notebooks/data_transformation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"../\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data set review & preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"artifacts/data_ingestion/Churn_Modelling.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10002, 14)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10002 entries, 0 to 10001\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10002 non-null  int64  \n",
      " 1   CustomerId       10002 non-null  int64  \n",
      " 2   Surname          10002 non-null  object \n",
      " 3   CreditScore      10002 non-null  int64  \n",
      " 4   Geography        10001 non-null  object \n",
      " 5   Gender           10002 non-null  object \n",
      " 6   Age              10001 non-null  float64\n",
      " 7   Tenure           10002 non-null  int64  \n",
      " 8   Balance          10002 non-null  float64\n",
      " 9   NumOfProducts    10002 non-null  int64  \n",
      " 10  HasCrCard        10001 non-null  float64\n",
      " 11  IsActiveMember   10001 non-null  float64\n",
      " 12  EstimatedSalary  10002 non-null  float64\n",
      " 13  Exited           10002 non-null  int64  \n",
      "dtypes: float64(5), int64(6), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          1\n",
       "Gender             0\n",
       "Age                1\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          1\n",
       "IsActiveMember     1\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9998, 14)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9998 entries, 0 to 10001\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        9998 non-null   int64  \n",
      " 1   CustomerId       9998 non-null   int64  \n",
      " 2   Surname          9998 non-null   object \n",
      " 3   CreditScore      9998 non-null   int64  \n",
      " 4   Geography        9998 non-null   object \n",
      " 5   Gender           9998 non-null   object \n",
      " 6   Age              9998 non-null   float64\n",
      " 7   Tenure           9998 non-null   int64  \n",
      " 8   Balance          9998 non-null   float64\n",
      " 9   NumOfProducts    9998 non-null   int64  \n",
      " 10  HasCrCard        9998 non-null   float64\n",
      " 11  IsActiveMember   9998 non-null   float64\n",
      " 12  EstimatedSalary  9998 non-null   float64\n",
      " 13  Exited           9998 non-null   int64  \n",
      "dtypes: float64(5), int64(6), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>CustomerId</th>\n",
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       "      <th>CreditScore</th>\n",
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       "      <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>42.0</td>\n",
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       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
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       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
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       "    <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>42.0</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>15574012</td>\n",
       "      <td>Chu</td>\n",
       "      <td>645</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>44.0</td>\n",
       "      <td>8</td>\n",
       "      <td>113755.78</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149756.71</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  42.0   \n",
       "1          2    15647311      Hill          608     Spain  Female  41.0   \n",
       "2          3    15619304      Onio          502    France  Female  42.0   \n",
       "3          4    15701354      Boni          699    France  Female  39.0   \n",
       "5          6    15574012       Chu          645     Spain    Male  44.0   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1        1.0             1.0   \n",
       "1       1   83807.86              1        0.0             1.0   \n",
       "2       8  159660.80              3        1.0             0.0   \n",
       "3       1       0.00              2        0.0             0.0   \n",
       "5       8  113755.78              2        1.0             0.0   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "5        149756.71       1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          9996\n",
       "CustomerId         9996\n",
       "Surname            2932\n",
       "CreditScore         460\n",
       "Geography             3\n",
       "Gender                2\n",
       "Age                  73\n",
       "Tenure               11\n",
       "Balance            6379\n",
       "NumOfProducts         4\n",
       "HasCrCard             2\n",
       "IsActiveMember        2\n",
       "EstimatedSalary    9995\n",
       "Exited                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CustomerId'].duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CustomerId</th>\n",
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       "      <th>10000</th>\n",
       "      <td>10000</td>\n",
       "      <td>15628319</td>\n",
       "      <td>Walker</td>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
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       "      <td>France</td>\n",
       "      <td>Female</td>\n",
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       "      <th>9998</th>\n",
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       "      <td>Sabbatini</td>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>9999</td>\n",
       "      <td>15682355</td>\n",
       "      <td>Sabbatini</td>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "       RowNumber  CustomerId    Surname  CreditScore Geography  Gender   Age  \\\n",
       "10000      10000    15628319     Walker          792    France  Female  28.0   \n",
       "10001      10000    15628319     Walker          792    France  Female  28.0   \n",
       "9998        9999    15682355  Sabbatini          772   Germany    Male  42.0   \n",
       "9999        9999    15682355  Sabbatini          772   Germany    Male  42.0   \n",
       "\n",
       "       Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "10000       4  130142.79              1        1.0             0.0   \n",
       "10001       4  130142.79              1        1.0             0.0   \n",
       "9998        3   75075.31              2        1.0             0.0   \n",
       "9999        3   75075.31              2        1.0             0.0   \n",
       "\n",
       "       EstimatedSalary  Exited  \n",
       "10000         38190.78       0  \n",
       "10001         38190.78       0  \n",
       "9998          92888.52       1  \n",
       "9999          92888.52       1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = df[\"CustomerId\"]\n",
    "df[ids.isin(ids[ids.duplicated()])].sort_values(\"CustomerId\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41.0</td>\n",
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       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42.0</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>15574012</td>\n",
       "      <td>Chu</td>\n",
       "      <td>645</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>44.0</td>\n",
       "      <td>8</td>\n",
       "      <td>113755.78</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149756.71</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  42.0   \n",
       "1          2    15647311      Hill          608     Spain  Female  41.0   \n",
       "2          3    15619304      Onio          502    France  Female  42.0   \n",
       "3          4    15701354      Boni          699    France  Female  39.0   \n",
       "5          6    15574012       Chu          645     Spain    Male  44.0   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1        1.0             1.0   \n",
       "1       1   83807.86              1        0.0             1.0   \n",
       "2       8  159660.80              3        1.0             0.0   \n",
       "3       1       0.00              2        0.0             0.0   \n",
       "5       8  113755.78              2        1.0             0.0   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "5        149756.71       1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Exited', 'Retained'\n",
    "sizes = [df.Exited[df['Exited']==1].count(), df.Exited[df['Exited']==0].count()]\n",
    "explode = (0, 0.1)\n",
    "fig1, ax1 = plt.subplots(figsize=(10, 8))\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')\n",
    "plt.title(\"Proportion of customer churned and retained\", size = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x ='Exited', data = df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='IsActiveMember', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axar = plt.subplots(2, 2, figsize=(18, 8))\n",
    "sns.countplot(x='Gender', hue = 'Exited',data = df, ax=axar[0][0])\n",
    "sns.countplot(x='Geography', hue = 'Exited',data = df, ax=axar[0][1])\n",
    "sns.countplot(x='HasCrCard', hue = 'Exited',data = df, ax=axar[1][0])\n",
    "sns.countplot(x='IsActiveMember', hue = 'Exited',data = df, ax=axar[1][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering & Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['Gender'] = df['Gender'].map({'Male':0, 'Female':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>42.0</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>15574012</td>\n",
       "      <td>Chu</td>\n",
       "      <td>645</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>44.0</td>\n",
       "      <td>8</td>\n",
       "      <td>113755.78</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149756.71</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  42.0   \n",
       "1          2    15647311      Hill          608     Spain  Female  41.0   \n",
       "2          3    15619304      Onio          502    France  Female  42.0   \n",
       "3          4    15701354      Boni          699    France  Female  39.0   \n",
       "5          6    15574012       Chu          645     Spain    Male  44.0   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1        1.0             1.0   \n",
       "1       1   83807.86              1        0.0             1.0   \n",
       "2       8  159660.80              3        1.0             0.0   \n",
       "3       1       0.00              2        0.0             0.0   \n",
       "5       8  113755.78              2        1.0             0.0   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "5        149756.71       1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded = pd.get_dummies(df, columns = ['Gender', 'Geography'], drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>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",
       "      <th>Gender_Male</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</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>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>42.0</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>15574012</td>\n",
       "      <td>Chu</td>\n",
       "      <td>645</td>\n",
       "      <td>44.0</td>\n",
       "      <td>8</td>\n",
       "      <td>113755.78</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149756.71</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore   Age  Tenure    Balance  \\\n",
       "0          1    15634602  Hargrave          619  42.0       2       0.00   \n",
       "1          2    15647311      Hill          608  41.0       1   83807.86   \n",
       "2          3    15619304      Onio          502  42.0       8  159660.80   \n",
       "3          4    15701354      Boni          699  39.0       1       0.00   \n",
       "5          6    15574012       Chu          645  44.0       8  113755.78   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \\\n",
       "0              1        1.0             1.0        101348.88       1   \n",
       "1              1        0.0             1.0        112542.58       0   \n",
       "2              3        1.0             0.0        113931.57       1   \n",
       "3              2        0.0             0.0         93826.63       0   \n",
       "5              2        1.0             0.0        149756.71       1   \n",
       "\n",
       "   Gender_Male  Geography_Germany  Geography_Spain  \n",
       "0            0                  0                0  \n",
       "1            0                  0                1  \n",
       "2            0                  0                0  \n",
       "3            0                  0                0  \n",
       "5            1                  0                1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9996 entries, 0 to 10000\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   RowNumber          9996 non-null   int64  \n",
      " 1   CustomerId         9996 non-null   int64  \n",
      " 2   Surname            9996 non-null   object \n",
      " 3   CreditScore        9996 non-null   int64  \n",
      " 4   Age                9996 non-null   float64\n",
      " 5   Tenure             9996 non-null   int64  \n",
      " 6   Balance            9996 non-null   float64\n",
      " 7   NumOfProducts      9996 non-null   int64  \n",
      " 8   HasCrCard          9996 non-null   float64\n",
      " 9   IsActiveMember     9996 non-null   float64\n",
      " 10  EstimatedSalary    9996 non-null   float64\n",
      " 11  Exited             9996 non-null   int64  \n",
      " 12  Gender_Male        9996 non-null   uint8  \n",
      " 13  Geography_Germany  9996 non-null   uint8  \n",
      " 14  Geography_Spain    9996 non-null   uint8  \n",
      "dtypes: float64(5), int64(6), object(1), uint8(3)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_encoded.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df_encoded['HasCrCard'] = df_encoded['HasCrCard'].astype(np.uint8)\n",
    "df_encoded['IsActiveMember'] = df_encoded['IsActiveMember'].astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9996 entries, 0 to 10000\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   RowNumber          9996 non-null   int64  \n",
      " 1   CustomerId         9996 non-null   int64  \n",
      " 2   Surname            9996 non-null   object \n",
      " 3   CreditScore        9996 non-null   int64  \n",
      " 4   Age                9996 non-null   float64\n",
      " 5   Tenure             9996 non-null   int64  \n",
      " 6   Balance            9996 non-null   float64\n",
      " 7   NumOfProducts      9996 non-null   int64  \n",
      " 8   HasCrCard          9996 non-null   uint8  \n",
      " 9   IsActiveMember     9996 non-null   uint8  \n",
      " 10  EstimatedSalary    9996 non-null   float64\n",
      " 11  Exited             9996 non-null   int64  \n",
      " 12  Gender_Male        9996 non-null   uint8  \n",
      " 13  Geography_Germany  9996 non-null   uint8  \n",
      " 14  Geography_Spain    9996 non-null   uint8  \n",
      "dtypes: float64(3), int64(6), object(1), uint8(5)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_encoded.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
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       "      <th>CreditScore</th>\n",
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       "      <th>Tenure</th>\n",
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       "      <th>Geography_Spain</th>\n",
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       "      <th>normalizedAge</th>\n",
       "      <th>normalizedTenure</th>\n",
       "      <th>normalizedBalance</th>\n",
       "      <th>normalizedEstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.117505</td>\n",
       "      <td>0.216237</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>42.0</td>\n",
       "      <td>8</td>\n",
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       "      <td>1.032669</td>\n",
       "      <td>1.333214</td>\n",
       "      <td>0.240389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>15574012</td>\n",
       "      <td>Chu</td>\n",
       "      <td>645</td>\n",
       "      <td>44.0</td>\n",
       "      <td>8</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore   Age  Tenure    Balance  \\\n",
       "0          1    15634602  Hargrave          619  42.0       2       0.00   \n",
       "1          2    15647311      Hill          608  41.0       1   83807.86   \n",
       "2          3    15619304      Onio          502  42.0       8  159660.80   \n",
       "3          4    15701354      Boni          699  39.0       1       0.00   \n",
       "5          6    15574012       Chu          645  44.0       8  113755.78   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \\\n",
       "0              1          1               1        101348.88       1   \n",
       "1              1          0               1        112542.58       0   \n",
       "2              3          1               0        113931.57       1   \n",
       "3              2          0               0         93826.63       0   \n",
       "5              2          1               0        149756.71       1   \n",
       "\n",
       "   Gender_Male  Geography_Germany  Geography_Spain  normalizedCreditScore  \\\n",
       "0            0                  0                0              -0.326054   \n",
       "1            0                  0                1              -0.439902   \n",
       "2            0                  0                0              -1.536986   \n",
       "3            0                  0                0               0.501933   \n",
       "5            1                  0                1              -0.056958   \n",
       "\n",
       "   normalizedAge  normalizedTenure  normalizedBalance  \\\n",
       "0       0.293570         -1.041870          -1.225699   \n",
       "1       0.198222         -1.387627           0.117505   \n",
       "2       0.293570          1.032669           1.333214   \n",
       "3       0.007526         -1.387627          -1.225699   \n",
       "5       0.484266          1.032669           0.597486   \n",
       "\n",
       "   normalizedEstimatedSalary  \n",
       "0                   0.021599  \n",
       "1                   0.216237  \n",
       "2                   0.240389  \n",
       "3                  -0.109199  \n",
       "5                   0.863322  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded['normalizedCreditScore'] = StandardScaler().fit_transform(df_encoded.CreditScore.values.reshape(-1,1))\n",
    "df_encoded['normalizedAge'] = StandardScaler().fit_transform(df_encoded.Age.values.reshape(-1,1))\n",
    "df_encoded['normalizedTenure'] = StandardScaler().fit_transform(df_encoded.Tenure.values.reshape(-1,1))\n",
    "df_encoded['normalizedBalance'] = StandardScaler().fit_transform(df_encoded.Balance.values.reshape(-1,1))\n",
    "df_encoded['normalizedEstimatedSalary'] = StandardScaler().fit_transform(df_encoded.EstimatedSalary.values.reshape(-1,1))\n",
    "df_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   NumOfProducts  HasCrCard  IsActiveMember  Exited  Gender_Male  \\\n",
       "0              1          1               1       1            0   \n",
       "1              1          0               1       0            0   \n",
       "2              3          1               0       1            0   \n",
       "3              2          0               0       0            0   \n",
       "5              2          1               0       1            1   \n",
       "\n",
       "   Geography_Germany  Geography_Spain  normalizedCreditScore  normalizedAge  \\\n",
       "0                  0                0              -0.326054       0.293570   \n",
       "1                  0                1              -0.439902       0.198222   \n",
       "2                  0                0              -1.536986       0.293570   \n",
       "3                  0                0               0.501933       0.007526   \n",
       "5                  0                1              -0.056958       0.484266   \n",
       "\n",
       "   normalizedTenure  normalizedBalance  normalizedEstimatedSalary  \n",
       "0         -1.041870          -1.225699                   0.021599  \n",
       "1         -1.387627           0.117505                   0.216237  \n",
       "2          1.032669           1.333214                   0.240389  \n",
       "3         -1.387627          -1.225699                  -0.109199  \n",
       "5          1.032669           0.597486                   0.863322  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s = df_encoded.drop([\"RowNumber\", \"CustomerId\", \"Surname\", \"CreditScore\", \"Age\", \"Tenure\", \"Balance\", \"EstimatedSalary\"], axis = 1)\n",
    "df_s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_s.drop(['Exited'], axis='columns')\n",
    "y = df_s.Exited"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Upsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_resample, y_resample = SMOTE().fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\Anaconda3\\envs\\mlops\\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='Exited', ylabel='count'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y_resample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_resample, y_resample, test_size=0.2, random_state=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12734, 11)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3184, 11)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15918, 12)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = pd.concat([X_resample, y_resample], axis=1)\n",
    "df_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "      <th>normalizedCreditScore</th>\n",
       "      <th>normalizedAge</th>\n",
       "      <th>normalizedTenure</th>\n",
       "      <th>normalizedBalance</th>\n",
       "      <th>normalizedEstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.326054</td>\n",
       "      <td>0.293570</td>\n",
       "      <td>-1.041870</td>\n",
       "      <td>-1.225699</td>\n",
       "      <td>0.021599</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.439902</td>\n",
       "      <td>0.198222</td>\n",
       "      <td>-1.387627</td>\n",
       "      <td>0.117505</td>\n",
       "      <td>0.216237</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.536986</td>\n",
       "      <td>0.293570</td>\n",
       "      <td>1.032669</td>\n",
       "      <td>1.333214</td>\n",
       "      <td>0.240389</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.501933</td>\n",
       "      <td>0.007526</td>\n",
       "      <td>-1.387627</td>\n",
       "      <td>-1.225699</td>\n",
       "      <td>-0.109199</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.056958</td>\n",
       "      <td>0.484266</td>\n",
       "      <td>1.032669</td>\n",
       "      <td>0.597486</td>\n",
       "      <td>0.863322</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NumOfProducts  HasCrCard  IsActiveMember  Gender_Male  Geography_Germany  \\\n",
       "0              1          1               1            0                  0   \n",
       "1              1          0               1            0                  0   \n",
       "2              3          1               0            0                  0   \n",
       "3              2          0               0            0                  0   \n",
       "4              2          1               0            1                  0   \n",
       "\n",
       "   Geography_Spain  normalizedCreditScore  normalizedAge  normalizedTenure  \\\n",
       "0                0              -0.326054       0.293570         -1.041870   \n",
       "1                1              -0.439902       0.198222         -1.387627   \n",
       "2                0              -1.536986       0.293570          1.032669   \n",
       "3                0               0.501933       0.007526         -1.387627   \n",
       "4                1              -0.056958       0.484266          1.032669   \n",
       "\n",
       "   normalizedBalance  normalizedEstimatedSalary  Exited  \n",
       "0          -1.225699                   0.021599       1  \n",
       "1           0.117505                   0.216237       0  \n",
       "2           1.333214                   0.240389       1  \n",
       "3          -1.225699                  -0.109199       0  \n",
       "4           0.597486                   0.863322       1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from pathlib import Path\n",
    "\n",
    "\n",
    "@dataclass(frozen=True)\n",
    "class DataTransformationConfig:\n",
    "    root_dir: Path\n",
    "    data_path: Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlProject.constants import *\n",
    "from mlProject.utils.common import read_yaml, create_directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConfigurationManager:\n",
    "    def __init__(\n",
    "        self,\n",
    "        config_filepath = CONFIG_FILE_PATH,\n",
    "        params_filepath = PARAMS_FILE_PATH,\n",
    "        schema_filepath = SCHEMA_FILE_PATH):\n",
    "\n",
    "        self.config = read_yaml(config_filepath)\n",
    "        self.params = read_yaml(params_filepath)\n",
    "        self.schema = read_yaml(schema_filepath)\n",
    "\n",
    "        create_directories([self.config.artifacts_root])\n",
    "\n",
    "\n",
    "    \n",
    "    def get_data_transformation_config(self) -> DataTransformationConfig:\n",
    "        config = self.config.data_transformation\n",
    "\n",
    "        create_directories([config.root_dir])\n",
    "\n",
    "        data_transformation_config = DataTransformationConfig(\n",
    "            root_dir=config.root_dir,\n",
    "            data_path=config.data_path,\n",
    "        )\n",
    "\n",
    "        return data_transformation_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from mlProject import logger\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataTransformation:\n",
    "    def __init__(self, config: DataTransformationConfig):\n",
    "        self.config = config\n",
    "\n",
    "\n",
    "    def fe_and_pre_process(self):\n",
    "        data = pd.read_csv(self.config.data_path)\n",
    "\n",
    "        df = data.dropna()\n",
    "        df.drop_duplicates(inplace=True)\n",
    "\n",
    "        df_encoded = pd.get_dummies(df, columns = ['Gender', 'Geography'], drop_first = True)\n",
    "\n",
    "        df_encoded['HasCrCard'] = df_encoded['HasCrCard'].astype(np.uint8)\n",
    "        df_encoded['IsActiveMember'] = df_encoded['IsActiveMember'].astype(np.uint8)\n",
    "        \n",
    "        df_encoded['normalizedCreditScore'] = StandardScaler().fit_transform(df_encoded.CreditScore.values.reshape(-1,1))\n",
    "        df_encoded['normalizedAge'] = StandardScaler().fit_transform(df_encoded.Age.values.reshape(-1,1))\n",
    "        df_encoded['normalizedTenure'] = StandardScaler().fit_transform(df_encoded.Tenure.values.reshape(-1,1))\n",
    "        df_encoded['normalizedBalance'] = StandardScaler().fit_transform(df_encoded.Balance.values.reshape(-1,1))\n",
    "        df_encoded['normalizedEstimatedSalary'] = StandardScaler().fit_transform(df_encoded.EstimatedSalary.values.reshape(-1,1))\n",
    "\n",
    "        df_scaled = df_encoded.drop([\"RowNumber\", \"CustomerId\", \"Surname\", \"CreditScore\", \"Age\", \"Tenure\", \"Balance\", \"EstimatedSalary\"], axis = 1)\n",
    "        \n",
    "        X = df_scaled.drop(['Exited'], axis='columns')\n",
    "        y = df_scaled.Exited\n",
    "\n",
    "        X_resample, y_resample = SMOTE().fit_resample(X, y)\n",
    "\n",
    "        data = pd.concat([X_resample, y_resample], axis=1)\n",
    "\n",
    "        # Split the data into training and test sets. (0.80, 0.20) split.\n",
    "        train, test = train_test_split(data, test_size=0.20, random_state=12)\n",
    "\n",
    "        train.to_csv(os.path.join(self.config.root_dir, \"train.csv\"),index = False)\n",
    "        test.to_csv(os.path.join(self.config.root_dir, \"test.csv\"),index = False)\n",
    "\n",
    "        logger.info(\"Feature Engineering and Pre-processing is done!\")\n",
    "        logger.info(train.shape)\n",
    "        logger.info(test.shape)\n",
    "\n",
    "        print(train.shape)\n",
    "        print(test.shape)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-06-27 20:47:48,509: INFO: common: yaml file: config\\config.yaml loaded successfully]\n",
      "[2024-06-27 20:47:48,511: INFO: common: yaml file: params.yaml loaded successfully]\n",
      "[2024-06-27 20:47:48,515: INFO: common: yaml file: schema.yaml loaded successfully]\n",
      "[2024-06-27 20:47:48,516: INFO: common: created directory at: artifacts]\n",
      "[2024-06-27 20:47:48,517: INFO: common: created directory at: artifacts/data_transformation]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\Anaconda3\\envs\\mlops\\lib\\site-packages\\pandas\\util\\_decorators.py:311: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-06-27 20:47:48,806: INFO: 2914486385: Feature Engineering and Pre-processing is done!]\n",
      "[2024-06-27 20:47:48,806: INFO: 2914486385: (12734, 12)]\n",
      "[2024-06-27 20:47:48,806: INFO: 2914486385: (3184, 12)]\n",
      "(12734, 12)\n",
      "(3184, 12)\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    config = ConfigurationManager()\n",
    "    data_transformation_config = config.get_data_transformation_config()\n",
    "    data_transformation = DataTransformation(config=data_transformation_config)\n",
    "    data_transformation.fe_and_pre_process()\n",
    "except Exception as e:\n",
    "    raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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