Kaustubh_Kamble_Customer_Churn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9e1dccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d31aaa5",
   "metadata": {},
   "source": [
    "#### Reading the CSV file and doing basic analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e5764ffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"customer_churn.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "75a4f431",
   "metadata": {
    "scrolled": true
   },
   "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>CustomerID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Location</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Customer_1</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>17</td>\n",
       "      <td>73.36</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Customer_2</td>\n",
       "      <td>62</td>\n",
       "      <td>Female</td>\n",
       "      <td>New York</td>\n",
       "      <td>1</td>\n",
       "      <td>48.76</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Customer_3</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>5</td>\n",
       "      <td>85.47</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Customer_4</td>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>Miami</td>\n",
       "      <td>3</td>\n",
       "      <td>97.94</td>\n",
       "      <td>297</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Customer_5</td>\n",
       "      <td>46</td>\n",
       "      <td>Female</td>\n",
       "      <td>Miami</td>\n",
       "      <td>19</td>\n",
       "      <td>58.14</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID        Name  Age  Gender     Location  \\\n",
       "0           1  Customer_1   63    Male  Los Angeles   \n",
       "1           2  Customer_2   62  Female     New York   \n",
       "2           3  Customer_3   24  Female  Los Angeles   \n",
       "3           4  Customer_4   36  Female        Miami   \n",
       "4           5  Customer_5   46  Female        Miami   \n",
       "\n",
       "   Subscription_Length_Months  Monthly_Bill  Total_Usage_GB  Churn  \n",
       "0                          17         73.36             236      0  \n",
       "1                           1         48.76             172      0  \n",
       "2                           5         85.47             460      0  \n",
       "3                           3         97.94             297      1  \n",
       "4                          19         58.14             266      0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b9c658ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Houston        20157\n",
       "Los Angeles    20041\n",
       "Miami          20031\n",
       "Chicago        19958\n",
       "New York       19813\n",
       "Name: Location, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Location.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e736c491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 9 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   CustomerID                  100000 non-null  int64  \n",
      " 1   Name                        100000 non-null  object \n",
      " 2   Age                         100000 non-null  int64  \n",
      " 3   Gender                      100000 non-null  object \n",
      " 4   Location                    100000 non-null  object \n",
      " 5   Subscription_Length_Months  100000 non-null  int64  \n",
      " 6   Monthly_Bill                100000 non-null  float64\n",
      " 7   Total_Usage_GB              100000 non-null  int64  \n",
      " 8   Churn                       100000 non-null  int64  \n",
      "dtypes: float64(1), int64(5), object(3)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "07051386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                    0\n",
       "Name                          0\n",
       "Age                           0\n",
       "Gender                        0\n",
       "Location                      0\n",
       "Subscription_Length_Months    0\n",
       "Monthly_Bill                  0\n",
       "Total_Usage_GB                0\n",
       "Churn                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values in the dataset\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "44148c8f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                      int64\n",
       "Name                           object\n",
       "Age                             int64\n",
       "Gender                         object\n",
       "Location                       object\n",
       "Subscription_Length_Months      int64\n",
       "Monthly_Bill                  float64\n",
       "Total_Usage_GB                  int64\n",
       "Churn                           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the datatypes of the features\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e1da59c",
   "metadata": {},
   "source": [
    "### Finding patterns in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1fa3127d",
   "metadata": {
    "scrolled": true
   },
   "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>Gender</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25272</td>\n",
       "      <td>24949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24944</td>\n",
       "      <td>24835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender  Female   Male\n",
       "Churn                \n",
       "0        25272  24949\n",
       "1        24944  24835"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with Gender column\n",
    "pd.crosstab(df.Churn, df.Gender)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de7d570f",
   "metadata": {},
   "source": [
    "* There is almost an equal distribution of Male and Female Customers being churned and not being churned\n",
    "* Number of Female Customers is very slightly more than the number of Male Customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ddc2a2f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of crosstab\n",
    "pd.crosstab(df.Churn, df.Gender).plot(kind=\"bar\", figsize=(10,6),color=[\"salmon\", \"lightblue\"])\n",
    "plt.title(\"Churn Frequency for Sex\")\n",
    "plt.xlabel(\"0 = Not Churned, 1 = Churned\")\n",
    "plt.ylabel(\"Number of Customers\")\n",
    "plt.legend([\"Female\",\"Male\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "333715ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Age\"].hist(bins=10, color='lightgreen', edgecolor='black')\n",
    "plt.title('Age Histogram')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "daa2aaf3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(data=df, x='Location', palette='deep')\n",
    "plt.title('Number of Customer for Each Location')\n",
    "plt.xlabel('Location')\n",
    "plt.ylabel('Number of Customers');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "580a847e",
   "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>Location</th>\n",
       "      <th>Chicago</th>\n",
       "      <th>Houston</th>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>Miami</th>\n",
       "      <th>New York</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10013</td>\n",
       "      <td>10258</td>\n",
       "      <td>10161</td>\n",
       "      <td>9955</td>\n",
       "      <td>9834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9945</td>\n",
       "      <td>9899</td>\n",
       "      <td>9880</td>\n",
       "      <td>10076</td>\n",
       "      <td>9979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Location  Chicago  Houston  Los Angeles  Miami  New York\n",
       "Churn                                                   \n",
       "0           10013    10258        10161   9955      9834\n",
       "1            9945     9899         9880  10076      9979"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare Churn column with Location column\n",
    "pd.crosstab(df.Churn, df.Location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "36709db9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Churn, df.Location).plot(kind=\"bar\", figsize=(10,6))\n",
    "plt.title(\"Churn Frequency for Location\")\n",
    "plt.xlabel(\"0 = Not Churned, 1 = Churned\")\n",
    "plt.ylabel(\"Number of Customers\")\n",
    "plt.legend([\"Chicago\", \"Houston\", \"Los Angeles\", \"Miami\", \"New York\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57896b54",
   "metadata": {},
   "source": [
    "#### Checking for non-numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "90c49324",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name\n",
      "Gender\n",
      "Location\n"
     ]
    }
   ],
   "source": [
    "for label, content in df.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a16bb871",
   "metadata": {},
   "outputs": [],
   "source": [
    "for label, content in df.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        df[label] = pd.Categorical(content).codes+ 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "27a1c016",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                      int64\n",
       "Name                            int32\n",
       "Age                             int64\n",
       "Gender                           int8\n",
       "Location                         int8\n",
       "Subscription_Length_Months      int64\n",
       "Monthly_Bill                  float64\n",
       "Total_Usage_GB                  int64\n",
       "Churn                           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0846ea27",
   "metadata": {},
   "source": [
    "#### Renaming \"Churn\" to \"target\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3079cc44",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'Churn':'target'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6483564d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50221\n",
       "1    49779\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A balanced binary distribution\n",
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52983b4a",
   "metadata": {},
   "source": [
    "#### Creating and Plotting a `Correlation Matrix` to better understand the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fe3065e8",
   "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>CustomerID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Location</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.818259</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.000131</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.005444</td>\n",
       "      <td>0.001265</td>\n",
       "      <td>-0.004025</td>\n",
       "      <td>-0.004586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>0.818259</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000948</td>\n",
       "      <td>0.003875</td>\n",
       "      <td>-0.004283</td>\n",
       "      <td>0.007357</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>-0.002791</td>\n",
       "      <td>-0.001418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.000948</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000832</td>\n",
       "      <td>-0.005889</td>\n",
       "      <td>0.003382</td>\n",
       "      <td>0.001110</td>\n",
       "      <td>0.001927</td>\n",
       "      <td>0.001559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>-0.000131</td>\n",
       "      <td>0.003875</td>\n",
       "      <td>0.000832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003314</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>0.002239</td>\n",
       "      <td>-0.001385</td>\n",
       "      <td>0.002121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <td>0.000062</td>\n",
       "      <td>-0.004283</td>\n",
       "      <td>-0.005889</td>\n",
       "      <td>-0.003314</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001768</td>\n",
       "      <td>0.003716</td>\n",
       "      <td>0.002834</td>\n",
       "      <td>0.006405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <td>0.005444</td>\n",
       "      <td>0.007357</td>\n",
       "      <td>0.003382</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>-0.001768</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005294</td>\n",
       "      <td>-0.002203</td>\n",
       "      <td>0.002328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <td>0.001265</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>0.001110</td>\n",
       "      <td>0.002239</td>\n",
       "      <td>0.003716</td>\n",
       "      <td>-0.005294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>-0.000211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <td>-0.004025</td>\n",
       "      <td>-0.002791</td>\n",
       "      <td>0.001927</td>\n",
       "      <td>-0.001385</td>\n",
       "      <td>0.002834</td>\n",
       "      <td>-0.002203</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.004586</td>\n",
       "      <td>-0.001418</td>\n",
       "      <td>0.001559</td>\n",
       "      <td>0.002121</td>\n",
       "      <td>0.006405</td>\n",
       "      <td>0.002328</td>\n",
       "      <td>-0.000211</td>\n",
       "      <td>-0.002842</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            CustomerID      Name       Age    Gender  \\\n",
       "CustomerID                    1.000000  0.818259 -0.001085 -0.000131   \n",
       "Name                          0.818259  1.000000 -0.000948  0.003875   \n",
       "Age                          -0.001085 -0.000948  1.000000  0.000832   \n",
       "Gender                       -0.000131  0.003875  0.000832  1.000000   \n",
       "Location                      0.000062 -0.004283 -0.005889 -0.003314   \n",
       "Subscription_Length_Months    0.005444  0.007357  0.003382  0.000320   \n",
       "Monthly_Bill                  0.001265  0.000353  0.001110  0.002239   \n",
       "Total_Usage_GB               -0.004025 -0.002791  0.001927 -0.001385   \n",
       "target                       -0.004586 -0.001418  0.001559  0.002121   \n",
       "\n",
       "                            Location  Subscription_Length_Months  \\\n",
       "CustomerID                  0.000062                    0.005444   \n",
       "Name                       -0.004283                    0.007357   \n",
       "Age                        -0.005889                    0.003382   \n",
       "Gender                     -0.003314                    0.000320   \n",
       "Location                    1.000000                   -0.001768   \n",
       "Subscription_Length_Months -0.001768                    1.000000   \n",
       "Monthly_Bill                0.003716                   -0.005294   \n",
       "Total_Usage_GB              0.002834                   -0.002203   \n",
       "target                      0.006405                    0.002328   \n",
       "\n",
       "                            Monthly_Bill  Total_Usage_GB    target  \n",
       "CustomerID                      0.001265       -0.004025 -0.004586  \n",
       "Name                            0.000353       -0.002791 -0.001418  \n",
       "Age                             0.001110        0.001927  0.001559  \n",
       "Gender                          0.002239       -0.001385  0.002121  \n",
       "Location                        0.003716        0.002834  0.006405  \n",
       "Subscription_Length_Months     -0.005294       -0.002203  0.002328  \n",
       "Monthly_Bill                    1.000000        0.003187 -0.000211  \n",
       "Total_Usage_GB                  0.003187        1.000000 -0.002842  \n",
       "target                         -0.000211       -0.002842  1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b1f14806",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "ax = sns.heatmap(corr_matrix, annot=True, linewidth=0.5, fmt=\".2f\", cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2af324a5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 0             1\n",
       "1             2\n",
       "2             3\n",
       "3             4\n",
       "4             5\n",
       "          ...  \n",
       "99995     99996\n",
       "99996     99997\n",
       "99997     99998\n",
       "99998     99999\n",
       "99999    100000\n",
       "Name: CustomerID, Length: 100000, dtype: int64>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No repition in CustomerID\n",
    "df['CustomerID'].unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6f8a7fe3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 0             1\n",
       "1         11113\n",
       "2         22224\n",
       "3         33335\n",
       "4         44446\n",
       "          ...  \n",
       "99995     99997\n",
       "99996     99998\n",
       "99997     99999\n",
       "99998    100000\n",
       "99999         6\n",
       "Name: Name, Length: 100000, dtype: int32>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No repition in Name\n",
    "df['Name'].unique"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31dc7432",
   "metadata": {},
   "source": [
    "* All the values of **Name** and **CustomerId** are unique in the dataset\n",
    "* As a result, we could drop both the features to get optimal computation time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f7434f72",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                      int64\n",
       "Name                            int32\n",
       "Age                             int64\n",
       "Gender                           int8\n",
       "Location                         int8\n",
       "Subscription_Length_Months      int64\n",
       "Monthly_Bill                  float64\n",
       "Total_Usage_GB                  int64\n",
       "target                          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7c674475",
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Location</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>73.36</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>48.76</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>85.47</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>97.94</td>\n",
       "      <td>297</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>58.14</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Location  Subscription_Length_Months  Monthly_Bill  \\\n",
       "0   63       2         3                          17         73.36   \n",
       "1   62       1         5                           1         48.76   \n",
       "2   24       1         3                           5         85.47   \n",
       "3   36       1         4                           3         97.94   \n",
       "4   46       1         4                          19         58.14   \n",
       "\n",
       "   Total_Usage_GB  target  \n",
       "0             236       0  \n",
       "1             172       0  \n",
       "2             460       0  \n",
       "3             297       1  \n",
       "4             266       0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop([\"Name\",\"CustomerID\"], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23882fb1",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ee3a5df",
   "metadata": {},
   "source": [
    "#### Creating X & y and splitting the data into training and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "34756c04",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('target', axis=1)\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c9509ed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9ba46b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((80000, 6), (20000, 6))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5c7d9cf3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((80000,), (20000,))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "174926f4",
   "metadata": {},
   "source": [
    "#### Fitting 4 different Classification Estimators on the data and storing the scores in `model_scores`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a3808b83",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_scores = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c721d8",
   "metadata": {},
   "source": [
    "**Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "13d93903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 375 ms\n",
      "Wall time: 249 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "model_scores[\"LogisticRegression\"] = lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3017b6",
   "metadata": {},
   "source": [
    "**SGC Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4069d045",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 234 ms\n",
      "Wall time: 248 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)  # Don't cheat - fit only on training data\n",
    "X_train_scaler = scaler.transform(X_train)\n",
    "X_test_scaler = scaler.transform(X_test)  # apply same transformation to test data\n",
    "\n",
    "\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train_scaler, y_train)\n",
    "model_scores[\"SGDClassifier\"] = sgd.score(X_test_scaler, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec70e60",
   "metadata": {},
   "source": [
    "**KNeighborsClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b9541614",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 781 ms\n",
      "Wall time: 977 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "model_scores[\"KNeighborsClassifier\"] = knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d83c52f",
   "metadata": {},
   "source": [
    "**RandomForestClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b9a98161",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 43.1 s\n",
      "Wall time: 7.08 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(n_jobs=-1)\n",
    "clf.fit(X_train, y_train)\n",
    "model_scores['RandomForestClassifier'] = clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "15986883",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.4956,\n",
       " 'SGDClassifier': 0.4967,\n",
       " 'KNeighborsClassifier': 0.502,\n",
       " 'RandomForestClassifier': 0.4975}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the accuracy scores for the Models\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1451592",
   "metadata": {},
   "source": [
    "**Plotting the scores using bar graph** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9d32d3f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# model_scores_df.plot.bar()\n",
    "model_scores_df = pd.DataFrame(model_scores, index=['accuracy']).T\n",
    "model_scores_df = model_scores_df.sort_values(by=\"accuracy\", ascending=False)\n",
    "model_scores_df\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(model_scores_df.index, model_scores_df['accuracy'])\n",
    "plt.title(\"Accuracy for Baseline Models\")\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3416741f",
   "metadata": {},
   "source": [
    "### Computing cross validated metrics using `cross_val_score`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a62e92ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def cross_validated_metrics(model, X, y):\n",
    "    cvm = []\n",
    "    cvm.append(np.mean(cross_val_score(model, X, y, cv=5, scoring=\"accuracy\")))\n",
    "    cvm.append(np.mean(cross_val_score(model, X, y, cv=5, scoring=\"precision\")))\n",
    "    cvm.append(np.mean(cross_val_score(model, X, y, cv=5, scoring=\"recall\")))\n",
    "    cvm.append(np.mean(cross_val_score(model, X, y, cv=5, scoring=\"f1\")))\n",
    "    return cvm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "df6104e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Storing Cross-Validated Metrics\n",
    "cv_metrics = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d41dab37",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 2min 5s\n",
      "Wall time: 4min 26s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "cv_metrics['RandomForestClassifier'] = cross_validated_metrics(clf, X, y)\n",
    "cv_metrics['KNeighborsClassifier'] = cross_validated_metrics(knn, X, y)\n",
    "cv_metrics['SGDClassifier'] = cross_validated_metrics(sgd, X, y)\n",
    "cv_metrics['LogisticRegression'] = cross_validated_metrics(lr, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bb16e6c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RandomForestClassifier': [0.49776,\n",
       "  0.49692503369461055,\n",
       "  0.4722675543360147,\n",
       "  0.48370202139830837],\n",
       " 'KNeighborsClassifier': [0.50096,\n",
       "  0.4987526003237927,\n",
       "  0.49563068561439294,\n",
       "  0.49718412514086363],\n",
       " 'SGDClassifier': [0.49808,\n",
       "  0.42631496894409937,\n",
       "  0.5999598232221776,\n",
       "  0.4034006838446539],\n",
       " 'LogisticRegression': [0.50044,\n",
       "  0.49768152716304054,\n",
       "  0.31627851042830546,\n",
       "  0.3854323886587595]}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0d2a131f",
   "metadata": {
    "scrolled": true
   },
   "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>RandomForestClassifier</th>\n",
       "      <th>KNeighborsClassifier</th>\n",
       "      <th>SGDClassifier</th>\n",
       "      <th>LogisticRegression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.497760</td>\n",
       "      <td>0.500960</td>\n",
       "      <td>0.498080</td>\n",
       "      <td>0.500440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.496925</td>\n",
       "      <td>0.498753</td>\n",
       "      <td>0.426315</td>\n",
       "      <td>0.497682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.472268</td>\n",
       "      <td>0.495631</td>\n",
       "      <td>0.599960</td>\n",
       "      <td>0.316279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.483702</td>\n",
       "      <td>0.497184</td>\n",
       "      <td>0.403401</td>\n",
       "      <td>0.385432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RandomForestClassifier  KNeighborsClassifier  SGDClassifier  \\\n",
       "Accuracy                 0.497760              0.500960       0.498080   \n",
       "Precision                0.496925              0.498753       0.426315   \n",
       "Recall                   0.472268              0.495631       0.599960   \n",
       "F1                       0.483702              0.497184       0.403401   \n",
       "\n",
       "           LogisticRegression  \n",
       "Accuracy             0.500440  \n",
       "Precision            0.497682  \n",
       "Recall               0.316279  \n",
       "F1                   0.385432  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvm_df = pd.DataFrame(cv_metrics, index=[\"Accuracy\",\"Precision\",\"Recall\",\"F1\"])\n",
    "cvm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "68df6563",
   "metadata": {},
   "outputs": [],
   "source": [
    "cvm_col = list(cvm_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ef3e896b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvm_df.plot.bar(title=\"Cross Validated Metrics for the Models\", legend=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0a4f8f",
   "metadata": {},
   "source": [
    "* In a customer churn context, **high precision** means that when the model predicts a customer will churn, it is likely to be correct. You want to avoid falsely flagging customers as churning when they are not because this can result in unnecessary retention efforts or negative customer experiences.\n",
    "\n",
    "* In a customer churn context, **high recall** means that the model is effective at capturing most of the customers who are truly churning. This can be important if retaining customers is a high priority and you want to avoid missing potential churn cases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0fcaef",
   "metadata": {},
   "source": [
    "* Based on the provided metrics, the SGDClassifier appears to have the highest recall , which means it's better at identifying true churn cases. However, it has relatively low precision, meaning it may generate more false positives.\n",
    "\n",
    "* The RandomForestClassifier also has a decent balance between precision and recall\n",
    "* The KNeighborsClassifier has the highest precision but a lower recall, which means it's better at avoiding false positives but might miss some true churn cases.\n",
    "\n",
    "* The LogisticRegression model has the lowest performance in terms of both precision and recall."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e20c1ae",
   "metadata": {},
   "source": [
    "* Eliminating LogisticRegression because of low precision and reacll\n",
    "* Eliminating Sgdclassifier because of low precision\n",
    "* Choosing **RandomForestClassifier** and **KNeigborsClassifier** for further tuning because they have a balance of precision and recall"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bf6a491",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning on KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "336a4c9f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 25s\n",
      "Wall time: 1min 54s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Tuning KNN\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "recalls = []\n",
    "precisions = []\n",
    "\n",
    "# Creating a list of different values for K-Neighbors\n",
    "neighbors = range(5,100,2)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "max_r=0\n",
    "max_p=0\n",
    "\n",
    "max_recall=0\n",
    "max_precision=0\n",
    "\n",
    "# Loop through different n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "    \n",
    "    knn.fit(X_train, y_train)\n",
    "    knn.score(X_test, y_test)\n",
    "    \n",
    "    y_preds = knn.predict(X_test)\n",
    "    \n",
    "    recall = recall_score(y_test, y_preds)\n",
    "    precision = precision_score(y_test, y_preds)\n",
    "\n",
    "    recalls.append(recall)\n",
    "    precisions.append(precision)\n",
    "    \n",
    "    if recall > max_recall:\n",
    "        max_recall = recall\n",
    "        max_r = i\n",
    "    \n",
    "    if precision > max_precision:\n",
    "        max_precision = precision\n",
    "        max_p = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "05e998b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Recall score on the test data is for n_neighbors = 23\n",
      "Maximum Precision score on the test data is for n_neighbors = 85\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, recalls, label=\"Recall Score\")\n",
    "plt.plot(neighbors, precisions, label=\"Precision Score\")\n",
    "plt.xticks(np.arange(1,100,10))\n",
    "plt.xlabel(\"Number of Neighbors\")\n",
    "plt.ylabel(\"Model Score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum Recall score on the test data is for n_neighbors = {max_r}\")\n",
    "print(f\"Maximum Precision score on the test data is for n_neighbors = {max_p}\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f0c9ef93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5073"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=max_r)\n",
    "knn.fit(X_train, y_train)\n",
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "aa2ed82f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score, accuracy_score\n",
    "\n",
    "knn_y_preds = knn.predict(X_test)\n",
    "\n",
    "knn_recall = recall_score(y_test, knn_y_preds)\n",
    "knn_precision = precision_score(y_test, knn_y_preds)\n",
    "knn_accuracy = accuracy_score(y_test, knn_y_preds)\n",
    "knn_f1 = f1_score(y_test, knn_y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "93f49bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.49667807529695995, 0.5040866366979976, 0.5073, 0.5003549335767163)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_recall, knn_precision, knn_accuracy, knn_f1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8282f7f9",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning on RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0622eb39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n",
      "CPU times: total: 50.5 s\n",
      "Wall time: 1min 16s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=2,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  5,  9, 13, 17]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  6, 10, 14, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([100, 300, 500, 700, 900]),\n",
       "                                        &#x27;n_jobs&#x27;: [-1]},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=2,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  5,  9, 13, 17]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  6, 10, 14, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([100, 300, 500, 700, 900]),\n",
       "                                        &#x27;n_jobs&#x27;: [-1]},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=2,\n",
       "                   param_distributions={'max_depth': [None, 3, 5],\n",
       "                                        'min_samples_leaf': array([ 1,  5,  9, 13, 17]),\n",
       "                                        'min_samples_split': array([ 2,  6, 10, 14, 18]),\n",
       "                                        'n_estimators': array([100, 300, 500, 700, 900]),\n",
       "                                        'n_jobs': [-1]},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "# Create hyperparameter grid for RandomForestClassifer\n",
    "rf_grid = {\n",
    "    \"n_jobs\" : [-1],\n",
    "    \"n_estimators\" : np.arange(100,1000,200),\n",
    "    \"max_depth\" : [None, 3,5],\n",
    "    \"min_samples_split\" : np.arange(2,20,4),\n",
    "    \"min_samples_leaf\" : np.arange(1,20,4)\n",
    "}\n",
    "\n",
    "\n",
    "# Setup grid hyperparameter search for RandomForestClassifier\n",
    "rfc_t = RandomizedSearchCV(RandomForestClassifier(), param_distributions=rf_grid, cv=5, n_iter=2, verbose=True)\n",
    "\n",
    "# Fit the hyperparameter search model\n",
    "rfc_t.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fcdd045c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_jobs': -1,\n",
       " 'n_estimators': 700,\n",
       " 'min_samples_split': 14,\n",
       " 'min_samples_leaf': 13,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_t.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "470b7ccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4995"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_t.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4c8e9bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_jobs=-1, n_estimators=900, min_samples_split=18, min_samples_leaf=1, max_depth=5)\n",
    "rfc.fit(X_train, y_train)\n",
    "rfc_y_preds = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7c8641c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score\n",
    "\n",
    "rfc_recall = recall_score(y_test, rfc_y_preds)\n",
    "rfc_precision = precision_score(y_test, rfc_y_preds)\n",
    "rfc_accuracy = accuracy_score(y_test, rfc_y_preds)\n",
    "rfc_f1= f1_score(y_test, rfc_y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c6373638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.33480974431246224, 0.5000751766651631, 0.50335, 0.4010853180584866)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_recall, rfc_precision, rfc_accuracy, rfc_f1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4230707",
   "metadata": {},
   "source": [
    "**Storing the metrics for the Tuned Classifiers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "78b8dcdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_metrics = {}\n",
    "new_metrics['KNeighborsClassifier'] = [knn_accuracy, knn_precision, knn_recall, knn_f1]\n",
    "new_metrics['RandomForestClassifier'] = [rfc_accuracy, rfc_precision, rfc_recall, rfc_f1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4a4dca60",
   "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>KNeighborsClassifier</th>\n",
       "      <th>RandomForestClassifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.507300</td>\n",
       "      <td>0.503350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.504087</td>\n",
       "      <td>0.500075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.496678</td>\n",
       "      <td>0.334810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.500355</td>\n",
       "      <td>0.401085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           KNeighborsClassifier  RandomForestClassifier\n",
       "Accuracy               0.507300                0.503350\n",
       "Precision              0.504087                0.500075\n",
       "Recall                 0.496678                0.334810\n",
       "F1                     0.500355                0.401085"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_metrics_df = pd.DataFrame(new_metrics, index=[\"Accuracy\",\"Precision\",\"Recall\",\"F1\"])\n",
    "new_metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3e02c4d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_metrics_df.plot.bar(title=\"Metrics for Tuned Models\", legend=True);\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "523675f8",
   "metadata": {},
   "source": [
    "## Evaluating tuned KNeighborClassifier estimator\n",
    "\n",
    "* ROC Curve\n",
    "* Confusion Matrix\n",
    "* Classification Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cb2c99",
   "metadata": {},
   "source": [
    "**ROC Curve**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5f734d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x2480291efd0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "# Plottin ROC curve and calculating the AUC metric\n",
    "RocCurveDisplay.from_predictions(y_test, knn_y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137ef6e4",
   "metadata": {},
   "source": [
    "**Confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "167490e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5212 4854]\n",
      " [5000 4934]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "print(confusion_matrix(y_test, knn_y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d2f10a1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,4))\n",
    "ax = sns.heatmap(confusion_matrix(y_test, knn_y_preds),annot=True, cbar=False)\n",
    "plt.xlabel('True Label')\n",
    "plt.ylabel('Predicted Label');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b5bfd99",
   "metadata": {},
   "source": [
    "**Classification Report**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9302d6dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.52      0.51     10066\n",
      "           1       0.50      0.50      0.50      9934\n",
      "\n",
      "    accuracy                           0.51     20000\n",
      "   macro avg       0.51      0.51      0.51     20000\n",
      "weighted avg       0.51      0.51      0.51     20000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, knn_y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "882790fd",
   "metadata": {},
   "source": [
    "# Exporting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f9ad2783",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier(n_neighbors=23)\n"
     ]
    }
   ],
   "source": [
    "print(knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6babe137",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(knn, open('model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "effde7a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['knn_model.pkl']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(knn, 'knn_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "44839cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    50216\n",
       "2    49784\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "87b4ab96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    20157\n",
       "3    20041\n",
       "4    20031\n",
       "1    19958\n",
       "5    19813\n",
       "Name: Location, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Location.value_counts()"
   ]
  }
 ],
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