Insurance_Prediction/Insurance_Claim_Prediction_Fortune_Adekogbe.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('train_data.csv')\n",
    "test_df = pd.read_csv('test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [train_df,test_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>YearOfObservation</th>\n",
       "      <th>Insured_Period</th>\n",
       "      <th>Residential</th>\n",
       "      <th>Building_Painted</th>\n",
       "      <th>Building_Fenced</th>\n",
       "      <th>Garden</th>\n",
       "      <th>Settlement</th>\n",
       "      <th>Building Dimension</th>\n",
       "      <th>Building_Type</th>\n",
       "      <th>Date_of_Occupancy</th>\n",
       "      <th>NumberOfWindows</th>\n",
       "      <th>Geo_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>H11920</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>V</td>\n",
       "      <td>N</td>\n",
       "      <td>O</td>\n",
       "      <td>R</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H11921</td>\n",
       "      <td>4</td>\n",
       "      <td>0.997268</td>\n",
       "      <td>0</td>\n",
       "      <td>V</td>\n",
       "      <td>N</td>\n",
       "      <td>O</td>\n",
       "      <td>R</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3310</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>H9805</td>\n",
       "      <td>7</td>\n",
       "      <td>0.369863</td>\n",
       "      <td>0</td>\n",
       "      <td>V</td>\n",
       "      <td>V</td>\n",
       "      <td>V</td>\n",
       "      <td>U</td>\n",
       "      <td>790.0</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>.</td>\n",
       "      <td>3310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H7493</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>V</td>\n",
       "      <td>N</td>\n",
       "      <td>O</td>\n",
       "      <td>R</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H7494</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>V</td>\n",
       "      <td>N</td>\n",
       "      <td>O</td>\n",
       "      <td>R</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer Id  YearOfObservation  Insured_Period Residential Building_Painted  \\\n",
       "0      H11920                  7        1.000000           0                V   \n",
       "1      H11921                  4        0.997268           0                V   \n",
       "2       H9805                  7        0.369863           0                V   \n",
       "3       H7493                  6        1.000000           0                V   \n",
       "4       H7494                  4        1.000000           0                V   \n",
       "\n",
       "  Building_Fenced Garden Settlement  Building Dimension Building_Type  \\\n",
       "0               N      O          R               300.0             1   \n",
       "1               N      O          R               300.0             1   \n",
       "2               V      V          U               790.0             1   \n",
       "3               N      O          R              1405.0             1   \n",
       "4               N      O          R              1405.0             1   \n",
       "\n",
       "   Date_of_Occupancy NumberOfWindows Geo_Code  \n",
       "0               60.0               3     3310  \n",
       "1               60.0               3     3310  \n",
       "2               60.0               .     3310  \n",
       "3               16.0               3     3321  \n",
       "4               16.0               3     3321  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Claim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>H0</td>\n",
       "      <td>1</td>\n",
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       "      <td>H10000</td>\n",
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       "      <th>2</th>\n",
       "      <td>H10001</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>H10002</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H10003</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Customer Id  Claim\n",
       "0          H0      1\n",
       "1      H10000      1\n",
       "2      H10001      1\n",
       "3      H10002      1\n",
       "4      H10003      1"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.000000    5325\n",
       "0.997268     547\n",
       "0.000000     165\n",
       "0.747945      60\n",
       "0.495890      49\n",
       "            ... \n",
       "0.797260       1\n",
       "0.336986       1\n",
       "0.372603       1\n",
       "0.546448       1\n",
       "0.698630       1\n",
       "Name: Insured_Period, Length: 401, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummable = ['Building_Type','Residential', 'Building_Painted', 'Building_Fenced', 'Garden', 'Settlement','NumberOfWindows']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   .    3551\n",
       "4        939\n",
       "3        844\n",
       "5        639\n",
       "2        363\n",
       "6        306\n",
       "7        211\n",
       "8        116\n",
       "1         75\n",
       ">=10      67\n",
       "9         49\n",
       "Name: NumberOfWindows, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['NumberOfWindows'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fill with mean or drop for train_df\n",
    "for datum in data:v  \n",
    "    x = round(datum['Building Dimension'].mean())\n",
    "    datum['Building Dimension'].fillna(x,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1960.0    1818\n",
       "1980.0    1155\n",
       "1988.0     530\n",
       "1900.0     371\n",
       "1970.0     285\n",
       "          ... \n",
       "1908.0       1\n",
       "1946.0       1\n",
       "1939.0       1\n",
       "1931.0       1\n",
       "1927.0       1\n",
       "Name: Date_of_Occupancy, Length: 134, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Date_of_Occupancy'].value_counts() #Fill with mean,mode and median in varying proportions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "for datum in data:\n",
    "    x = datum['Geo_Code'].mode()[0]\n",
    "    datum['Geo_Code'].fillna(x,inplace=True) #Fill with mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_nan(df,feature_name,new_values,weights):\n",
    "    '''\n",
    "    A function that replaces the nan values with some different values in different proportions.\n",
    "    \n",
    "    Parameters:\n",
    "    --------------------------------------------------\n",
    "        df: a dataframe  \n",
    "        feature_name: the name of the feature to be worked on as a string\n",
    "        new_values: this is a list of the values to replace the nan values as strings\n",
    "        weights: this is a list of the weights assigned to each value. Its sum is to be equal to 1.\n",
    "    Returns:\n",
    "    ----------------------------------------------------------------------\n",
    "        The edited feature as a numpy array.\n",
    "    '''\n",
    "    assert sum(weights) ==1.0,'Weights do not add up to one...'\n",
    "    assert len(new_values)==len(weights),'New values do not correspond with weights'\n",
    "    from random import choices\n",
    "    mask= df[feature_name].isna()\n",
    "    length = sum(mask)\n",
    "    replacement = choices(new_values,weights =weights,k=length)\n",
    "    df.loc[mask,feature_name]=replacement\n",
    "    return df[feature_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "for datum in data:\n",
    "    y = round(datum['Date_of_Occupancy'].mean())\n",
    "    z = datum['Date_of_Occupancy'].mode()[0]\n",
    "    datum['Date_of_Occupancy'] = replace_nan(datum,'Date_of_Occupancy',[y,z],[0.5,0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer Id            object\n",
       "YearOfObservation       int64\n",
       "Insured_Period         object\n",
       "Residential            object\n",
       "Building_Painted       object\n",
       "Building_Fenced        object\n",
       "Garden                 object\n",
       "Settlement             object\n",
       "Building Dimension    float64\n",
       "Building_Type          object\n",
       "Date_of_Occupancy     float64\n",
       "NumberOfWindows        object\n",
       "Geo_Code               object\n",
       "Claim                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "for datum in data:\n",
    "    datum['YearOfObservation']= 2020 - datum['YearOfObservation']\n",
    "    datum['Date_of_Occupancy']= 2020 - datum['Date_of_Occupancy']\n",
    "    datum['Insured_Period']=datum['Insured_Period'].astype('float64')\n",
    "    datum['Residential']=datum['Residential'].astype('object')\n",
    "    datum['Building_Type']=datum['Building_Type'].astype('object')\n",
    "    datum['Geo_Code']=datum['Geo_Code'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "dum_train = pd.get_dummies(train_df[dummable])\n",
    "dum_train['Customer Id']=train_df['Customer Id']\n",
    "train_data = pd.merge(train_df,dum_train,on='Customer Id')\n",
    "train_data.drop(dummable,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "dum_test = pd.get_dummies(test_df[dummable])\n",
    "dum_test['Customer Id']=test_df['Customer Id']\n",
    "test_data = pd.merge(test_df,dum_test,on='Customer Id')\n",
    "test_data.drop(dummable,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data.columns)==len(test_data.columns)+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = train_df['Claim']\n",
    "X_train = train_data.drop(['Customer Id','Claim','Geo_Code'],axis=1)\n",
    "X_test = test_data.drop(['Customer Id','Geo_Code'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.2"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "\n",
    "Y_prediction = random_forest.predict(X_test)\n",
    "\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame()\n",
    "submission['Customer Id'] = test_df['Customer Id']\n",
    "submission['Claim'] = Y_prediction\n",
    "submission.to_csv('solution00.csv',index=False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=XGBClassifier()\n",
    "model.fit(X_train,Y_train)\n",
    "Y_xgb = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearOfObservation</th>\n",
       "      <th>Insured_Period</th>\n",
       "      <th>Building Dimension</th>\n",
       "      <th>Date_of_Occupancy</th>\n",
       "      <th>Building_Type_1</th>\n",
       "      <th>Building_Type_2</th>\n",
       "      <th>Building_Type_3</th>\n",
       "      <th>Building_Type_4</th>\n",
       "      <th>Residential_0</th>\n",
       "      <th>Residential_1</th>\n",
       "      <th>...</th>\n",
       "      <th>NumberOfWindows_1</th>\n",
       "      <th>NumberOfWindows_2</th>\n",
       "      <th>NumberOfWindows_3</th>\n",
       "      <th>NumberOfWindows_4</th>\n",
       "      <th>NumberOfWindows_5</th>\n",
       "      <th>NumberOfWindows_6</th>\n",
       "      <th>NumberOfWindows_7</th>\n",
       "      <th>NumberOfWindows_8</th>\n",
       "      <th>NumberOfWindows_9</th>\n",
       "      <th>NumberOfWindows_&gt;=10</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3066</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3067</th>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      YearOfObservation  Insured_Period  Building Dimension  \\\n",
       "3064                  5             1.0              1667.0   \n",
       "3065                  8             1.0              1667.0   \n",
       "3066                  8             1.0              1667.0   \n",
       "3067                  7             1.0              1667.0   \n",
       "3068                  8             1.0              1667.0   \n",
       "\n",
       "      Date_of_Occupancy  Building_Type_1  Building_Type_2  Building_Type_3  \\\n",
       "3064              120.0                0                0                0   \n",
       "3065               72.0                0                1                0   \n",
       "3066               27.0                0                1                0   \n",
       "3067              220.0                1                0                0   \n",
       "3068               70.0                0                1                0   \n",
       "\n",
       "      Building_Type_4  Residential_0  Residential_1  ...  NumberOfWindows_1  \\\n",
       "3064                1              1              0  ...                  0   \n",
       "3065                0              1              0  ...                  0   \n",
       "3066                0              1              0  ...                  0   \n",
       "3067                0              1              0  ...                  0   \n",
       "3068                0              1              0  ...                  0   \n",
       "\n",
       "      NumberOfWindows_2  NumberOfWindows_3  NumberOfWindows_4  \\\n",
       "3064                  0                  0                  0   \n",
       "3065                  0                  0                  0   \n",
       "3066                  0                  0                  0   \n",
       "3067                  0                  0                  0   \n",
       "3068                  0                  0                  0   \n",
       "\n",
       "      NumberOfWindows_5  NumberOfWindows_6  NumberOfWindows_7  \\\n",
       "3064                  0                  0                  0   \n",
       "3065                  0                  0                  0   \n",
       "3066                  0                  0                  0   \n",
       "3067                  0                  0                  0   \n",
       "3068                  0                  0                  0   \n",
       "\n",
       "      NumberOfWindows_8  NumberOfWindows_9  NumberOfWindows_>=10  \n",
       "3064                  0                  0                     0  \n",
       "3065                  0                  0                     0  \n",
       "3066                  0                  0                     0  \n",
       "3067                  0                  0                     0  \n",
       "3068                  0                  0                     0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Claim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3063</th>\n",
       "      <td>H9987</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3064</th>\n",
       "      <td>H9988</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3065</th>\n",
       "      <td>H9994</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3066</th>\n",
       "      <td>H9996</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3067</th>\n",
       "      <td>H9998</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     Customer Id  Claim\n",
       "3063       H9987      1\n",
       "3064       H9988      1\n",
       "3065       H9994      1\n",
       "3066       H9996      1\n",
       "3067       H9998      1"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission1 = pd.DataFrame()\n",
    "submission1['Customer Id'] = test_df['Customer Id']\n",
    "submission1['Claim'] = Y_xgb\n",
    "submission1.to_csv('solution0a.csv',index=False )"
   ]
  }
 ],
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