10-01-22.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\dany0\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "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>claim_number</th>\n",
       "      <th>age_of_driver</th>\n",
       "      <th>gender</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>annual_income</th>\n",
       "      <th>high_education_ind</th>\n",
       "      <th>address_change_ind</th>\n",
       "      <th>living_status</th>\n",
       "      <th>claim_day_of_week</th>\n",
       "      <th>accident_site</th>\n",
       "      <th>past_num_of_claims</th>\n",
       "      <th>channel</th>\n",
       "      <th>claim_est_payout</th>\n",
       "      <th>age_of_vehicle</th>\n",
       "      <th>vehicle_category</th>\n",
       "      <th>vehicle_price</th>\n",
       "      <th>vehicle_color</th>\n",
       "      <th>vehicle_weight</th>\n",
       "      <th>fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38301</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Local</td>\n",
       "      <td>1</td>\n",
       "      <td>Broker</td>\n",
       "      <td>7530.940993</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Compact</td>\n",
       "      <td>12885.45235</td>\n",
       "      <td>white</td>\n",
       "      <td>16161.33381</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Highway</td>\n",
       "      <td>1</td>\n",
       "      <td>Online</td>\n",
       "      <td>2966.024895</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Large</td>\n",
       "      <td>29429.45218</td>\n",
       "      <td>white</td>\n",
       "      <td>28691.96422</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38923</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Own</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Local</td>\n",
       "      <td>0</td>\n",
       "      <td>Broker</td>\n",
       "      <td>6283.888333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Compact</td>\n",
       "      <td>21701.18195</td>\n",
       "      <td>white</td>\n",
       "      <td>22090.94758</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>58</td>\n",
       "      <td>F</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40605</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Own</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Local</td>\n",
       "      <td>3</td>\n",
       "      <td>Broker</td>\n",
       "      <td>6169.747994</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>13198.27344</td>\n",
       "      <td>other</td>\n",
       "      <td>38329.58106</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36380</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Highway</td>\n",
       "      <td>0</td>\n",
       "      <td>Broker</td>\n",
       "      <td>4541.387150</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>38060.21122</td>\n",
       "      <td>gray</td>\n",
       "      <td>25876.56319</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_number  age_of_driver gender  marital_status  annual_income  \\\n",
       "0             1             46      M             1.0          38301   \n",
       "1             3             21      F             0.0          30445   \n",
       "2             4             49      F             0.0          38923   \n",
       "3             5             58      F             1.0          40605   \n",
       "4             6             38      M             1.0          36380   \n",
       "\n",
       "   high_education_ind  address_change_ind living_status claim_day_of_week  \\\n",
       "0                   1                   1          Rent            Friday   \n",
       "1                   0                   1          Rent          Thursday   \n",
       "2                   0                   1           Own           Tuesday   \n",
       "3                   1                   0           Own          Thursday   \n",
       "4                   1                   0          Rent           Tuesday   \n",
       "\n",
       "  accident_site  past_num_of_claims channel  claim_est_payout  age_of_vehicle  \\\n",
       "0         Local                   1  Broker       7530.940993             9.0   \n",
       "1       Highway                   1  Online       2966.024895             4.0   \n",
       "2         Local                   0  Broker       6283.888333             3.0   \n",
       "3         Local                   3  Broker       6169.747994             4.0   \n",
       "4       Highway                   0  Broker       4541.387150             7.0   \n",
       "\n",
       "  vehicle_category  vehicle_price vehicle_color  vehicle_weight  fraud  \n",
       "0          Compact    12885.45235         white     16161.33381      0  \n",
       "1            Large    29429.45218         white     28691.96422      0  \n",
       "2          Compact    21701.18195         white     22090.94758      1  \n",
       "3           Medium    13198.27344         other     38329.58106      1  \n",
       "4           Medium    38060.21122          gray     25876.56319      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df=pd.read_csv('./ClaimCars/training_set_fd.csv', delimiter=';')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17998 entries, 0 to 17997\n",
      "Data columns (total 19 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   claim_number        17998 non-null  int64  \n",
      " 1   age_of_driver       17998 non-null  int64  \n",
      " 2   gender              17998 non-null  object \n",
      " 3   marital_status      17993 non-null  float64\n",
      " 4   annual_income       17998 non-null  int64  \n",
      " 5   high_education_ind  17998 non-null  int64  \n",
      " 6   address_change_ind  17998 non-null  int64  \n",
      " 7   living_status       17998 non-null  object \n",
      " 8   claim_day_of_week   17998 non-null  object \n",
      " 9   accident_site       17998 non-null  object \n",
      " 10  past_num_of_claims  17998 non-null  int64  \n",
      " 11  channel             17998 non-null  object \n",
      " 12  claim_est_payout    17981 non-null  float64\n",
      " 13  age_of_vehicle      17990 non-null  float64\n",
      " 14  vehicle_category    17998 non-null  object \n",
      " 15  vehicle_price       17998 non-null  float64\n",
      " 16  vehicle_color       17998 non-null  object \n",
      " 17  vehicle_weight      17998 non-null  float64\n",
      " 18  fraud               17998 non-null  int64  \n",
      "dtypes: float64(5), int64(7), object(7)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero istanze dataset:  17998\n",
      "Sono presenti missing values?  True\n",
      "Numero di missing values:  30\n",
      "Numero di missing values per attributo:  claim_number           0\n",
      "age_of_driver          0\n",
      "gender                 0\n",
      "marital_status         5\n",
      "annual_income          0\n",
      "high_education_ind     0\n",
      "address_change_ind     0\n",
      "living_status          0\n",
      "claim_day_of_week      0\n",
      "accident_site          0\n",
      "past_num_of_claims     0\n",
      "channel                0\n",
      "claim_est_payout      17\n",
      "age_of_vehicle         8\n",
      "vehicle_category       0\n",
      "vehicle_price          0\n",
      "vehicle_color          0\n",
      "vehicle_weight         0\n",
      "fraud                  0\n",
      "dtype: int64\n",
      "Il dataset è bilanciato rispetto a 'fraud'?  False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Count (fraud)'}>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Numero istanze dataset: \", len(df))\n",
    "print(\"Sono presenti missing values? \", df.isnull().values.any())\n",
    "print(\"Numero di missing values: \", df.isnull().sum().sum())\n",
    "print(\"Numero di missing values per attributo: \", df.isnull().sum())\n",
    "print(\"Il dataset è bilanciato rispetto a 'fraud'? \", df['fraud'].value_counts()[0] == df['fraud'].value_counts()[1])\n",
    "df['fraud'].value_counts().plot(kind='bar', title='Count (fraud)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_age=df.loc[df['age_of_driver']<91]\n",
    "age_group = pd.cut(df_age['age_of_driver'], bins=[17, 21, 25, 30,40, 50, 90])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 50]    5768\n",
       "(30, 40]    5064\n",
       "(50, 90]    4712\n",
       "(25, 30]    1584\n",
       "(21, 25]     716\n",
       "(17, 21]     128\n",
       "Name: age_of_driver, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_group.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_age['age_of_driver'].hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dany0\\AppData\\Local\\Temp\\ipykernel_11272\\1632709738.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_age['age_group'] = age_group\n"
     ]
    },
    {
     "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>fraud</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(17, 21]</th>\n",
       "      <td>0.781250</td>\n",
       "      <td>0.218750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(21, 25]</th>\n",
       "      <td>0.857542</td>\n",
       "      <td>0.142458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25, 30]</th>\n",
       "      <td>0.843434</td>\n",
       "      <td>0.156566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(30, 40]</th>\n",
       "      <td>0.829581</td>\n",
       "      <td>0.170419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40, 50]</th>\n",
       "      <td>0.805652</td>\n",
       "      <td>0.194348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50, 90]</th>\n",
       "      <td>0.904711</td>\n",
       "      <td>0.095289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fraud             0         1\n",
       "age_group                    \n",
       "(17, 21]   0.781250  0.218750\n",
       "(21, 25]   0.857542  0.142458\n",
       "(25, 30]   0.843434  0.156566\n",
       "(30, 40]   0.829581  0.170419\n",
       "(40, 50]   0.805652  0.194348\n",
       "(50, 90]   0.904711  0.095289"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=age_group, hue='fraud', data=df_age)\n",
    "df_age['age_group'] = age_group\n",
    "df_age.groupby('age_group')['fraud'].value_counts(normalize=True).unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentuale frodi donne sotto i 40 anni:  0.17992766726943943\n",
      "Percentuale frodi uomini sotto i 40 anni:  0.1487977369165488\n",
      "Percentuale frodi donne sopra i 40 anni:  0.16974661840350544\n",
      "Percentuale frodi uomini sopra i 40 anni:  0.1360381861575179\n"
     ]
    }
   ],
   "source": [
    "df_under= df.loc[df['age_of_driver']<40]\n",
    "df_over= df.loc[df['age_of_driver']>=40]\n",
    "df_under_fro = df_under.loc[df['fraud']==1].groupby('gender').count()['claim_number']\n",
    "df_over_fro = df_over.loc[df['fraud']==1].groupby('gender').count()['claim_number']\n",
    "df_u_f = df_under_fro['F']/df_under[df_under['gender']=='F'].count()['gender']\n",
    "df_u_m = df_under_fro['M']/df_under[df_under['gender']=='M'].count()['gender']\n",
    "df_o_f = df_over_fro['F']/df_over[df_over['gender']=='F'].count()['gender']\n",
    "df_o_m = df_over_fro['M']/df_over[df_over['gender']=='M'].count()['gender']\n",
    "print(\"Percentuale frodi donne sotto i 40 anni: \", df_u_f)\n",
    "print(\"Percentuale frodi uomini sotto i 40 anni: \", df_u_m)\n",
    "print(\"Percentuale frodi donne sopra i 40 anni: \", df_o_f)\n",
    "print(\"Percentuale frodi uomini sopra i 40 anni: \", df_o_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fraud          0         1\n",
      "gender                    \n",
      "F       0.820072  0.179928\n",
      "M       0.851202  0.148798\n",
      "fraud          0         1\n",
      "gender                    \n",
      "F       0.830253  0.169747\n",
      "M       0.863962  0.136038\n"
     ]
    }
   ],
   "source": [
    "print(df_under.groupby('gender')['fraud'].value_counts(normalize=True).unstack())\n",
    "print(df_over.groupby('gender')['fraud'].value_counts(normalize=True).unstack())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['claim_est_payout'].hist(bins=100)\n",
    "df.loc[df['fraud']==0]['claim_est_payout'].hist(bins=100)\n",
    "df.loc[df['fraud']==1]['claim_est_payout'].hist(bins=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =  pd.read_csv('./ClaimCars/training_set_fd.csv', delimiter=';')\n",
    "df = df.dropna()\n",
    "data_text=df.drop(['claim_number', 'vehicle_color', 'vehicle_weight', 'marital_status', 'fraud'], axis=1)\n",
    "data_label=df['fraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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_of_driver</th>\n",
       "      <th>gender</th>\n",
       "      <th>annual_income</th>\n",
       "      <th>high_education_ind</th>\n",
       "      <th>address_change_ind</th>\n",
       "      <th>living_status</th>\n",
       "      <th>claim_day_of_week</th>\n",
       "      <th>accident_site</th>\n",
       "      <th>past_num_of_claims</th>\n",
       "      <th>channel</th>\n",
       "      <th>claim_est_payout</th>\n",
       "      <th>age_of_vehicle</th>\n",
       "      <th>vehicle_category</th>\n",
       "      <th>vehicle_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46</td>\n",
       "      <td>M</td>\n",
       "      <td>38301</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Local</td>\n",
       "      <td>1</td>\n",
       "      <td>Broker</td>\n",
       "      <td>7530.940993</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Compact</td>\n",
       "      <td>12885.45235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>30445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Highway</td>\n",
       "      <td>1</td>\n",
       "      <td>Online</td>\n",
       "      <td>2966.024895</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Large</td>\n",
       "      <td>29429.45218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>38923</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Own</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Local</td>\n",
       "      <td>0</td>\n",
       "      <td>Broker</td>\n",
       "      <td>6283.888333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Compact</td>\n",
       "      <td>21701.18195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>F</td>\n",
       "      <td>40605</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Own</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Local</td>\n",
       "      <td>3</td>\n",
       "      <td>Broker</td>\n",
       "      <td>6169.747994</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>13198.27344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>36380</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Highway</td>\n",
       "      <td>0</td>\n",
       "      <td>Broker</td>\n",
       "      <td>4541.387150</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>38060.21122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_of_driver gender  annual_income  high_education_ind  \\\n",
       "0             46      M          38301                   1   \n",
       "1             21      F          30445                   0   \n",
       "2             49      F          38923                   0   \n",
       "3             58      F          40605                   1   \n",
       "4             38      M          36380                   1   \n",
       "\n",
       "   address_change_ind living_status claim_day_of_week accident_site  \\\n",
       "0                   1          Rent            Friday         Local   \n",
       "1                   1          Rent          Thursday       Highway   \n",
       "2                   1           Own           Tuesday         Local   \n",
       "3                   0           Own          Thursday         Local   \n",
       "4                   0          Rent           Tuesday       Highway   \n",
       "\n",
       "   past_num_of_claims channel  claim_est_payout  age_of_vehicle  \\\n",
       "0                   1  Broker       7530.940993             9.0   \n",
       "1                   1  Online       2966.024895             4.0   \n",
       "2                   0  Broker       6283.888333             3.0   \n",
       "3                   3  Broker       6169.747994             4.0   \n",
       "4                   0  Broker       4541.387150             7.0   \n",
       "\n",
       "  vehicle_category  vehicle_price  \n",
       "0          Compact    12885.45235  \n",
       "1            Large    29429.45218  \n",
       "2          Compact    21701.18195  \n",
       "3           Medium    13198.27344  \n",
       "4           Medium    38060.21122  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_text.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17968, 14)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "categoricals = data_text.select_dtypes(include='object').columns\n",
    "for col in categoricals:\n",
    "    data_text[col] = le.fit_transform(data_text[col])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_text, data_label, test_size=1/3)\n",
    "data_text.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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_of_driver</th>\n",
       "      <th>gender</th>\n",
       "      <th>annual_income</th>\n",
       "      <th>high_education_ind</th>\n",
       "      <th>address_change_ind</th>\n",
       "      <th>living_status</th>\n",
       "      <th>claim_day_of_week</th>\n",
       "      <th>accident_site</th>\n",
       "      <th>past_num_of_claims</th>\n",
       "      <th>channel</th>\n",
       "      <th>claim_est_payout</th>\n",
       "      <th>age_of_vehicle</th>\n",
       "      <th>vehicle_category</th>\n",
       "      <th>vehicle_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>38301</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7530.940993</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12885.45235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>30445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2966.024895</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29429.45218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>38923</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6283.888333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>21701.18195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>40605</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6169.747994</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>13198.27344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>36380</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4541.387150</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>38060.21122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_of_driver  gender  annual_income  high_education_ind  \\\n",
       "0             46       1          38301                   1   \n",
       "1             21       0          30445                   0   \n",
       "2             49       0          38923                   0   \n",
       "3             58       0          40605                   1   \n",
       "4             38       1          36380                   1   \n",
       "\n",
       "   address_change_ind  living_status  claim_day_of_week  accident_site  \\\n",
       "0                   1              1                  0              1   \n",
       "1                   1              1                  4              0   \n",
       "2                   1              0                  5              1   \n",
       "3                   0              0                  4              1   \n",
       "4                   0              1                  5              0   \n",
       "\n",
       "   past_num_of_claims  channel  claim_est_payout  age_of_vehicle  \\\n",
       "0                   1        0       7530.940993             9.0   \n",
       "1                   1        1       2966.024895             4.0   \n",
       "2                   0        0       6283.888333             3.0   \n",
       "3                   3        0       6169.747994             4.0   \n",
       "4                   0        0       4541.387150             7.0   \n",
       "\n",
       "   vehicle_category  vehicle_price  \n",
       "0                 0    12885.45235  \n",
       "1                 1    29429.45218  \n",
       "2                 0    21701.18195  \n",
       "3                 2    13198.27344  \n",
       "4                 2    38060.21122  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_text.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "\n",
    "# enc = OrdinalEncoder()\n",
    "# data = enc.fit_transform(data_text)\n",
    "# data = pd.DataFrame(data, columns=data_text.columns)\n",
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tree:                precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.84      0.85      5021\n",
      "           1       0.23      0.24      0.23       969\n",
      "\n",
      "    accuracy                           0.75      5990\n",
      "   macro avg       0.54      0.54      0.54      5990\n",
      "weighted avg       0.75      0.75      0.75      5990\n",
      "\n",
      "Logreg:                precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      1.00      0.91      5021\n",
      "           1       0.00      0.00      0.00       969\n",
      "\n",
      "    accuracy                           0.84      5990\n",
      "   macro avg       0.42      0.50      0.46      5990\n",
      "weighted avg       0.70      0.84      0.76      5990\n",
      "\n",
      "Dummy:                precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      1.00      0.91      5021\n",
      "           1       0.00      0.00      0.00       969\n",
      "\n",
      "    accuracy                           0.84      5990\n",
      "   macro avg       0.42      0.50      0.46      5990\n",
      "weighted avg       0.70      0.84      0.76      5990\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\dany0\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\dany0\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\dany0\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "tree = DecisionTreeClassifier()\n",
    "logreg = LogisticRegression(solver='liblinear')\n",
    "dummy = DummyClassifier()\n",
    "tree.fit(X_train, y_train)\n",
    "y_pred_tree = tree.predict(X_test)\n",
    "print(\"Tree: \", classification_report(y_test, y_pred_tree))\n",
    "cm_tree = confusion_matrix(y_test, y_pred_tree, normalize='true')\n",
    "\n",
    "logreg.fit(X_train, y_train)\n",
    "y_pred_logreg = logreg.predict(X_test)\n",
    "print(\"Logreg: \", classification_report(y_test, y_pred_logreg))\n",
    "cm_logreg = confusion_matrix(y_test, y_pred_logreg, normalize='true')\n",
    "\n",
    "dummy.fit(X_train, y_train)\n",
    "y_pred_dummy = dummy.predict(X_test)\n",
    "print(\"Dummy: \", classification_report(y_test, y_pred_dummy))\n",
    "cm_dummy = confusion_matrix(y_test, y_pred_dummy, normalize='true')\n",
    "\n",
    "sns.heatmap(cm_tree, annot=True)\n",
    "plt.title('Confusion matrix tree')  \n",
    "plt.show()\n",
    "sns.heatmap(cm_logreg, annot=True)\n",
    "plt.title('Confusion matrix logreg')\n",
    "plt.show()\n",
    "sns.heatmap(cm_dummy, annot=True)\n",
    "plt.title('Confusion matrix dummy')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tree:  0.21321695560894013\n",
      "Logreg:  0.0\n",
      "Dummy:  0.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores_tree = cross_val_score(tree, data_text, data_label, cv=10, scoring='f1')\n",
    "scores_logreg = cross_val_score(logreg, data_text, data_label, cv=10, scoring='f1')\n",
    "scores_dummy = cross_val_score(dummy, data_text, data_label, cv=10, scoring='f1')\n",
    "\n",
    "print(\"Tree: \", scores_tree.mean())\n",
    "print(\"Logreg: \", scores_logreg.mean())\n",
    "print(\"Dummy: \", scores_dummy.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\dany0\\OneDrive - Unimore\\AIE\\Big Data\\Prove\\10-01-22.ipynb Cell 17\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/dany0/OneDrive%20-%20Unimore/AIE/Big%20Data/Prove/10-01-22.ipynb#X53sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m param_grid \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mC\u001b[39m\u001b[39m'\u001b[39m: [\u001b[39m10\u001b[39m, \u001b[39m100\u001b[39m], \u001b[39m'\u001b[39m\u001b[39mpenalty\u001b[39m\u001b[39m'\u001b[39m: [\u001b[39m'\u001b[39m\u001b[39ml1\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39ml2\u001b[39m\u001b[39m'\u001b[39m]}\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/dany0/OneDrive%20-%20Unimore/AIE/Big%20Data/Prove/10-01-22.ipynb#X53sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m logreg_cv \u001b[39m=\u001b[39m GridSearchCV(logreg, param_grid, cv\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m, scoring\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mf1\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/dany0/OneDrive%20-%20Unimore/AIE/Big%20Data/Prove/10-01-22.ipynb#X53sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m logreg_cv\u001b[39m.\u001b[39;49mfit(data_text, data_label)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/dany0/OneDrive%20-%20Unimore/AIE/Big%20Data/Prove/10-01-22.ipynb#X53sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mTuned hyperparameters: \u001b[39m\u001b[39m\"\u001b[39m, logreg_cv\u001b[39m.\u001b[39mbest_params_)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/dany0/OneDrive%20-%20Unimore/AIE/Big%20Data/Prove/10-01-22.ipynb#X53sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mBest score: \u001b[39m\u001b[39m\"\u001b[39m, logreg_cv\u001b[39m.\u001b[39mbest_score_)\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:891\u001b[0m, in \u001b[0;36mBaseSearchCV.fit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    885\u001b[0m     results \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_format_results(\n\u001b[0;32m    886\u001b[0m         all_candidate_params, n_splits, all_out, all_more_results\n\u001b[0;32m    887\u001b[0m     )\n\u001b[0;32m    889\u001b[0m     \u001b[39mreturn\u001b[39;00m results\n\u001b[1;32m--> 891\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_search(evaluate_candidates)\n\u001b[0;32m    893\u001b[0m \u001b[39m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[0;32m    894\u001b[0m \u001b[39m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[0;32m    895\u001b[0m first_test_score \u001b[39m=\u001b[39m all_out[\u001b[39m0\u001b[39m][\u001b[39m\"\u001b[39m\u001b[39mtest_scores\u001b[39m\u001b[39m\"\u001b[39m]\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1392\u001b[0m, in \u001b[0;36mGridSearchCV._run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1390\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_run_search\u001b[39m(\u001b[39mself\u001b[39m, evaluate_candidates):\n\u001b[0;32m   1391\u001b[0m     \u001b[39m\"\"\"Search all candidates in param_grid\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1392\u001b[0m     evaluate_candidates(ParameterGrid(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mparam_grid))\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:838\u001b[0m, in \u001b[0;36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    830\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m    831\u001b[0m     \u001b[39mprint\u001b[39m(\n\u001b[0;32m    832\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mFitting \u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m folds for each of \u001b[39m\u001b[39m{1}\u001b[39;00m\u001b[39m candidates,\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    833\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m totalling \u001b[39m\u001b[39m{2}\u001b[39;00m\u001b[39m fits\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[0;32m    834\u001b[0m             n_splits, n_candidates, n_candidates \u001b[39m*\u001b[39m n_splits\n\u001b[0;32m    835\u001b[0m         )\n\u001b[0;32m    836\u001b[0m     )\n\u001b[1;32m--> 838\u001b[0m out \u001b[39m=\u001b[39m parallel(\n\u001b[0;32m    839\u001b[0m     delayed(_fit_and_score)(\n\u001b[0;32m    840\u001b[0m         clone(base_estimator),\n\u001b[0;32m    841\u001b[0m         X,\n\u001b[0;32m    842\u001b[0m         y,\n\u001b[0;32m    843\u001b[0m         train\u001b[39m=\u001b[39;49mtrain,\n\u001b[0;32m    844\u001b[0m         test\u001b[39m=\u001b[39;49mtest,\n\u001b[0;32m    845\u001b[0m         parameters\u001b[39m=\u001b[39;49mparameters,\n\u001b[0;32m    846\u001b[0m         split_progress\u001b[39m=\u001b[39;49m(split_idx, n_splits),\n\u001b[0;32m    847\u001b[0m         candidate_progress\u001b[39m=\u001b[39;49m(cand_idx, n_candidates),\n\u001b[0;32m    848\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_and_score_kwargs,\n\u001b[0;32m    849\u001b[0m     )\n\u001b[0;32m    850\u001b[0m     \u001b[39mfor\u001b[39;49;00m (cand_idx, parameters), (split_idx, (train, test)) \u001b[39min\u001b[39;49;00m product(\n\u001b[0;32m    851\u001b[0m         \u001b[39menumerate\u001b[39;49m(candidate_params), \u001b[39menumerate\u001b[39;49m(cv\u001b[39m.\u001b[39;49msplit(X, y, groups))\n\u001b[0;32m    852\u001b[0m     )\n\u001b[0;32m    853\u001b[0m )\n\u001b[0;32m    855\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(out) \u001b[39m<\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m    856\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    857\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mNo fits were performed. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    858\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mWas the CV iterator empty? \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    859\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mWere there no candidates?\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    860\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\joblib\\parallel.py:1046\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdispatch_one_batch(iterator):\n\u001b[0;32m   1044\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterating \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_original_iterator \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m-> 1046\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdispatch_one_batch(iterator):\n\u001b[0;32m   1047\u001b[0m     \u001b[39mpass\u001b[39;00m\n\u001b[0;32m   1049\u001b[0m \u001b[39mif\u001b[39;00m pre_dispatch \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mall\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mor\u001b[39;00m n_jobs \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m   1050\u001b[0m     \u001b[39m# The iterable was consumed all at once by the above for loop.\u001b[39;00m\n\u001b[0;32m   1051\u001b[0m     \u001b[39m# No need to wait for async callbacks to trigger to\u001b[39;00m\n\u001b[0;32m   1052\u001b[0m     \u001b[39m# consumption.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\joblib\\parallel.py:861\u001b[0m, in \u001b[0;36mParallel.dispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    859\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m    860\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 861\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dispatch(tasks)\n\u001b[0;32m    862\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mTrue\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\joblib\\parallel.py:779\u001b[0m, in \u001b[0;36mParallel._dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    777\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[0;32m    778\u001b[0m     job_idx \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jobs)\n\u001b[1;32m--> 779\u001b[0m     job \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_backend\u001b[39m.\u001b[39;49mapply_async(batch, callback\u001b[39m=\u001b[39;49mcb)\n\u001b[0;32m    780\u001b[0m     \u001b[39m# A job can complete so quickly than its callback is\u001b[39;00m\n\u001b[0;32m    781\u001b[0m     \u001b[39m# called before we get here, causing self._jobs to\u001b[39;00m\n\u001b[0;32m    782\u001b[0m     \u001b[39m# grow. To ensure correct results ordering, .insert is\u001b[39;00m\n\u001b[0;32m    783\u001b[0m     \u001b[39m# used (rather than .append) in the following line\u001b[39;00m\n\u001b[0;32m    784\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jobs\u001b[39m.\u001b[39minsert(job_idx, job)\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py:208\u001b[0m, in \u001b[0;36mSequentialBackend.apply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mapply_async\u001b[39m(\u001b[39mself\u001b[39m, func, callback\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m    207\u001b[0m     \u001b[39m\"\"\"Schedule a func to be run\"\"\"\u001b[39;00m\n\u001b[1;32m--> 208\u001b[0m     result \u001b[39m=\u001b[39m ImmediateResult(func)\n\u001b[0;32m    209\u001b[0m     \u001b[39mif\u001b[39;00m callback:\n\u001b[0;32m    210\u001b[0m         callback(result)\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py:572\u001b[0m, in \u001b[0;36mImmediateResult.__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    569\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, batch):\n\u001b[0;32m    570\u001b[0m     \u001b[39m# Don't delay the application, to avoid keeping the input\u001b[39;00m\n\u001b[0;32m    571\u001b[0m     \u001b[39m# arguments in memory\u001b[39;00m\n\u001b[1;32m--> 572\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresults \u001b[39m=\u001b[39m batch()\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\joblib\\parallel.py:262\u001b[0m, in \u001b[0;36mBatchedCalls.__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    258\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m    259\u001b[0m     \u001b[39m# Set the default nested backend to self._backend but do not set the\u001b[39;00m\n\u001b[0;32m    260\u001b[0m     \u001b[39m# change the default number of processes to -1\u001b[39;00m\n\u001b[0;32m    261\u001b[0m     \u001b[39mwith\u001b[39;00m parallel_backend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend, n_jobs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_n_jobs):\n\u001b[1;32m--> 262\u001b[0m         \u001b[39mreturn\u001b[39;00m [func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    263\u001b[0m                 \u001b[39mfor\u001b[39;00m func, args, kwargs \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mitems]\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\joblib\\parallel.py:262\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    258\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m    259\u001b[0m     \u001b[39m# Set the default nested backend to self._backend but do not set the\u001b[39;00m\n\u001b[0;32m    260\u001b[0m     \u001b[39m# change the default number of processes to -1\u001b[39;00m\n\u001b[0;32m    261\u001b[0m     \u001b[39mwith\u001b[39;00m parallel_backend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend, n_jobs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_n_jobs):\n\u001b[1;32m--> 262\u001b[0m         \u001b[39mreturn\u001b[39;00m [func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    263\u001b[0m                 \u001b[39mfor\u001b[39;00m func, args, kwargs \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mitems]\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py:216\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m    215\u001b[0m     \u001b[39mwith\u001b[39;00m config_context(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig):\n\u001b[1;32m--> 216\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfunction(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:680\u001b[0m, in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[0;32m    678\u001b[0m         estimator\u001b[39m.\u001b[39mfit(X_train, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params)\n\u001b[0;32m    679\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 680\u001b[0m         estimator\u001b[39m.\u001b[39mfit(X_train, y_train, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params)\n\u001b[0;32m    682\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[0;32m    683\u001b[0m     \u001b[39m# Note fit time as time until error\u001b[39;00m\n\u001b[0;32m    684\u001b[0m     fit_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m start_time\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1528\u001b[0m, in \u001b[0;36mLogisticRegression.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m   1522\u001b[0m \u001b[39mif\u001b[39;00m effective_n_jobs(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_jobs) \u001b[39m!=\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m   1523\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m   1524\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39mn_jobs\u001b[39m\u001b[39m'\u001b[39m\u001b[39m > 1 does not have any effect when\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   1525\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m \u001b[39m\u001b[39m'\u001b[39m\u001b[39msolver\u001b[39m\u001b[39m'\u001b[39m\u001b[39m is set to \u001b[39m\u001b[39m'\u001b[39m\u001b[39mliblinear\u001b[39m\u001b[39m'\u001b[39m\u001b[39m. Got \u001b[39m\u001b[39m'\u001b[39m\u001b[39mn_jobs\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   1526\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m = \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(effective_n_jobs(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_jobs))\n\u001b[0;32m   1527\u001b[0m     )\n\u001b[1;32m-> 1528\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcoef_, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mintercept_, n_iter_ \u001b[39m=\u001b[39m _fit_liblinear(\n\u001b[0;32m   1529\u001b[0m     X,\n\u001b[0;32m   1530\u001b[0m     y,\n\u001b[0;32m   1531\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mC,\n\u001b[0;32m   1532\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfit_intercept,\n\u001b[0;32m   1533\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mintercept_scaling,\n\u001b[0;32m   1534\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mclass_weight,\n\u001b[0;32m   1535\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpenalty,\n\u001b[0;32m   1536\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdual,\n\u001b[0;32m   1537\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbose,\n\u001b[0;32m   1538\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmax_iter,\n\u001b[0;32m   1539\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtol,\n\u001b[0;32m   1540\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrandom_state,\n\u001b[0;32m   1541\u001b[0m     sample_weight\u001b[39m=\u001b[39;49msample_weight,\n\u001b[0;32m   1542\u001b[0m )\n\u001b[0;32m   1543\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_iter_ \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([n_iter_])\n\u001b[0;32m   1544\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\n",
      "File \u001b[1;32mc:\\Users\\dany0\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1186\u001b[0m, in \u001b[0;36m_fit_liblinear\u001b[1;34m(X, y, C, fit_intercept, intercept_scaling, class_weight, penalty, dual, verbose, max_iter, tol, random_state, multi_class, loss, epsilon, sample_weight)\u001b[0m\n\u001b[0;32m   1183\u001b[0m sample_weight \u001b[39m=\u001b[39m _check_sample_weight(sample_weight, X, dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39mfloat64)\n\u001b[0;32m   1185\u001b[0m solver_type \u001b[39m=\u001b[39m _get_liblinear_solver_type(multi_class, penalty, loss, dual)\n\u001b[1;32m-> 1186\u001b[0m raw_coef_, n_iter_ \u001b[39m=\u001b[39m liblinear\u001b[39m.\u001b[39;49mtrain_wrap(\n\u001b[0;32m   1187\u001b[0m     X,\n\u001b[0;32m   1188\u001b[0m     y_ind,\n\u001b[0;32m   1189\u001b[0m     sp\u001b[39m.\u001b[39;49misspmatrix(X),\n\u001b[0;32m   1190\u001b[0m     solver_type,\n\u001b[0;32m   1191\u001b[0m     tol,\n\u001b[0;32m   1192\u001b[0m     bias,\n\u001b[0;32m   1193\u001b[0m     C,\n\u001b[0;32m   1194\u001b[0m     class_weight_,\n\u001b[0;32m   1195\u001b[0m     max_iter,\n\u001b[0;32m   1196\u001b[0m     rnd\u001b[39m.\u001b[39;49mrandint(np\u001b[39m.\u001b[39;49miinfo(\u001b[39m\"\u001b[39;49m\u001b[39mi\u001b[39;49m\u001b[39m\"\u001b[39;49m)\u001b[39m.\u001b[39;49mmax),\n\u001b[0;32m   1197\u001b[0m     epsilon,\n\u001b[0;32m   1198\u001b[0m     sample_weight,\n\u001b[0;32m   1199\u001b[0m )\n\u001b[0;32m   1200\u001b[0m \u001b[39m# Regarding rnd.randint(..) in the above signature:\u001b[39;00m\n\u001b[0;32m   1201\u001b[0m \u001b[39m# seed for srand in range [0..INT_MAX); due to limitations in Numpy\u001b[39;00m\n\u001b[0;32m   1202\u001b[0m \u001b[39m# on 32-bit platforms, we can't get to the UINT_MAX limit that\u001b[39;00m\n\u001b[0;32m   1203\u001b[0m \u001b[39m# srand supports\u001b[39;00m\n\u001b[0;32m   1204\u001b[0m n_iter_ \u001b[39m=\u001b[39m \u001b[39mmax\u001b[39m(n_iter_)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "logreg = LogisticRegression(solver='liblinear', max_iter=1000)\n",
    "param_grid = {'C': [10, 100], 'penalty': ['l1', 'l2']}\n",
    "logreg_cv = GridSearchCV(logreg, param_grid, cv=10, scoring='f1')\n",
    "logreg_cv.fit(data_text, data_label)\n",
    "print(\"Tuned hyperparameters: \", logreg_cv.best_params_)\n",
    "print(\"Best score: \", logreg_cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "\n",
    "discretizer = KBinsDiscretizer(n_bins=10, encode='ordinal', strategy='uniform')\n",
    "data_text['claim_est_payout'] = discretizer.fit_transform(data_text['claim_est_payout'].values.reshape(-1, 1))\n",
    "data_text['vehicle_price'] = discretizer.fit_transform(data_text['vehicle_price'].values.reshape(-1, 1))\n",
    "\n",
    "scaler = MaxAbsScaler()\n",
    "data_text = scaler.fit_transform(data_text)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tree:  0.21598660235860184\n",
      "Logreg:  0.024218024385331598\n",
      "Dummy:  0.0\n"
     ]
    }
   ],
   "source": [
    "scores_tree = cross_val_score(tree, data_text, data_label, cv=10, scoring='f1')\n",
    "scores_logreg = cross_val_score(logreg, data_text, data_label, cv=10, scoring='f1')\n",
    "scores_dummy = cross_val_score(dummy, data_text, data_label, cv=10, scoring='f1')\n",
    "\n",
    "print(\"Tree: \", scores_tree.mean())\n",
    "print(\"Logreg: \", scores_logreg.mean())\n",
    "print(\"Dummy: \", scores_dummy.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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_of_driver</th>\n",
       "      <th>gender</th>\n",
       "      <th>annual_income</th>\n",
       "      <th>high_education_ind</th>\n",
       "      <th>address_change_ind</th>\n",
       "      <th>living_status</th>\n",
       "      <th>claim_day_of_week</th>\n",
       "      <th>accident_site</th>\n",
       "      <th>past_num_of_claims</th>\n",
       "      <th>channel</th>\n",
       "      <th>claim_est_payout</th>\n",
       "      <th>age_of_vehicle</th>\n",
       "      <th>vehicle_category</th>\n",
       "      <th>vehicle_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>38301</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7530.940993</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12885.45235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>30445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2966.024895</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29429.45218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>38923</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6283.888333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>21701.18195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>40605</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6169.747994</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>13198.27344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>36380</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4541.387150</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>38060.21122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_of_driver  gender  annual_income  high_education_ind  \\\n",
       "0             46       1          38301                   1   \n",
       "1             21       0          30445                   0   \n",
       "2             49       0          38923                   0   \n",
       "3             58       0          40605                   1   \n",
       "4             38       1          36380                   1   \n",
       "\n",
       "   address_change_ind  living_status  claim_day_of_week  accident_site  \\\n",
       "0                   1              1                  0              1   \n",
       "1                   1              1                  4              0   \n",
       "2                   1              0                  5              1   \n",
       "3                   0              0                  4              1   \n",
       "4                   0              1                  5              0   \n",
       "\n",
       "   past_num_of_claims  channel  claim_est_payout  age_of_vehicle  \\\n",
       "0                   1        0       7530.940993             9.0   \n",
       "1                   1        1       2966.024895             4.0   \n",
       "2                   0        0       6283.888333             3.0   \n",
       "3                   3        0       6169.747994             4.0   \n",
       "4                   0        0       4541.387150             7.0   \n",
       "\n",
       "   vehicle_category  vehicle_price  \n",
       "0                 0    12885.45235  \n",
       "1                 1    29429.45218  \n",
       "2                 0    21701.18195  \n",
       "3                 2    13198.27344  \n",
       "4                 2    38060.21122  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df =  pd.read_csv('./ClaimCars/training_set_fd.csv', delimiter=';')\n",
    "\n",
    "df = df.dropna()\n",
    "\n",
    "data_text=df.drop(['claim_number', 'vehicle_color', 'vehicle_weight', 'marital_status', 'fraud'], axis=1)\n",
    "\n",
    "data_label=df['fraud']\n",
    "categoricals = data_text.select_dtypes(include='object').columns\n",
    "for col in categoricals:\n",
    "    data_text[col] = le.fit_transform(data_text[col])\n",
    "\n",
    "data_text.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.pipeline import FeatureUnion\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "def identity(X):\n",
    "  return X\n",
    "\n",
    "\n",
    "Identity = FunctionTransformer(identity, validate=True)\n",
    "\n",
    "transf = ColumnTransformer([('payout_discretizer', KBinsDiscretizer(n_bins=5, encode='ordinal', strategy='uniform'), ['claim_est_payout']),\n",
    "                            ('vehicle_price_discretizer', KBinsDiscretizer(n_bins=5, encode='ordinal', strategy='uniform'),['vehicle_price'])])\n",
    "\n",
    "union = FeatureUnion([('identity', Identity), ('transformer', transf)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logreg:  0.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "pipe = Pipeline([('union', union), ('logreg', logreg)])\n",
    "\n",
    "pipe.fit(data_text, data_label)\n",
    "y_pred = pipe.predict(data_text)\n",
    "print(\"Logreg: \", f1_score(data_label, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned hyperparameters:  {'feature_selection__k': 5, 'transformer__transformer__payout_discretizer__n_bins': 5, 'transformer__transformer__vehicle_price_discretizer__n_bins': 5}\n",
      "Best score:  0.02015659724234347\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "pipe = Pipeline([('transformer', union), ('feature_selection', SelectKBest(k=2)), ('logreg', logreg)])\n",
    "\n",
    "param_grid = { 'transformer__transformer__payout_discretizer__n_bins': [5, 10, 15],\n",
    "                'transformer__transformer__vehicle_price_discretizer__n_bins': [5, 10, 15],\n",
    "                'feature_selection__k': [1, 2, 3, 4, 5, 6, 7, 8, 9]}\n",
    "grid = GridSearchCV(pipe, param_grid, cv=10, scoring='f1')\n",
    "grid.fit(data_text, data_label)\n",
    "print(\"Tuned hyperparameters: \", grid.best_params_)\n",
    "print(\"Best score: \", grid.best_score_)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}