/M0MytOFeROJ08zK7+aKsieTp5mVlZyZ8hmZsWpgtzp5Glm5VfVDUaSLgOiqekR8e2SRGRmVa8KcmezNc/JZYvCzDoMkd2u1N41mTwjYmzuuKT1IuK90odkZtWuGq551rQ0g6RPSXoOeCGN7ybpdyWPzMyqk7LOkPMZ2rIWkydwKXAQsAAgIp4B9i1hTGZWxUR2n2c+Q4vrksZImivp2ZyycyTNkvR0GobnTDtT0nRJL0o6KKd8WCqbLumMfPYjn+RJRLzRoGhlPsuZmTVGym/Iw7XAsEbKL4mIgWmYkG1TOwEjgZ3TMr+TVCupFrgcOBjYCTgizdusfG5VekPSXkBIWgf4DvB8HsuZmTWqtW5VioiJkrbKc/YRwI0RsRx4VdJ0YHCaNj0iXkmx3Zjmfa65leVT8zwROBnYHHgTGJjGzcwKlm+tM+XXTSRNzhlG57mZUyRNTaf1PVPZ5kDuWfTMVNZUebNarHlGxHzgqDwDNjNrUW3+Nc/5ETGowNVfAZxHdp/6ecDFwNcKXEeL8mlt31rSHZLmpQuzf5O0dWsHYmYdh6S8hmJExFsRsTIiVgFX8eGp+SygX86sfVNZU+XNyue0/c/AzUAf4GPALcC4PJYzM/uIrLU9v6Go9Ut9ckYPB+pb4scDIyV1kdQfGAA8AUwCBkjqL6kzWaPS+Ja2k0+D0XoRcX3O+A2SfpDPTpiZfcRa1Co/uiqNA4aSXRudCZwNDJU0kOy0fQbwDYCImCbpZrKGoDrg5IhYmdZzCnAXUAuMiYhpLW27ud+2b5Re/jPd93RjCuYrwISC99LMLGmt37ZHxBGNFF/TzPznA+c3Uj6BAvNaczXPKWTJsn43v5G7LeDMQjZkZlavqntVioj+5QzEzDoGAbVt/KeX+cirP09JHye7837d+rKIuK5UQZlZdWv/qTOP5CnpbLILsjuRXRM4GHgYcPI0s4JJ1fEMo3xuVfoicAAwJyKOA3YDepQ0KjOraq342/aKyee0fVlErJJUJ6k7MJc1byg1MytIVTcY5ZgsaUOyO/WnAEuBx0oZlJlVtyrInXn9tv2k9PL3ku4EukfE1NKGZWbVSlJ1t7ZL2qO5aRHxZGlCMrNqV+2n7Rc3My2Az7RyLOyyfT8m/Ku5zVpb8867KyodghWgblWTD8Qtq7x6YW/jmrtJfv9yBmJmHYOo/pqnmVlJVMElTydPMysvqQP9PNPMrDVVQe7Mqyd5SfqqpJ+m8S0kDW5pOTOzplTDL4zyafT6HfApoL7fvCVkj+k0MytYaz63vZLyOW3fMyL2kPQUQES8nbqqNzMrSlXfqpTjg/RQ+ACQ1AtYVdKozKyqtfFKZV7ySZ6/AW4HNpV0PlkvS2eVNCozq1pV//PMehHxJ0lTyLqlE3BYRDxf8sjMrGpVQe7MqzPkLYD3gDtyyyLi9VIGZmbVqb7BqL3L57T9H3z4ILh1gf7Ai8DOJYzLzKpYFeTOvE7bd8kdT70tndTE7GZmzVMHOW1vKCKelLRnKYIxs45BVfAIuHyueX43Z7QG2AN4s2QRmVlVE9CpCm70zKfm2S3ndR3ZNdBbSxOOmXUEVd8lXbo5vltEfL9M8ZhZlcta2ysdxdpr7jEcnSKiTtLe5QzIzKpcO+j0Ix/N1TyfILu++bSk8cAtwLv1EyPithLHZmZVqqPc57kusIDsmUX193sG4ORpZgUTUFvlDUabppb2Z/kwadZrG0+RMrN2SNRU+a1KtcAG0OheOnmaWVGyB8BVOoq111zynB0R55YtEjPrGDrAL4yqYPfMrC2q9gajA8oWhZl1GNVy2t5km1dELCxnIGbWcdTWKK+hJZLGSJor6dmcso0k3SPp5fR/z1QuSb+RNF3S1NTJUf0yo9L8L0salc8+VMENA2bWnogs8eQz5OFaYFiDsjOA+yJiAHBfGgc4GBiQhtHAFZAlW+BsYE9gMHB2fcJtjpOnmZWXst+25zO0JCImAg3PkkcAY9PrscBhOeXXRebfwIaS+gAHAfdExMKIeBu4h48m5I8ouEs6M7O1VcAlz00kTc4ZvzIirmxhmd4RMTu9ngP0Tq83B97ImW9mKmuqvFlOnmZWVgU+hmN+RAwqdlsREZJKcl+6T9vNrOyU51Ckt9LpOOn/ual8FtAvZ76+qayp8mY5eZpZmYmamvyGIo0H6lvMRwF/yyk/JrW6DwEWpdP7u4ADJfVMDUUHprJm+bTdzMqqvrW9VdYljQOGkl0bnUnWan4hcLOk44HXgC+n2ScAw4HpZE8EPg6y2zIlnQdMSvOdm8+tmk6eZlZ2rdWTfEQc0cSkj/zIJyICOLmJ9YwBxhSybSdPMyu7KviBkZOnmZWZOsAzjMzMWpuAWidPM7PCtf/U6eRpZhVQBRVPJ08zK6/sVqX2nz2dPM2s7FzzNDMrmJBrnmZmhXFru5lZMeTTdjOzojh5mpkVwdc8zcwKlHWGXOko1p6Tp5mVXbU/t93MrCR82m4sX/EBR3znclZ8UEfdylUM229XTj12GGf87008++IbBLBV31788vSRrN+1C2++9TY/uGgci5cuY9Wq4AcnHMLQITtWejc6lOXLP+DL3/ktKz6oY+XKVRy8326cdtww3pi9gG+dez3vLHqXj2/fj1//6Eg6r9OJv/zzCS74/R303qQHAMcc/mlGHjqkwnvRfvm0vQWSxgCHAnMj4uOl2k6ldV6nE9f/+pus37ULH9StZOS3f8t+g3fkxyeNoNv66wJw/u/+xvW3P8yJRx7A5Tfcy/D9BnLUiL14ecYcTjjzah4cclaF96Jj6dy5E3/+9Umsv152zL70rcsYOngHrr7lQY7/4n78zwG78+OLb+HmCY/z1RF7A3DI/gM599QvVDjyalEdN8mX8hlG15LHs4/bO0ms37ULAHV1K/mgbiUSqxNnRLB8+Qer+y+UYOl77wOw5N332XTj7pUJvAOTxPrrfXjM6upWgsRjT07n4P12BeALwz7J3Q8/W8kwq1e6zzOfoS0rWc0zIiZK2qpU629LVq5cxWEnXsJrs+bz1cP2ZuCOWwJw+kU38sATz7Ptlr0585v/D4BvjzqIY3/4B667/WGWvb+C6371jUqG3mGtXLmK/xn9a16bNZ+jD9+bLT+2Md03WJdOnWoB2KxXD96at2j1/HdOnMoTU1+hf99e/OSUEXxs056VCr0qtPG8mJeKPz1T0mhJkyVNXjB/XqXDKUptbQ13XPU9Hr75pzzzwuu89OpsAC46fSSP3nw222zRm3/c/zQAd/zrKT5/0Cd55OafcvUFJ/C9C8axatWqCkbfMdXW1jDhmu/z2C1n88zzr/Pf1+c2Oe8Be+3MQzf+hDvH/IB9Bm3H9y8YV8ZIq0/9zzPzGdqyiifPiLgyIgZFxKCNN+lV6XDWSvcNujJk4LZMfOKF1WW1tTUcuv9A7npoKgC3THic4UN3A2CPnbdixYoPeHvRuxWJ16B7t658avdtefK5GSxe+n52Cg/MmbeI3r2yBqKePdanS+fsJO0rhwzh2ZdmVizeqlHiB7eXQ8WTZ3u34J2lLF66DID3l3/AI1Neon+/TZkxaz6QXfO879FpbN1vUwA+1rsnjz35MgDTX3uL5Svq2GjDDSoTfAe14J2lLF5Sf8xW8NDkl9h2i94M2X1b/vlg9kfu1jsn8bm9s3bOuQsWr1723kefZZstNi1/0FVGef5ry3yr0lqat2AxP7hoHKtWBatWBcOH7sb+Q3Zk5HcuZ+l77xMBO27Th5+d+kUAzjzxf/jxxbfwx79MRBIX/XBkVTwMqz2Zu2Ax379gHCtXrSJWBYfsvxsH7LUzA7bajG+dex0XXzOBnQb05cvD9wTg2lsncu+j06itrWHDbuvxqzOaetqt5asaPvLKHmVcghXnPIweeAs4OyKuaW6Z3Xb/REz416MlicdKo7YabtjrQA4a+imeeWpKRQ/ajrvsHtf97YG85h28zYZTImJQaSMqTilb2/3n2cwaVwV/c33abmZlJfm37WZmRWn/qdPJ08wqoQqyp5OnmZVZ278NKR9OnmZWdlVwydPJ08zKSzh5mpkVxaftZmZFcM3TzKwIVZA7nTzNrMzaQY9J+XDyNLOyq4Zrnu6SzszKqv4BcPkMea1PmiHpP5KeljQ5lW0k6R5JL6f/e6ZySfqNpOmSpkrao9j9cPI0s/Jr/c6Q94+IgTk9MJ0B3BcRA4D70jjAwcCANIwGrih2F5w8zazsytAZ8ghgbHo9Fjgsp/y6yPwb2FBSn2I24ORpZmVXwNMzN6l/xlkaRjeyugDuljQlZ3rviJidXs8BeqfXmwNv5Cw7M5UVzA1GZlZ2BdQp5+fRGfKnI2KWpE2BeyS9kDsxIkJSq/f67pqnmZVfK17zjIhZ6f+5wO3AYOCt+tPx9H/941FnAf1yFu+bygrm5GlmZVXfGXI+Q8vr0vqSutW/Bg4EngXGA6PSbKOAv6XX44FjUqv7EGBRzul9QXzabmZl14p3efYGbk8PUewE/Dki7pQ0CbhZ0vHAa8CX0/wTgOHAdOA94LhiN+zkaWbl10rZMyJeAXZrpHwBcEAj5QGc3BrbdvI0szJzZ8hmZkVxr0pmZgVyZ8hmZkXyabuZWRFc8zQzK0IV5E4nTzMrM7nmaWZWpPafPZ08zays6jtDbu+cPM2s7HzabmZWBN+qZGZWjPafO508zaz8qiB3OnmaWXnJtyqZmRVHVZA9nTzNrOzaf+p08jSzCqiCiqeTp5mVmztDNjMrmPvzNDMrkpOnmVkRfNpuZlYo3+dpZlY44VuVzMyKUwXZ08nTzMrO1zzNzIrgzpDNzIrh5GlmVjiftpuZFahafmGkiKh0DKtJmge8Vuk4SmATYH6lg7CCVOsx2zIielUyAEl3kr2/+ZgfEcNKGU+x2lTyrFaSJkfEoErHYfnzMbOW1FQ6ADOz9sjJ08ysCE6e5XFlpQOwgvmYWbN8zdPMrAiueZqZFcHJ08ysCE6eJSRpmKQXJU2XdEal47GWSRojaa6kZysdi7VtTp4lIqkWuBw4GNgJOELSTpWNyvJwLdAmb8q2tsXJs3QGA9Mj4pWIWAHcCIyocEzWgoiYCCysdBzW9jl5ls7mwBs54zNTmZlVASdPM7MiOHmWziygX85431RmZlXAybN0JgEDJPWX1BkYCYyvcExm1kqcPEskIuqAU4C7gOeBmyNiWmWjspZIGgc8Bmwvaaak4ysdk7VN/nmmmVkRXPM0MyuCk6eZWRGcPM3MiuDkaWZWBCdPM7MiOHm2I5JWSnpa0rOSbpG03lqs61pJX0yvr26u0xJJQyXtVcQ2Zkj6yFMSmypvMM/SArd1jqTvFxqjWbGcPNuXZRExMCI+DqwATsydKKlTMSuNiBMi4rlmZhkKFJw8zaqZk2f79RCwbaoVPiRpPPCcpFpJ/ytpkqSpkr4BoMxvU/+i9wKb1q9I0gOSBqXXwyQ9KekZSfdJ2oosSZ+War37SOol6da0jUmS9k7LbizpbknTJF0NqKWdkPRXSVPSMqMbTLskld8nqVcq20bSnWmZhyTt0CrvplmBiqqpWGWlGubBwJ2paA/g4xHxakpAiyLik5K6AI9IuhvYHdierG/R3sBzwJgG6+0FXAXsm9a1UUQslPR7YGlE/CrN92fgkoh4WNIWZL+i2hE4G3g4Is6VdAiQz69zvpa20RWYJOnWiFgArA9MjojTJP00rfsUsgeznRgRL0vaE/gd8Jki3kazteLk2b50lfR0ev0QcA3Z6fQTEfFqKj8Q2LX+eibQAxgA7AuMi4iVwJuS/tXI+ocAE+vXFRFN9Wv5WWAnaXXFsrukDdI2Pp+W/Yekt/PYp29LOjy97pdiXQCsAm5K5TcAt6Vt7AXckrPtLnlsw6zVOXm2L8siYmBuQUoi7+YWAd+KiLsazDe8FeOoAYZExPuNxJI3SUPJEvGnIuI9SQ8A6zYxe6TtvtPwPTCrBF/zrD53Ad+UtA6ApO0krQ9MBL6Sron2AfZvZNl/A/tK6p+W3SiVLwG65cx3N/Ct+hFJA9PLicCRqexgoGcLsfYA3k6Jcweymm+9GqC+9nwk2eWAxcCrkr6UtiFJu7WwDbOScPKsPleTXc98Mj3E7A9kZxi3Ay+nadeR9Ry0hoiYB4wmO0V+hg9Pm+8ADq9vMAK+DQxKDVLP8WGr/8/Iku80stP311uI9U6gk6TngQvJkne9d4HBaR8+A5ybyo8Cjk/xTcOPNrEKca9KZmZFcM3TzKwITp5mZkVw8jQzK4KTp5lZEZw8zcyK4ORpZlYEJ08zsyL8f/koOtTkHq1NAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "titles_options = [\n",
    "    (\"Confusion matrix, without normalization\", None),\n",
    "    (\"Normalized confusion matrix\", \"true\"),\n",
    "]\n",
    "for title, normalize in titles_options:\n",
    "    disp = ConfusionMatrixDisplay.from_estimator(\n",
    "        clf,\n",
    "        X_test,\n",
    "        y_test,\n",
    "        # display_labels=class_names,\n",
    "        cmap=plt.cm.Blues,\n",
    "        normalize=normalize,\n",
    "    )\n",
    "    disp.ax_.set_title(title)\n",
    "\n",
    "    print(title)\n",
    "    print(disp.confusion_matrix)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "8a0412b84595eb5122996a27084f4b8f4af6e1eb910646ec03a64773d10ebc80"
  },
  "kernelspec": {
   "display_name": "Python 3.7.9 ('.venv': venv)",
   "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": "3.7.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}