Health Insurance Cross Sell Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b0717be",
   "metadata": {},
   "source": [
    "# Health Insurance Cross Sell Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c51123d",
   "metadata": {},
   "source": [
    "In this notebook, we will apply different Machine Learning methods to predict wether customers who hold a health insurance are also interested in taking a car insurance.\n",
    "\n",
    "For this purpose, we will use a dataset which is available on kaggle (https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction). It holds information about the customers in a csv file, as well as information about wether a customer is interested in a car insurance. The original dataset is already split into training and test data, however we will only use the training dataset here and treat it as if it was the complete dataset, since we do not have access to the target labels in the original test dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa2bb2fb",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0bbfb80",
   "metadata": {},
   "source": [
    "Before doing anything, we need to import the libraries needed for the task. We will use numpy and pandas, two standard libraries for working with data structures, as well as matplotlib for visualisation, sklearn for providing Machine Learning methods and tabulate for displaying results in tabular form:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24c182ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "from tabulate import tabulate\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import svm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import recall_score, precision_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d923645b",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c1f536",
   "metadata": {},
   "source": [
    "The data is conveniently stored in a single csv file which we can open as a pandas dataframe. The resulting dataframe contains 381109 rows and 12 columns:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb73415f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(381109, 12)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = './data/data.csv'\n",
    "data = pd.read_csv(data_path)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bcd0854",
   "metadata": {},
   "source": [
    "In the data description on the kaggle website the meaning of the individual columns is explained. Our task is to predict the variable 'Response', which is a binary variable indicating if a customer is interested in taking a car insurance or not. Let's have a look at the first rows of the dataframe and the data type of each column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86275325",
   "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>id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0   1    Male   44                1         28.0                   0   \n",
       "1   2    Male   76                1          3.0                   0   \n",
       "2   3    Male   47                1         28.0                   0   \n",
       "3   4    Male   21                1         11.0                   1   \n",
       "4   5  Female   29                1         41.0                   1   \n",
       "\n",
       "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
       "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
       "1    1-2 Year             No         33536.0                  26.0      183   \n",
       "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
       "3    < 1 Year             No         28619.0                 152.0      203   \n",
       "4    < 1 Year             No         27496.0                 152.0       39   \n",
       "\n",
       "   Response  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "71bdc06e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        int64\n",
       "Gender                   object\n",
       "Age                       int64\n",
       "Driving_License           int64\n",
       "Region_Code             float64\n",
       "Previously_Insured        int64\n",
       "Vehicle_Age              object\n",
       "Vehicle_Damage           object\n",
       "Annual_Premium          float64\n",
       "Policy_Sales_Channel    float64\n",
       "Vintage                   int64\n",
       "Response                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee24e78a",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137473fb",
   "metadata": {},
   "source": [
    "An important step in any Machine Learning task is cleaning the data and bringing it into the correct form. Some of the variables are not encoded correctly yet. We will now adjust the encoding of those variables so that they are better suitable for Machine Learning purposes:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d373a4",
   "metadata": {},
   "source": [
    "Starting with the 'Gender' column, it is currently encoded as a string, however for the purposes of Machine Learning it would be more practicable as a numeric variable. Thus we change the type and values of the column accordingly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d7d0431e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Gender'] = data['Gender'].map({'Male': 0, 'Female': 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7ecb6c",
   "metadata": {},
   "source": [
    "The 'Region Code' column holds an identifier for the customer's location. Treating it as a numerical variable would be unreasonable since it is not a quantitative measure. This variable is better represented as a string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "68063191",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Region_Code'] = data['Region_Code'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14a2a105",
   "metadata": {},
   "source": [
    "Same as for the  gender information, the 'Vehicle Damage' column indicating if a car was damaged in the past is better suited in numerical form:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "217042b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Vehicle_Damage'] = data['Vehicle_Damage'].map({'No': 0, 'Yes': 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaa89528",
   "metadata": {},
   "source": [
    "Same as for the regional code, the 'Policy_Sales_Channel' column is an identifier rather than a quantitative measure, and should therefore be changed to a string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2375084",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Policy_Sales_Channel'] = data['Policy_Sales_Channel'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45feb771",
   "metadata": {},
   "source": [
    "Having performed the necessary data cleaning steps, our dataset now looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7a301438",
   "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>id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>1</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>0</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>1</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>0</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>0</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  Gender  Age  Driving_License Region_Code  Previously_Insured  \\\n",
       "0   1       0   44                1        28.0                   0   \n",
       "1   2       0   76                1         3.0                   0   \n",
       "2   3       0   47                1        28.0                   0   \n",
       "3   4       0   21                1        11.0                   1   \n",
       "4   5       1   29                1        41.0                   1   \n",
       "\n",
       "  Vehicle_Age  Vehicle_Damage  Annual_Premium Policy_Sales_Channel  Vintage  \\\n",
       "0   > 2 Years               1         40454.0                 26.0      217   \n",
       "1    1-2 Year               0         33536.0                 26.0      183   \n",
       "2   > 2 Years               1         38294.0                 26.0       27   \n",
       "3    < 1 Year               0         28619.0                152.0      203   \n",
       "4    < 1 Year               0         27496.0                152.0       39   \n",
       "\n",
       "   Response  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "25acc2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        int64\n",
       "Gender                    int64\n",
       "Age                       int64\n",
       "Driving_License           int64\n",
       "Region_Code              object\n",
       "Previously_Insured        int64\n",
       "Vehicle_Age              object\n",
       "Vehicle_Damage            int64\n",
       "Annual_Premium          float64\n",
       "Policy_Sales_Channel     object\n",
       "Vintage                   int64\n",
       "Response                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb62552d",
   "metadata": {},
   "source": [
    "## Splitting into training and test set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d052ab25",
   "metadata": {},
   "source": [
    "Next, we will split the data set in a training set and a test set, so that we can evaluate our results later. Note that in the original kaggle dataset there is already a test set, however we don't have access to the labels there and therefore we have to create our own test set. We see that our training set holds 304887 rows, and our test set holds 76222 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3c6e2c34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(304887, 12)\n",
      "(76222, 12)\n"
     ]
    }
   ],
   "source": [
    "d_train, d_test = train_test_split(data, test_size=0.2)\n",
    "\n",
    "print(d_train.shape)\n",
    "print(d_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b64322",
   "metadata": {},
   "source": [
    "## Exploratory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b87e900c",
   "metadata": {},
   "source": [
    "As a first step of analysis it is often useful to visualize the distribution the individual variables. This can help\n",
    "getting an overview about the characteristics of the dataset and spotting potential pitfalls that might occur later in the analysis. Since in our case the explained variable is binary, we can additionally use a stacked histogram to get a sense for potential relationships between single explanatory variables and the explained variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b59ff1dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split dataset by response variable\n",
    "d_train_0 = d_train[d_train['Response'] == 0]\n",
    "d_train_1 = d_train[d_train['Response'] == 1]\n",
    "\n",
    "# Set up plot matrix\n",
    "gridsize_y = 4\n",
    "gridsize_x = 3\n",
    "fig, axs = plt.subplots(gridsize_y, gridsize_x, figsize=(12,12))\n",
    "\n",
    "# Generate individual plots with axis label and legend\n",
    "for i,varname in enumerate(d_train.columns):\n",
    "    pos_y = i%gridsize_y\n",
    "    pos_x = int(i/gridsize_y)\n",
    "    axs[pos_y, pos_x].hist(x=[d_train_0[varname],d_train_1[varname]],\n",
    "                           histtype='barstacked',\n",
    "                           label=['not interested', 'interested'])\n",
    "    axs[pos_y, pos_x].set_xlabel(varname)\n",
    "    handles, labels = axs[pos_y, pos_x].get_legend_handles_labels() \n",
    "    axs[pos_y, pos_x].legend(handles[::-1], labels[::-1])\n",
    "    \n",
    "# Avoid overlapping text from axis labels\n",
    "axs[0,1].set_xticklabels([])\n",
    "axs[0,2].set_xticks([100000,300000,500000])\n",
    "axs[1,2].set_xticklabels([])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5786554b",
   "metadata": {},
   "source": [
    "We can see that some of the variables are quite uniformly distributed (e.g. 'Vintage'), whereas others are very heterogenous (e.g. 'Age', 'Policy Sales Channel'). As for potential pitfalls, we see that almost every customer in the dataset has a driver's license, meaning that even if the 'Driving_License' variable was a good predictor for our response variable, it would be of very limited use for the Machine Learning task.\n",
    "\n",
    "Moreover, the stacked histogram reveals that the two variables 'Previously_Insured' and 'Vehicle_Damage' have a visible impact on the response variable. This intuitively makes sense: A customer who already experienced car damage in the past will be more likely to take a car insurance, and a customer who already has a car insurance will be less likely to do so. Another relationship stands out that is less intuitive: Judging from the 'Vehicle_Age' variable, it appears that customers whose vehicle is between 1 and 2 years old are much more likely to take an insurance than customers whose car is less than one year old. Having a closer look, we see that customers whose car is older than 2 years have an even higher chance of being interested in an insurance. So it seems that the readiness to take an insurance increases with the age of the customer's car.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "645fac18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04347628014335176\n",
      "0.17398138676114674\n",
      "0.29058832672728685\n"
     ]
    }
   ],
   "source": [
    "# Closer look at Vehicle_Age: Calculate proportions of interested customers for each value of Vehicle_Age\n",
    "\n",
    "# Split up dataframes by vehicle age\n",
    "d_new = d_train[d_train['Vehicle_Age']=='< 1 Year']\n",
    "d_middle = d_train[d_train['Vehicle_Age']=='1-2 Year']\n",
    "d_old = d_train[d_train['Vehicle_Age']=='> 2 Years']\n",
    "\n",
    "# Extract only interested customers\n",
    "d_new_1 = d_new[d_new['Response']==1]\n",
    "d_middle_1 = d_middle[d_middle['Response']==1]\n",
    "d_old_1 = d_old[d_old['Response']==1]\n",
    "\n",
    "# Calculate proportions\n",
    "interested_chance_new = len(d_new_1)/len(d_new)\n",
    "interested_chance_middle = len(d_middle_1)/len(d_middle)\n",
    "interested_chance_old = len(d_old_1)/len(d_old)\n",
    "\n",
    "print(interested_chance_new)\n",
    "print(interested_chance_middle)\n",
    "print(interested_chance_old)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c91a2bf2",
   "metadata": {},
   "source": [
    "## More Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d73712cc",
   "metadata": {},
   "source": [
    "For being able to use a nominal variable in a Machine Learning model, it is possible to transform it into a set of binary variables by adding a new binary variable for each state of the original variable, indicating if the variable for this sample has this state or not. This process is known as one-hot encoding. We will now one-hot encode the 'Vehicle_Age' variable. The other two nominal variables 'Region_Encoding' and 'Policy_Sales_Channel' we will not use in the further analysis, since one-hot encoding them would increase the number of variables by a huge amount. We also will exclude the 'Driving_License' variable for the reasons described above and the 'id' variable since it does not contain meaningful information for out task. Furthermore, we rescale the continuous variables 'Annual_Premium' and 'Age' logarithmically to make them more normally distributed, and we subsequently apply a minmax scaling to all continuous variables.\n",
    "\n",
    "The variables are then split up into the predictors and the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4bbd8d65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-Hot encode Vehicle Age\n",
    "d_train_onehot = pd.get_dummies(data=d_train, columns=['Vehicle_Age'], prefix='V_Age')\n",
    "\n",
    "def scale_log(x):\n",
    "    x_rescaled = np.log(x)\n",
    "    return x_rescaled\n",
    "\n",
    "def scale_minmax(x):\n",
    "    x_rescaled = (x - np.min(x)) / (np.max(x) - np.min(x))\n",
    "    return x_rescaled\n",
    "\n",
    "# Rescale non-binary columns\n",
    "d_train_onehot['Annual_Premium'] = scale_log(d_train_onehot['Annual_Premium'])\n",
    "d_train_onehot['Age'] = scale_log(d_train_onehot['Age'])\n",
    "d_train_onehot['Annual_Premium'] = scale_minmax(d_train_onehot['Annual_Premium'])\n",
    "d_train_onehot['Age'] = scale_minmax(d_train_onehot['Age'])\n",
    "d_train_onehot['Vintage'] = scale_minmax(d_train_onehot['Vintage'])\n",
    "\n",
    "\n",
    "cols_to_drop = ['id', 'Driving_License', 'Region_Code', \n",
    "                'Policy_Sales_Channel','Response']\n",
    "\n",
    "\n",
    "# Split into features and labels\n",
    "X_train = d_train_onehot.drop(cols_to_drop, axis=1) \n",
    "y_train = d_train_onehot['Response']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20eed1a",
   "metadata": {},
   "source": [
    "## Train models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91dbe634",
   "metadata": {},
   "source": [
    "Having prepared the training data adequately, we will now apply a number of common classification methods to our task. Each of the classifiers can be trained with just two lines of code using the scikit-learn library. I will shortly explain each of the classifiers below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f08615",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3875aa9",
   "metadata": {},
   "source": [
    "\n",
    "Since our target variable is binary rather than continuous, we cannot use a linear regression to model it. Instead, the logistic regression is used in such cases. It models the log-odds of an event as a linear combination of the predictors. The log-odds is a function that maps probability values to real numbers. The resulting probabilities can then be used to predict the target variable by defining a treshold probability and classifying all observations above that treshold as positive and all observations below as negative. The estimation of the parameters is usually done by the maximum-likelihood method.\n",
    "\n",
    "Furthermore, we use balanced class weights to make sure that the model doesn't only optimize the majority class at the cost of the minority class.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e2fb3c45",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(class_weight='balanced')\n",
    "lr_clf = lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c86f3131",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "750efc0d",
   "metadata": {},
   "source": [
    "Support Vector Machines construct a hyperplane that divides the two classes by maximizing the hyperplane's distance to the nearest training samples. Training the model then means finding the parameters of the hyperplane. Once trained, the hyperplane then serves as a decision boundary for the prediction on new samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c248fb8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(class_weight=&#x27;balanced&#x27;)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(class_weight=&#x27;balanced&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(class_weight='balanced')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: Training of the SVM on a local machine can take several hours\n",
    "svm_clf = svm.SVC(class_weight='balanced')\n",
    "svm_clf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21fbec3a",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f64e97",
   "metadata": {},
   "source": [
    "Random Forests are an ensemble method, meaning that they fit multiple classifiers and then generate the final prediction by averaging among those classifiers. In the case of Random Forest, those classifiers are decision trees that are fitted on different subsets of the training data. Decision trees aim to subsequently split the data based on an attribute value such that the different classes are separated best. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4a0fc6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#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>RandomForestClassifier(class_weight=&#x27;balanced&#x27;)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier(class_weight='balanced')\n",
    "rf_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4334b0df",
   "metadata": {},
   "source": [
    "### Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7deef84a",
   "metadata": {},
   "source": [
    "The Naive Bayes method estimates the probability of the target variable via maximum likelihood estimation. It assumes that the predictor variables are all independent of each other which reduces the number of parameters considerably. Because of this underlying assumption, it is not possible to adjust class weights for this method. One could alternatively reduce the class imbalance by resampling from the minority samples. For now, we will keep using the same training data for each classifier to maintain comparability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "83d4258c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#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>GaussianNB()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb_clf = GaussianNB()\n",
    "gnb_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17409297",
   "metadata": {},
   "source": [
    "### K Nearest Neighbor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "240c7e31",
   "metadata": {},
   "source": [
    "K Nearest Neighbor is a very straightforward classifier that simply determines the class of an unknown sample by taking the majority vote of the classes from the k nearest training samples, according to a given distance metric. There is again no parameter for the class weights. We will instead assign weights to the training sample based on their distance to the  target sample, so that samples further away contribute less. This is not the same mechanism as in the methods above, but might still reduce the impact of the class imbalance to some degree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f6f7b1f5",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "KNeighborsClassifier(weights='distance')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf = KNeighborsClassifier(weights='distance')\n",
    "knn_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a7b83d",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "669ac8bc",
   "metadata": {},
   "source": [
    "Let's see how well the different models perform on unseen test data. For this, we have to one-hot encode, rescale and split the test data in the same way as we did with the training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e90e7684",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-Hot encode Vehicle Age\n",
    "d_test_onehot = pd.get_dummies(data=d_test, columns=['Vehicle_Age'], prefix='V_Age')\n",
    "\n",
    "def scale_log(x):\n",
    "    x_rescaled = np.log(x)\n",
    "    return x_rescaled\n",
    "\n",
    "def scale_minmax(x):\n",
    "    x_rescaled = (x - np.min(x)) / (np.max(x) - np.min(x))\n",
    "    return x_rescaled\n",
    "\n",
    "# Rescale columns\n",
    "d_test_onehot['Annual_Premium'] = scale_log(d_test_onehot['Annual_Premium'])\n",
    "d_test_onehot['Age'] = scale_log(d_test_onehot['Age'])\n",
    "d_test_onehot['Annual_Premium'] = scale_minmax(d_test_onehot['Annual_Premium'])\n",
    "d_test_onehot['Age'] = scale_minmax(d_test_onehot['Age'])\n",
    "d_test_onehot['Vintage'] = scale_minmax(d_test_onehot['Vintage'])\n",
    "\n",
    "cols_to_drop = ['id', 'Driving_License', 'Region_Code', \n",
    "                'Policy_Sales_Channel','Response']\n",
    "\n",
    "# Split into features and labels\n",
    "X_test = d_test_onehot.drop(cols_to_drop, axis=1) \n",
    "y_test = d_test_onehot['Response']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd9b2dfd",
   "metadata": {},
   "source": [
    "We will calculate the accuracy, the precision and the recall of each model. The accuracy measures the fraction of the samples that were classified correctly. The precision measures the fraction of the predicted positives that were classified correctly, and the recall measures the fraction of the true positives that were classified correctly. It is generally useful to report both precision and recall to reveal different aspects of the model, for example how biased the model is towards predicting the majority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "066e4d57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier             Accuracy    Precision    Recall\n",
      "-------------------  ----------  -----------  --------\n",
      "Logistic Regression    0.637887     0.25048   0.972332\n",
      "Random Forest          0.851381     0.308622  0.165691\n",
      "Naive Bayes            0.718716     0.283189  0.836969\n",
      "K Nearest Neighbor     0.846029     0.304986  0.194637\n"
     ]
    }
   ],
   "source": [
    "def get_performance_metrics(clf, X, y):\n",
    "    \"\"\" Calculate performance metrics of a trained classifier\"\"\"\n",
    "    y_pred = clf.predict(X)\n",
    "\n",
    "    mean_acc = clf.score(X,y)\n",
    "    precision = precision_score(y, y_pred)\n",
    "    recall = recall_score(y, y_pred)\n",
    "    \n",
    "    return mean_acc, precision, recall\n",
    "\n",
    "\n",
    "\n",
    "mean_acc_lr, prec_lr, rec_lr = get_performance_metrics(lr_clf, X_test, y_test)\n",
    "# Note: Evaluating the SVM on a local machine can take several minutes\n",
    "mean_acc_svm, prec_svm, rec_svm = get_performance_metrics(svm_clf, X_test, y_test)\n",
    "mean_acc_rf, prec_rf, rec_rf = get_performance_metrics(rf_clf, X_test, y_test)\n",
    "mean_acc_gnb, prec_gnb, rec_gnb = get_performance_metrics(gnb_clf, X_test, y_test)\n",
    "mean_acc_knn, prec_knn, rec_knn = get_performance_metrics(knn_clf, X_test, y_test)\n",
    "\n",
    "\n",
    "performance_metrics = [\n",
    "    ['Logistic Regression', mean_acc_lr, prec_lr, rec_lr],\n",
    "    ['Support Vector Machine', mean_acc_svm, prec_svm, rec_svm],\n",
    "    ['Random Forest', mean_acc_rf, prec_rf, rec_rf],\n",
    "    ['Naive Bayes', mean_acc_gnb, prec_gnb, rec_gnb],\n",
    "    ['K Nearest Neighbor', mean_acc_knn, prec_knn, rec_knn]\n",
    "]\n",
    "\n",
    "head = ['Classifier', 'Accuracy', 'Precision', 'Recall']\n",
    "\n",
    "print(tabulate(performance_metrics, headers=head))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35682aa8",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc55e805",
   "metadata": {},
   "source": [
    "In this notebook, we trained 5 different classification models to predict wether a customer who already has a health insurance is interested in taking a car insurance, based on demographic and business-related customer data. The overall best accuracy scores were reached by the Random Forest and the K Nearest Neighbor classifiers while the Logistic Regression model and the Support Vector Machine had the highest recall scores. Naive Bayes hit a middle ground with both comparably high recall and accuracy scores. The results of all the classifiers are still quite low, considerung that a model which always predicts the majority class would have likely reached similar or better accuracy scores. Further steps to improve the models could be tuning the hyperparameters via a grid search, or using a different model like a Deep Neural Network which was not feasible here because of runtime and hardware limitations. For the task at hand it is conceivable that a high recall is much more desirable than a high precision, since it might be important to identify every customer who is willing to take a car insurance in order to approach them: Every interested customer who is not approached means lost revenue, however approaching a customer who is not interested might only be a very small expense. Thus, the logistic regression model and the Support Vector Machine might both be promising for this particular use case."
   ]
  }
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