Channels.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as dates\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(' ... ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quote_number</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>channel</th>\n",
       "      <th>region</th>\n",
       "      <th>dateofbirth</th>\n",
       "      <th>license_years</th>\n",
       "      <th>num_convictions</th>\n",
       "      <th>num_fault_claims</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>manfyear</th>\n",
       "      <th>purchaseyear</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>transmission_type</th>\n",
       "      <th>payment_plan</th>\n",
       "      <th>nature_of_use</th>\n",
       "      <th>overnightparking_loc</th>\n",
       "      <th>policy_start_date</th>\n",
       "      <th>premium</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20886</td>\n",
       "      <td>05/04/2019 10:34</td>\n",
       "      <td>Google</td>\n",
       "      <td>BRISTOL</td>\n",
       "      <td>01/12/1965</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>YAMAHA</td>\n",
       "      <td>MT-03</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>petrol</td>\n",
       "      <td>automatic</td>\n",
       "      <td>annual</td>\n",
       "      <td>social-domestic-pleasure(sdp)</td>\n",
       "      <td>unsecured_car_park</td>\n",
       "      <td>22/04/2019</td>\n",
       "      <td>85.94</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7708</td>\n",
       "      <td>25/04/2019 13:21</td>\n",
       "      <td>Go Compare</td>\n",
       "      <td>BRISTOL</td>\n",
       "      <td>01/07/1960</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>YAMAHA</td>\n",
       "      <td>XJR 1300</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>electric</td>\n",
       "      <td>manual</td>\n",
       "      <td>annual</td>\n",
       "      <td>sdp-plus-commuting</td>\n",
       "      <td>garage</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>127.21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14029</td>\n",
       "      <td>27/04/2019 13:03</td>\n",
       "      <td>Money Supermarket</td>\n",
       "      <td>BRISTOL</td>\n",
       "      <td>08/12/1963</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>CB 650F (86HP)</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>petrol</td>\n",
       "      <td>manual</td>\n",
       "      <td>annual</td>\n",
       "      <td>sdp-plus-commuting</td>\n",
       "      <td>drive</td>\n",
       "      <td>01/05/2019</td>\n",
       "      <td>249.56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quote_number         timestamp            channel   region dateofbirth  \\\n",
       "0         20886  05/04/2019 10:34             Google  BRISTOL  01/12/1965   \n",
       "1          7708  25/04/2019 13:21         Go Compare  BRISTOL  01/07/1960   \n",
       "2         14029  27/04/2019 13:03  Money Supermarket  BRISTOL  08/12/1963   \n",
       "\n",
       "   license_years  num_convictions  num_fault_claims    make           model  \\\n",
       "0           32.0                0                 0  YAMAHA           MT-03   \n",
       "1           41.0                0                 0  YAMAHA        XJR 1300   \n",
       "2           36.0                0                 1   HONDA  CB 650F (86HP)   \n",
       "\n",
       "   manfyear  purchaseyear fuel_type transmission_type payment_plan  \\\n",
       "0    2008.0        2012.0    petrol         automatic       annual   \n",
       "1    2012.0        2012.0  electric            manual       annual   \n",
       "2    2015.0        2017.0    petrol            manual       annual   \n",
       "\n",
       "                   nature_of_use overnightparking_loc policy_start_date  \\\n",
       "0  social-domestic-pleasure(sdp)   unsecured_car_park        22/04/2019   \n",
       "1             sdp-plus-commuting               garage        12/05/2019   \n",
       "2             sdp-plus-commuting                drive        01/05/2019   \n",
       "\n",
       "   premium  converted  \n",
       "0    85.94          1  \n",
       "1   127.21          0  \n",
       "2   249.56          1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the shape of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset has 100000 observations and 20 features.\n"
     ]
    }
   ],
   "source": [
    "print(f'The dataset has {df.shape[0]:d} observations and {df.shape[1]:d} features.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now let's check the type of the rest of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quote_number              int64\n",
       "timestamp                object\n",
       "channel                  object\n",
       "region                   object\n",
       "dateofbirth              object\n",
       "license_years           float64\n",
       "num_convictions           int64\n",
       "num_fault_claims          int64\n",
       "make                     object\n",
       "model                    object\n",
       "manfyear                float64\n",
       "purchaseyear            float64\n",
       "fuel_type                object\n",
       "transmission_type        object\n",
       "payment_plan             object\n",
       "nature_of_use            object\n",
       "overnightparking_loc     object\n",
       "policy_start_date        object\n",
       "premium                 float64\n",
       "converted                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that we have 3 columns related to date and time but their variable type is object, so let's change that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['dateofbirth'] = pd.to_datetime(df['dateofbirth'])\n",
    "df['timestamp'] = pd.to_datetime(df['timestamp'])\n",
    "df['policy_start_date'] = pd.to_datetime(df['policy_start_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's obtain a summary about the numerical columns of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quote_number</th>\n",
       "      <th>license_years</th>\n",
       "      <th>num_convictions</th>\n",
       "      <th>num_fault_claims</th>\n",
       "      <th>manfyear</th>\n",
       "      <th>purchaseyear</th>\n",
       "      <th>premium</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>99993.000000</td>\n",
       "      <td>97544.000000</td>\n",
       "      <td>99998.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.248810e+06</td>\n",
       "      <td>25.115120</td>\n",
       "      <td>0.051080</td>\n",
       "      <td>0.298160</td>\n",
       "      <td>2011.609423</td>\n",
       "      <td>2015.025363</td>\n",
       "      <td>427.309153</td>\n",
       "      <td>0.343500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.830217e+06</td>\n",
       "      <td>14.167082</td>\n",
       "      <td>0.220162</td>\n",
       "      <td>0.555755</td>\n",
       "      <td>5.513250</td>\n",
       "      <td>3.974439</td>\n",
       "      <td>267.346187</td>\n",
       "      <td>0.474879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.708000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1937.000000</td>\n",
       "      <td>1971.000000</td>\n",
       "      <td>18.290000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.880006e+06</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>274.362500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.949290e+06</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>357.555000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.142949e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>488.980000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.471137e+07</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>7206.960000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       quote_number  license_years  num_convictions  num_fault_claims  \\\n",
       "count  1.000000e+05  100000.000000    100000.000000     100000.000000   \n",
       "mean   6.248810e+06      25.115120         0.051080          0.298160   \n",
       "std    3.830217e+06      14.167082         0.220162          0.555755   \n",
       "min    7.708000e+03       0.000000         0.000000          0.000000   \n",
       "25%    2.880006e+06      13.000000         0.000000          0.000000   \n",
       "50%    5.949290e+06      25.000000         0.000000          0.000000   \n",
       "75%    9.142949e+06      37.000000         0.000000          0.000000   \n",
       "max    1.471137e+07      53.000000         1.000000          2.000000   \n",
       "\n",
       "           manfyear  purchaseyear       premium      converted  \n",
       "count  99993.000000  97544.000000  99998.000000  100000.000000  \n",
       "mean    2011.609423   2015.025363    427.309153       0.343500  \n",
       "std        5.513250      3.974439    267.346187       0.474879  \n",
       "min     1937.000000   1971.000000     18.290000       0.000000  \n",
       "25%     2008.000000   2014.000000    274.362500       0.000000  \n",
       "50%     2013.000000   2016.000000    357.555000       0.000000  \n",
       "75%     2016.000000   2018.000000    488.980000       1.000000  \n",
       "max     2019.000000   2019.000000   7206.960000       1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And for the categorical columns, let's print the number of unique variables and the most frequent ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "channel   7   Money Supermarket\n",
      "region   21   BRISTOL\n",
      "make   188   VW\n",
      "model   18133   FIESTA ZETEC\n",
      "fuel_type   3   petrol\n",
      "transmission_type   2   automatic\n",
      "payment_plan   2   annual\n",
      "nature_of_use   3   sdp-plus-commuting\n",
      "overnightparking_loc   5   drive\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    if df[col].dtypes == object:\n",
    "        print(df[col].name, ' ', df[col].nunique(),  ' ', df[col].mode().iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the summary table above we notice that not every single column has 10,000 entries. Let's check how many values are missing from our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2473"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's see the proportion of missing values from every column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make            0.004\n",
       "model           0.004\n",
       "manfyear        0.007\n",
       "purchaseyear    2.456\n",
       "premium         0.002\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Obtain columns with missing values\n",
    "col_miss_val = df.loc[:, df.isna().any()]\n",
    "col_miss_val.isna().sum()/(len(col_miss_val))*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The percentage of the numerical missing values is quite small and insignificant, therefore we can subsitute them with the column mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yy19812\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df = df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quote_number</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>channel</th>\n",
       "      <th>region</th>\n",
       "      <th>dateofbirth</th>\n",
       "      <th>license_years</th>\n",
       "      <th>num_convictions</th>\n",
       "      <th>num_fault_claims</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>manfyear</th>\n",
       "      <th>purchaseyear</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>transmission_type</th>\n",
       "      <th>payment_plan</th>\n",
       "      <th>nature_of_use</th>\n",
       "      <th>overnightparking_loc</th>\n",
       "      <th>policy_start_date</th>\n",
       "      <th>premium</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29598</th>\n",
       "      <td>679178</td>\n",
       "      <td>2019-05-02 22:48:00</td>\n",
       "      <td>Go Compare</td>\n",
       "      <td>LEICESTER</td>\n",
       "      <td>1995-03-21</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.609423</td>\n",
       "      <td>2015.025363</td>\n",
       "      <td>diesel</td>\n",
       "      <td>manual</td>\n",
       "      <td>annual</td>\n",
       "      <td>social-domestic-pleasure(sdp)</td>\n",
       "      <td>drive</td>\n",
       "      <td>2019-05-02</td>\n",
       "      <td>285.86</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79627</th>\n",
       "      <td>2077301</td>\n",
       "      <td>2019-06-24 17:20:00</td>\n",
       "      <td>Go Compare</td>\n",
       "      <td>CAMBRIDGE</td>\n",
       "      <td>1970-11-25</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.609423</td>\n",
       "      <td>2015.025363</td>\n",
       "      <td>diesel</td>\n",
       "      <td>automatic</td>\n",
       "      <td>annual</td>\n",
       "      <td>sdp-plus-business</td>\n",
       "      <td>secured_car_park</td>\n",
       "      <td>2019-07-13</td>\n",
       "      <td>418.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84384</th>\n",
       "      <td>624544</td>\n",
       "      <td>2019-07-13 15:08:00</td>\n",
       "      <td>Confused</td>\n",
       "      <td>CARDIFF</td>\n",
       "      <td>1985-07-05</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.609423</td>\n",
       "      <td>2015.025363</td>\n",
       "      <td>petrol</td>\n",
       "      <td>automatic</td>\n",
       "      <td>annual</td>\n",
       "      <td>sdp-plus-commuting</td>\n",
       "      <td>drive</td>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>285.86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91809</th>\n",
       "      <td>1986296</td>\n",
       "      <td>2019-12-07 15:42:00</td>\n",
       "      <td>Money Supermarket</td>\n",
       "      <td>LEICESTER</td>\n",
       "      <td>1968-03-21</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.609423</td>\n",
       "      <td>2015.025363</td>\n",
       "      <td>diesel</td>\n",
       "      <td>automatic</td>\n",
       "      <td>annual</td>\n",
       "      <td>sdp-plus-commuting</td>\n",
       "      <td>drive</td>\n",
       "      <td>2019-07-20</td>\n",
       "      <td>565.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       quote_number           timestamp            channel     region  \\\n",
       "29598        679178 2019-05-02 22:48:00         Go Compare  LEICESTER   \n",
       "79627       2077301 2019-06-24 17:20:00         Go Compare  CAMBRIDGE   \n",
       "84384        624544 2019-07-13 15:08:00           Confused    CARDIFF   \n",
       "91809       1986296 2019-12-07 15:42:00  Money Supermarket  LEICESTER   \n",
       "\n",
       "      dateofbirth  license_years  num_convictions  num_fault_claims make  \\\n",
       "29598  1995-03-21            5.0                0                 0  NaN   \n",
       "79627  1970-11-25           28.0                0                 0  NaN   \n",
       "84384  1985-07-05           15.0                0                 0  NaN   \n",
       "91809  1968-03-21           30.0                1                 0  NaN   \n",
       "\n",
       "      model     manfyear  purchaseyear fuel_type transmission_type  \\\n",
       "29598   NaN  2011.609423   2015.025363    diesel            manual   \n",
       "79627   NaN  2011.609423   2015.025363    diesel         automatic   \n",
       "84384   NaN  2011.609423   2015.025363    petrol         automatic   \n",
       "91809   NaN  2011.609423   2015.025363    diesel         automatic   \n",
       "\n",
       "      payment_plan                  nature_of_use overnightparking_loc  \\\n",
       "29598       annual  social-domestic-pleasure(sdp)                drive   \n",
       "79627       annual              sdp-plus-business     secured_car_park   \n",
       "84384       annual             sdp-plus-commuting                drive   \n",
       "91809       annual             sdp-plus-commuting                drive   \n",
       "\n",
       "      policy_start_date  premium  converted  \n",
       "29598        2019-05-02   285.86          0  \n",
       "79627        2019-07-13   418.62          0  \n",
       "84384        2019-07-22   285.86          1  \n",
       "91809        2019-07-20   565.20          1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We cannot substitute the average values in categorical variables. Maybe we could substitute with the most common ones but that might cause some form of bias so it would be much better if we just remove these 4 observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore our data further. For instance, let's examine the age of the customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The oldest person is 72 years old and the youngest one 20.\n"
     ]
    }
   ],
   "source": [
    "df['yearofbirth'] = pd.DatetimeIndex(df['dateofbirth']).year\n",
    "df['age'] = datetime.datetime.now().year - df['yearofbirth']\n",
    "print(f'The oldest person is {df.age.max():d} years old and the youngest one {df.age.min():d}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to find out whether the age is related to some of the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def average(y):\n",
    "    \"\"\"This function calculates the average age of a group defined by a column\"\"\"\n",
    "    \n",
    "    def average_age(x):\n",
    "        \n",
    "        s = df.loc[x, 'age']\n",
    "        y = sum(s)/len(s)\n",
    "        \n",
    "        return y\n",
    "    \n",
    "    l = []\n",
    "    for i in y.value_counts().keys():\n",
    "        l.append(average_age(y == i))\n",
    "        \n",
    "    return l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[45.67418546365915,\n",
       " 45.90015792244253,\n",
       " 45.99876998769988,\n",
       " 46.46349520977866,\n",
       " 47.82692464690995,\n",
       " 46.564077840112205,\n",
       " 46.05598866052445]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average(df['channel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[48.98798489529695,\n",
       " 41.94471410941999,\n",
       " 46.21762270845653,\n",
       " 42.13119848669099,\n",
       " 46.22655509946321,\n",
       " 45.97627248373517,\n",
       " 46.45583916873729,\n",
       " 46.714466043928304,\n",
       " 46.43076923076923,\n",
       " 46.55,\n",
       " 46.32622187742436,\n",
       " 46.470588235294116,\n",
       " 46.286280336800395,\n",
       " 46.388919363686234,\n",
       " 46.39338731443995,\n",
       " 46.70388019060585,\n",
       " 46.38262742282843,\n",
       " 47.12261146496815,\n",
       " 47.26578947368421,\n",
       " 46.13465783664459,\n",
       " 47.101551480959095]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average(df['region'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently there is no preference about a channel based on the age of the customers. There is also no correlation between their age and where they live."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how many fault claims each channel has and draw a barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fault_claims = df[['channel', 'num_fault_claims']]\n",
    "fault_claims = fault_claims.groupby(['channel'], as_index=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "channel             Confused\n",
       "num_fault_claims        5093\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fault_claims.loc[fault_claims['num_fault_claims'].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,7))\n",
    "ax = sns.barplot(x='channel', y='num_fault_claims', data=fault_claims)\n",
    "\n",
    "ax.set_title('Number of fault claims per channel')\n",
    "ax.set_xlabel('Channels')\n",
    "ax.set_ylabel('Number of fault claims')\n",
    "\n",
    "plt.xticks(rotation=-30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's produce a correlation matrix to see which variables are related with each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['yearofbirth', 'age'], axis = 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "objList = df.select_dtypes(include = ['object', 'datetime64[ns]']).columns\n",
    "for feat in objList:\n",
    "    df[feat] = le.fit_transform(df[feat].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x1224 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatrix = df.corr()\n",
    "corrMatrix.replace(1, 0, inplace=True) # the diagonal is going to be equal to 1 anyways\n",
    "fig = plt.figure(figsize=(17,17))\n",
    "sns.heatmap(corrMatrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We remove the values from the diagonal as we know in advance they are equal to 1. By investigating the matrix we do not find many surpises actually. It is expected for the make to be correlated to the model, the purchase and the manifactured year.It is also expected for the date of birth to be inversely correlated to the license years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a Random Forest model which is a great classifier for large datasets with many features. Our goal is to predict the chance of conversion. As we can see below, conversion happens to roughly a third of the observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    65648\n",
       "1    34348\n",
       "Name: converted, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['converted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(df.loc[:, df.columns != 'converted'])\n",
    "y = np.array(df['converted'])\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(n_estimators=50, random_state=0)\n",
    "model.fit(x_train, y_train)\n",
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the MSE and its square root as metrices of error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.32746446862026124\n",
      "Root Mean Squared Error: 0.5722451123603077\n"
     ]
    }
   ],
   "source": [
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[18973  2741]\n",
      " [ 8065  3220]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.87      0.78     21714\n",
      "           1       0.54      0.29      0.37     11285\n",
      "\n",
      "    accuracy                           0.67     32999\n",
      "   macro avg       0.62      0.58      0.58     32999\n",
      "weighted avg       0.65      0.67      0.64     32999\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the confusion matrix we notice there are many observations falsely classified as 0, which is verified from the low precision value (0.54) of 0 in the classification report. As we can from the accuracy metric, the Random Forest model can correctly predict around the two thirds of the observations, but it is much more accurate when it comes to predicting conversion they occured."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}