Insurance_Prediction.ipynb

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   "metadata": {
    "id": "pitmwJ6eZrEZ"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
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       "      <td>28</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "            id  Gender  Age  ...  Policy_Sales_Channel  Vintage  Response\n",
       "0       350957    Male   50  ...                   124      240         0\n",
       "1       239788  Female   48  ...                   124      274         0\n",
       "2       348272  Female   34  ...                   156      138         1\n",
       "3        73940  Female   43  ...                   124      223         0\n",
       "4       292871    Male   29  ...                   152      106         0\n",
       "...        ...     ...  ...  ...                   ...      ...       ...\n",
       "304882  367674    Male   69  ...                    26      270         0\n",
       "304883  162796    Male   24  ...                   152       68         0\n",
       "304884  121015  Female   46  ...                   154      155         0\n",
       "304885  309301  Female   77  ...                   122       60         0\n",
       "304886   96979  Female   25  ...                   152      120         0\n",
       "\n",
       "[304887 rows x 12 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {
      "tags": []
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv(\"TRAIN.csv\")\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <td>No</td>\n",
       "      <td>31721</td>\n",
       "      <td>124</td>\n",
       "      <td>83</td>\n",
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       "      <th>76218</th>\n",
       "      <td>35090</td>\n",
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       "      <td>1</td>\n",
       "      <td>30</td>\n",
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       "      <td>52767</td>\n",
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       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>41533</td>\n",
       "      <td>124</td>\n",
       "      <td>133</td>\n",
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       "      <th>76220</th>\n",
       "      <td>298399</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>33902</td>\n",
       "      <td>160</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76221</th>\n",
       "      <td>364035</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>35023</td>\n",
       "      <td>152</td>\n",
       "      <td>277</td>\n",
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       "<p>76222 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "           id  Gender  Age  ...  Annual_Premium  Policy_Sales_Channel  Vintage\n",
       "0      291378    Male   31  ...            2630                   156      152\n",
       "1      154746  Female   24  ...           28105                   152      267\n",
       "2      218512  Female   25  ...           29302                   152      139\n",
       "3      173930    Male   49  ...           28357                    26       12\n",
       "4      241471    Male   55  ...           33288                    26       44\n",
       "...       ...     ...  ...  ...             ...                   ...      ...\n",
       "76217  311352    Male   44  ...           31721                   124       83\n",
       "76218   35090  Female   22  ...           27302                   152      207\n",
       "76219   52767    Male   51  ...           41533                   124      133\n",
       "76220  298399    Male   21  ...           33902                   160       78\n",
       "76221  364035  Female   43  ...           35023                   152      277\n",
       "\n",
       "[76222 rows x 11 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv(\"TEST.csv\")\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vMsAZoneeZeZ",
    "outputId": "59579afa-227e-4e74-f3b0-a1d8e15a85ea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                      0\n",
       "Gender                  0\n",
       "Age                     0\n",
       "Driving_License         0\n",
       "Region_Code             0\n",
       "Previously_Insured      0\n",
       "Vehicle_Age             0\n",
       "Vehicle_Damage          0\n",
       "Annual_Premium          0\n",
       "Policy_Sales_Channel    0\n",
       "Vintage                 0\n",
       "Response                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N5eCGUbGegCN",
    "outputId": "cc718fc6-55a4-456d-8826-cbf2dd7c0caa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                      0\n",
       "Gender                  0\n",
       "Age                     0\n",
       "Driving_License         0\n",
       "Region_Code             0\n",
       "Previously_Insured      0\n",
       "Vehicle_Age             0\n",
       "Vehicle_Damage          0\n",
       "Annual_Premium          0\n",
       "Policy_Sales_Channel    0\n",
       "Vintage                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.isnull().sum()"
   ]
  },
  {
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    "id": "UTLIm-Iqejl4",
    "outputId": "7478d443-6d46-4b96-bf38-176ece98b074"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 304887 entries, 0 to 304886\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count   Dtype \n",
      "---  ------                --------------   ----- \n",
      " 0   id                    304887 non-null  int64 \n",
      " 1   Gender                304887 non-null  object\n",
      " 2   Age                   304887 non-null  int64 \n",
      " 3   Driving_License       304887 non-null  int64 \n",
      " 4   Region_Code           304887 non-null  int64 \n",
      " 5   Previously_Insured    304887 non-null  int64 \n",
      " 6   Vehicle_Age           304887 non-null  object\n",
      " 7   Vehicle_Damage        304887 non-null  object\n",
      " 8   Annual_Premium        304887 non-null  int64 \n",
      " 9   Policy_Sales_Channel  304887 non-null  int64 \n",
      " 10  Vintage               304887 non-null  int64 \n",
      " 11  Response              304887 non-null  int64 \n",
      "dtypes: int64(9), object(3)\n",
      "memory usage: 27.9+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "base_uri": "https://localhost:8080/",
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    "id": "ZW4ZpOulepiv",
    "outputId": "04e81490-9981-483a-c436-a085acf6b80c"
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   "outputs": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>304887.000000</td>\n",
       "      <td>304887.000000</td>\n",
       "      <td>304887.000000</td>\n",
       "      <td>304887.000000</td>\n",
       "      <td>304887.000000</td>\n",
       "      <td>304887.000000</td>\n",
       "      <td>304887.000000</td>\n",
       "      <td>304887.000000</td>\n",
       "      <td>304887.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>190464.686973</td>\n",
       "      <td>38.832545</td>\n",
       "      <td>0.997875</td>\n",
       "      <td>26.381348</td>\n",
       "      <td>0.458435</td>\n",
       "      <td>30567.921318</td>\n",
       "      <td>112.021946</td>\n",
       "      <td>154.293105</td>\n",
       "      <td>0.122357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>109998.823015</td>\n",
       "      <td>15.520667</td>\n",
       "      <td>0.046053</td>\n",
       "      <td>13.235034</td>\n",
       "      <td>0.498270</td>\n",
       "      <td>17255.545191</td>\n",
       "      <td>54.212188</td>\n",
       "      <td>83.713258</td>\n",
       "      <td>0.327698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2630.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>95183.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24409.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>190485.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31673.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>285642.500000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39393.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>381108.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>540165.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id            Age  ...        Vintage       Response\n",
       "count  304887.000000  304887.000000  ...  304887.000000  304887.000000\n",
       "mean   190464.686973      38.832545  ...     154.293105       0.122357\n",
       "std    109998.823015      15.520667  ...      83.713258       0.327698\n",
       "min         1.000000      20.000000  ...      10.000000       0.000000\n",
       "25%     95183.500000      25.000000  ...      82.000000       0.000000\n",
       "50%    190485.000000      36.000000  ...     154.000000       0.000000\n",
       "75%    285642.500000      49.000000  ...     227.000000       0.000000\n",
       "max    381108.000000      85.000000  ...     299.000000       1.000000\n",
       "\n",
       "[8 rows x 9 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "3yLdL8TNe3A0",
    "outputId": "be25c1ab-8908-433d-a977-01dffcbf7c54"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f134f93c320>"
      ]
     },
     "execution_count": 119,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = train_df.Response.value_counts().index, y = train_df.Response.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "3bd6VuNnfPkL",
    "outputId": "2e394f81-60b1-4c2d-a13c-79a0216d64ea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f134f899320>"
      ]
     },
     "execution_count": 120,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Response', data=train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 583
    },
    "id": "17QvrSE-m-zP",
    "outputId": "5f62b8a2-51d8-40cf-fa50-f8c10d9fafd0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting correlation\n",
    "corrmat= train_df.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(14,8))\n",
    "\n",
    "#To plot heatmap\n",
    "g=sns.heatmap(train_df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gMSu5j8J8SMm",
    "outputId": "1a743d70-0c57-4c26-c1c2-61174bae8e18"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Gender', 'Vehicle_Age', 'Vehicle_Damage']"
      ]
     },
     "execution_count": 122,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols = train_df.select_dtypes(include = 'object').columns.tolist()\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 438
    },
    "id": "bcFDvETvn6Hm",
    "outputId": "fe9f96f7-cb77-4697-9fc3-d2fbf3d0e7f0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Vehicle_Age_1-2 Year</th>\n",
       "      <th>Vehicle_Age_&lt; 1 Year</th>\n",
       "      <th>Vehicle_Age_&gt; 2 Years</th>\n",
       "      <th>Vehicle_Damage_No</th>\n",
       "      <th>Vehicle_Damage_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>350957</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>35915</td>\n",
       "      <td>124</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>239788</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>26873</td>\n",
       "      <td>124</td>\n",
       "      <td>274</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>348272</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>2630</td>\n",
       "      <td>156</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73940</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>46877</td>\n",
       "      <td>124</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>292871</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>38009</td>\n",
       "      <td>152</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304882</th>\n",
       "      <td>367674</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>28321</td>\n",
       "      <td>26</td>\n",
       "      <td>270</td>\n",
       "      <td>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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304883</th>\n",
       "      <td>162796</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>34455</td>\n",
       "      <td>152</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304884</th>\n",
       "      <td>121015</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>130427</td>\n",
       "      <td>154</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304885</th>\n",
       "      <td>309301</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>41941</td>\n",
       "      <td>122</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304886</th>\n",
       "      <td>96979</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>34359</td>\n",
       "      <td>152</td>\n",
       "      <td>120</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>304887 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  Age  ...  Vehicle_Damage_No  Vehicle_Damage_Yes\n",
       "0       350957   50  ...                  0                   1\n",
       "1       239788   48  ...                  0                   1\n",
       "2       348272   34  ...                  0                   1\n",
       "3        73940   43  ...                  0                   1\n",
       "4       292871   29  ...                  1                   0\n",
       "...        ...  ...  ...                ...                 ...\n",
       "304882  367674   69  ...                  0                   1\n",
       "304883  162796   24  ...                  1                   0\n",
       "304884  121015   46  ...                  0                   1\n",
       "304885  309301   77  ...                  1                   0\n",
       "304886   96979   25  ...                  1                   0\n",
       "\n",
       "[304887 rows x 16 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.get_dummies(train_df, columns=cat_cols)\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 438
    },
    "id": "KWiV4Eoq9UgV",
    "outputId": "99feabdc-27d7-4802-e55b-a05f1e76509f"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
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       "      <td>241471</td>\n",
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       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>33288</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <th>76218</th>\n",
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       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
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       "      <td>152</td>\n",
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       "      <td>1</td>\n",
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       "      <th>76219</th>\n",
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       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>41533</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>76220</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>76221</th>\n",
       "      <td>364035</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>35023</td>\n",
       "      <td>152</td>\n",
       "      <td>277</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76222 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  Age  ...  Vehicle_Damage_No  Vehicle_Damage_Yes\n",
       "0      291378   31  ...                  0                   1\n",
       "1      154746   24  ...                  1                   0\n",
       "2      218512   25  ...                  1                   0\n",
       "3      173930   49  ...                  0                   1\n",
       "4      241471   55  ...                  0                   1\n",
       "...       ...  ...  ...                ...                 ...\n",
       "76217  311352   44  ...                  1                   0\n",
       "76218   35090   22  ...                  1                   0\n",
       "76219   52767   51  ...                  1                   0\n",
       "76220  298399   21  ...                  1                   0\n",
       "76221  364035   43  ...                  0                   1\n",
       "\n",
       "[76222 rows x 15 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.get_dummies(test_df, columns=cat_cols)\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 589
    },
    "id": "e5JVkhJP8ou_",
    "outputId": "2282dc00-4b90-47a5-eb4f-18f97eab0087"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting correlation\n",
    "corrmat= train_df.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(14,8))\n",
    "\n",
    "#To plot heatmap\n",
    "g=sns.heatmap(train_df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Kjln1nT19FCo",
    "outputId": "063b8063-7ac2-4a84-dc1b-f52029b718c8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((304887, 16), (76222, 15))"
      ]
     },
     "execution_count": 126,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I4ii_2ji9Q2C",
    "outputId": "00c94dc2-9d30-44ae-ecfb-39f90164a00f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((304887, 13), (304887,), (76222, 13))"
      ]
     },
     "execution_count": 127,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = train_df['Response']\n",
    "X = train_df\n",
    "test = test_df.drop(['id', 'Vintage'], axis=1)\n",
    "X.drop(columns=['Response', 'id', 'Vintage'], inplace=True)\n",
    "X.shape, Y.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "U_1pMS2_-9L5"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iLtiiqB6B9wo"
   },
   "outputs": [],
   "source": [
    "X_train = X_train.rename({\"Vehicle_Age_< 1 Year\": \"Vehicle_Age_L 1 Year\", \"Vehicle_Age_> 2 Years\": \"Vehicle_Age_G 2 Years\"}, axis='columns')\n",
    "X_val = X_val.rename({\"Vehicle_Age_< 1 Year\": \"Vehicle_Age_L 1 Year\", \"Vehicle_Age_> 2 Years\": \"Vehicle_Age_G 2 Years\"}, axis='columns')\n",
    "test = test.rename({\"Vehicle_Age_< 1 Year\": \"Vehicle_Age_L 1 Year\", \"Vehicle_Age_> 2 Years\": \"Vehicle_Age_G 2 Years\"}, axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u2kDtKwN_RSq",
    "outputId": "ddccb66e-0adf-4374-8d85-6b4be0b7e152"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((243909, 13), (60978, 13), (243909,), (60978,), (76222, 13))"
      ]
     },
     "execution_count": 130,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_val.shape, y_train.shape, y_val.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bPIW4KJPMJ6N",
    "outputId": "c38c5f6f-3527-454e-de3f-85be6f95fc69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Driving_License', 'Region_Code', 'Previously_Insured',\n",
       "       'Annual_Premium', 'Policy_Sales_Channel', 'Gender_Female',\n",
       "       'Gender_Male', 'Vehicle_Age_1-2 Year', 'Vehicle_Age_L 1 Year',\n",
       "       'Vehicle_Age_G 2 Years', 'Vehicle_Damage_No', 'Vehicle_Damage_Yes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 131,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mj12yyGtw--8",
    "outputId": "9681a5f6-d60e-4abe-b3a3-51d8fb718b04"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.88307443e+01 9.97978754e-01 2.63864064e+01 4.58773559e-01\n",
      " 3.05399534e+04 1.11923201e+02 4.59687834e-01 5.40312166e-01\n",
      " 5.25372167e-01 4.32353870e-01 4.22739628e-02 4.95250278e-01\n",
      " 5.04749722e-01]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "print(scaler.mean_)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_val = scaler.transform(X_val)\n",
    "test = scaler.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jx2C9T7sz_m8",
    "outputId": "5cdffa0b-77f2-4c05-82c0-cd7ca8103842"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.3974722 ,  0.04500377, -1.38995888, ..., -0.21009506,\n",
       "        -0.99054525,  0.99054525],\n",
       "       [ 0.526328  ,  0.04500377,  1.48273066, ..., -0.21009506,\n",
       "        -0.99054525,  0.99054525],\n",
       "       [-1.01994162,  0.04500377,  0.12198299, ..., -0.21009506,\n",
       "         1.009545  , -1.009545  ],\n",
       "       ...,\n",
       "       [-1.08436952,  0.04500377, -1.01197341, ..., -0.21009506,\n",
       "         1.009545  , -1.009545  ],\n",
       "       [-1.01994162,  0.04500377,  1.1047452 , ..., -0.21009506,\n",
       "         1.009545  , -1.009545  ],\n",
       "       [ 2.39473712,  0.04500377,  1.63392485, ..., -0.21009506,\n",
       "         1.009545  , -1.009545  ]])"
      ]
     },
     "execution_count": 133,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "12djCJz6Jdy4"
   },
   "source": [
    "## SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RyzOXwnVMjOA"
   },
   "outputs": [],
   "source": [
    "#import six\n",
    "#import sys\n",
    "#sys.modules['sklearn.externals.six'] = six"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4Ww02s70F3iW",
    "outputId": "f9d0b89f-509c-4f6f-e95d-9cabda8e855e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "sm = SMOTE(sampling_strategy= 1.0, random_state = 0)\n",
    "X_train_res, y_train_res = sm.fit_resample(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 438
    },
    "id": "YyGh1-P5qYih",
    "outputId": "bb67c2a6-3f0f-4d12-d538-3d0b66f3dcd7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Vehicle_Age_1-2 Year</th>\n",
       "      <th>Vehicle_Age_&lt; 1 Year</th>\n",
       "      <th>Vehicle_Age_&gt; 2 Years</th>\n",
       "      <th>Vehicle_Damage_No</th>\n",
       "      <th>Vehicle_Damage_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.397472</td>\n",
       "      <td>0.045004</td>\n",
       "      <td>-1.389959</td>\n",
       "      <td>-0.920682</td>\n",
       "      <td>-1.620694</td>\n",
       "      <td>0.812469</td>\n",
       "      <td>-0.922378</td>\n",
       "      <td>0.922378</td>\n",
       "      <td>0.95048</td>\n",
       "      <td>-0.872732</td>\n",
       "      <td>-0.210095</td>\n",
       "      <td>-0.990545</td>\n",
       "      <td>0.990545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.526328</td>\n",
       "      <td>0.045004</td>\n",
       "      <td>1.482731</td>\n",
       "      <td>-0.920682</td>\n",
       "      <td>0.002325</td>\n",
       "      <td>-1.583825</td>\n",
       "      <td>-0.922378</td>\n",
       "      <td>0.922378</td>\n",
       "      <td>0.95048</td>\n",
       "      <td>-0.872732</td>\n",
       "      <td>-0.210095</td>\n",
       "      <td>-0.990545</td>\n",
       "      <td>0.990545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.019942</td>\n",
       "      <td>0.045004</td>\n",
       "      <td>0.121983</td>\n",
       "      <td>1.086151</td>\n",
       "      <td>1.636320</td>\n",
       "      <td>0.738737</td>\n",
       "      <td>1.084154</td>\n",
       "      <td>-1.084154</td>\n",
       "      <td>-1.05210</td>\n",
       "      <td>1.145827</td>\n",
       "      <td>-0.210095</td>\n",
       "      <td>1.009545</td>\n",
       "      <td>-1.009545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.333044</td>\n",
       "      <td>0.045004</td>\n",
       "      <td>0.273177</td>\n",
       "      <td>1.086151</td>\n",
       "      <td>-0.626849</td>\n",
       "      <td>0.222612</td>\n",
       "      <td>-0.922378</td>\n",
       "      <td>0.922378</td>\n",
       "      <td>0.95048</td>\n",
       "      <td>-0.872732</td>\n",
       "      <td>-0.210095</td>\n",
       "      <td>1.009545</td>\n",
       "      <td>-1.009545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.235035</td>\n",
       "      <td>0.045004</td>\n",
       "      <td>0.121983</td>\n",
       "      <td>1.086151</td>\n",
       "      <td>0.156498</td>\n",
       "      <td>-1.583825</td>\n",
       "      <td>-0.922378</td>\n",
       "      <td>0.922378</td>\n",
       "      <td>0.95048</td>\n",
       "      <td>-0.872732</td>\n",
       "      <td>-0.210095</td>\n",
       "      <td>-0.990545</td>\n",
       "      <td>0.990545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342439</th>\n",
       "      <td>-0.495262</td>\n",
       "      <td>0.045004</td>\n",
       "      <td>-1.389959</td>\n",
       "      <td>-0.920682</td>\n",
       "      <td>0.794614</td>\n",
       "      <td>0.222612</td>\n",
       "      <td>1.084154</td>\n",
       "      <td>-1.084154</td>\n",
       "      <td>-1.05210</td>\n",
       "      <td>1.145827</td>\n",
       "      <td>-0.210095</td>\n",
       "      <td>-0.990545</td>\n",
       "      <td>0.990545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342440</th>\n",
       "      <td>0.459999</td>\n",
       "      <td>0.045004</td>\n",
       "      <td>0.121983</td>\n",
       "      <td>-0.920682</td>\n",
       "      <td>0.975564</td>\n",
       "      <td>-1.583825</td>\n",
       "      <td>1.084154</td>\n",
       "      <td>-1.084154</td>\n",
       "      <td>-1.05210</td>\n",
       "      <td>-0.872732</td>\n",
       "      <td>4.759750</td>\n",
       "      <td>-0.990545</td>\n",
       "      <td>0.990545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342441</th>\n",
       "      <td>0.655184</td>\n",
       "      <td>0.045004</td>\n",
       "      <td>0.121983</td>\n",
       "      <td>-0.920682</td>\n",
       "      <td>0.461836</td>\n",
       "      <td>-1.583825</td>\n",
       "      <td>1.084154</td>\n",
       "      <td>-1.084154</td>\n",
       "      <td>0.95048</td>\n",
       "      <td>-0.872732</td>\n",
       "      <td>-0.210095</td>\n",
       "      <td>-0.990545</td>\n",
       "      <td>0.990545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342442</th>\n",
       "      <td>-0.950196</td>\n",
       "      <td>0.045004</td>\n",
       "      <td>1.035388</td>\n",
       "      <td>-0.920682</td>\n",
       "      <td>-1.620694</td>\n",
       "      <td>0.750908</td>\n",
       "      <td>1.084154</td>\n",
       "      <td>-1.084154</td>\n",
       "      <td>-1.05210</td>\n",
       "      <td>1.145827</td>\n",
       "      <td>-0.210095</td>\n",
       "      <td>-0.990545</td>\n",
       "      <td>0.990545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342443</th>\n",
       "      <td>0.121688</td>\n",
       "      <td>0.045004</td>\n",
       "      <td>0.121983</td>\n",
       "      <td>-0.920682</td>\n",
       "      <td>0.859545</td>\n",
       "      <td>0.190917</td>\n",
       "      <td>-0.922378</td>\n",
       "      <td>0.922378</td>\n",
       "      <td>-1.05210</td>\n",
       "      <td>-0.872732</td>\n",
       "      <td>4.759750</td>\n",
       "      <td>-0.990545</td>\n",
       "      <td>0.990545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342444 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age  Driving_License  ...  Vehicle_Damage_No  Vehicle_Damage_Yes\n",
       "0       0.397472         0.045004  ...          -0.990545            0.990545\n",
       "1       0.526328         0.045004  ...          -0.990545            0.990545\n",
       "2      -1.019942         0.045004  ...           1.009545           -1.009545\n",
       "3       0.333044         0.045004  ...           1.009545           -1.009545\n",
       "4       1.235035         0.045004  ...          -0.990545            0.990545\n",
       "...          ...              ...  ...                ...                 ...\n",
       "342439 -0.495262         0.045004  ...          -0.990545            0.990545\n",
       "342440  0.459999         0.045004  ...          -0.990545            0.990545\n",
       "342441  0.655184         0.045004  ...          -0.990545            0.990545\n",
       "342442 -0.950196         0.045004  ...          -0.990545            0.990545\n",
       "342443  0.121688         0.045004  ...          -0.990545            0.990545\n",
       "\n",
       "[342444 rows x 13 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_res = pd.DataFrame(X_train_res, columns=X_train.columns)\n",
    "y_train_res = pd.DataFrame(y_train_res)\n",
    "X_train_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D_kBg53kKx-q",
    "outputId": "84d461bb-0bc2-488d-a124-4f32f1646c6d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((342444, 13), (342444, 1))"
      ]
     },
     "execution_count": 100,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_res.shape, y_train_res.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PTPhgPRyBhDZ"
   },
   "source": [
    "## RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B7CaIubclRDH"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf =  RandomForestClassifier(n_estimators=200, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZVNcmwI_nTMG",
    "outputId": "2cfb2cdc-43b5-4106-f851-4e2e0d89b6b6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
       "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 102,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "V-sy-xgTnarF"
   },
   "outputs": [],
   "source": [
    "predictions_rf = rf.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sIp81Ixinhdc",
    "outputId": "db826c1b-5ed9-4d1f-ff85-28eb2b1015e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8775132014825019"
      ]
     },
     "execution_count": 104,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, classification_report\n",
    "accuracy_score(y_val, predictions_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IXvK19Dqnkhr",
    "outputId": "79e2a312-56c3-4e0f-913b-b922903a248c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4696366462283356"
      ]
     },
     "execution_count": 105,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_val, predictions_rf, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vfkZlb5Ep0o5",
    "outputId": "2ce0c7c9-cdb1-44e6-bb75-a9af5bfbeddc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.21518987341772153\n",
      "0.0022898706896551722\n"
     ]
    }
   ],
   "source": [
    "Precision_score = precision_score(y_val, predictions_rf)\n",
    "Recall_score = recall_score(y_val, predictions_rf)\n",
    "print(Precision_score)\n",
    "print(Recall_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2RMHHVnip0o8",
    "outputId": "2a3c32ad-8a84-4d74-9c90-553e24904b1f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      1.00      0.93     53554\n",
      "           1       0.22      0.00      0.00      7424\n",
      "\n",
      "    accuracy                           0.88     60978\n",
      "   macro avg       0.55      0.50      0.47     60978\n",
      "weighted avg       0.80      0.88      0.82     60978\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, predictions_rf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FW-JcVD-BeGP"
   },
   "source": [
    "## XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OSQf_UR88Rfj",
    "outputId": "c119bf60-1eec-48ba-e920-003ec4ac8579"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "              learning_rate=0.01, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=3, missing=None, n_estimators=200, n_jobs=1,\n",
       "              nthread=None, objective='binary:logistic', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "              silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "xgb_model = XGBClassifier(random_state = 0, max_depth=2, n_estimators=100, learning_rate = 0.05, min_child_weight= 3)\n",
    "xgb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 346
    },
    "id": "kVbemxRMLQEU",
    "outputId": "c0f7afbb-a4c5-4401-b6ea-4389dc038824"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Importances :  [3.0998111e-02 0.0000000e+00 1.4640191e-02 6.7949325e-02 4.5139360e-04\n",
      " 3.3374557e-03 1.1764085e-01 6.8457663e-02 3.7452143e-02 4.3057207e-02\n",
      " 2.6354264e-02 5.8460575e-01 5.0556678e-03]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_importance\n",
    "\n",
    "print(\"Feature Importances : \", xgb_model.feature_importances_)\n",
    "\n",
    "# plot feature importance\n",
    "plot_importance(xgb_model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o-e3WyzcNItj"
   },
   "outputs": [],
   "source": [
    "xgb_pred = xgb_model.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q2LMEpuYNffO",
    "outputId": "6651cd5a-3bc1-45d1-9b7f-566c1e74792c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7127324608875332"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_val, xgb_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uvDYpfXUNffX",
    "outputId": "e4116a93-7d0b-44c6-c417-a8ba1c2c0410"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6169933634676728"
      ]
     },
     "execution_count": 43,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_val, xgb_pred, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OtG02hdKqLKE",
    "outputId": "b08fa149-56bf-431f-d498-1b97b409a48d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3040637909501393\n",
      "0.4263200431034483\n"
     ]
    }
   ],
   "source": [
    "Precision_score = precision_score(y_val, predictions_rf)\n",
    "Recall_score = recall_score(y_val, predictions_rf)\n",
    "print(Precision_score)\n",
    "print(Recall_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gy4aToRuqLKF",
    "outputId": "b922fc1f-25e3-42f2-fc34-401c2c7e10ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.86      0.89     53554\n",
      "           1       0.30      0.43      0.35      7424\n",
      "\n",
      "    accuracy                           0.81     60978\n",
      "   macro avg       0.61      0.65      0.62     60978\n",
      "weighted avg       0.84      0.81      0.82     60978\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, predictions_rf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DR-ewbdGO9c7"
   },
   "source": [
    "## GridSearchCV RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cLIHLSyirkPA"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {\n",
    "    'bootstrap': [True],\n",
    "    'max_depth': [5, 10, 20],\n",
    "    'n_estimators': [100, 200, 300]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6UNdfcjMrk9M"
   },
   "outputs": [],
   "source": [
    "rf_1 = RandomForestClassifier(random_state=1)\n",
    "# Instantiate the grid search model\n",
    "grid_search_1 = GridSearchCV(estimator = rf_1, param_grid = param_grid, \n",
    "                          cv = 3, n_jobs = -1, verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yOQXO6vltNbJ",
    "outputId": "ccc717c0-0a00-4c5c-c540-1767b1193ba1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "/usr/local/lib/python3.6/dist-packages/joblib/externals/loky/process_executor.py:691: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed: 11.6min finished\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:739: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  self.best_estimator_.fit(X, y, **fit_params)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=None,\n",
       "                                              oob_score=False, random_state=1,\n",
       "                                              verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'bootstrap': [True], 'max_depth': [5, 10, 20],\n",
       "                         'n_estimators': [100, 200, 300]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=2)"
      ]
     },
     "execution_count": 71,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_1.fit(X_train_res, y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "07F9WpSrtN6x",
    "outputId": "b9052c0c-e4e2-45ec-82b0-c520676287fe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True, 'max_depth': 20, 'n_estimators': 200}"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_1.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ah4wV_cKuc8i"
   },
   "outputs": [],
   "source": [
    "best_grid_1 = grid_search_1.best_estimator_\n",
    "predictions_gs1 = best_grid_1.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7t-PDCv-y8aw",
    "outputId": "81421cba-2ade-43ef-e295-e21c56726613"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6449268193184732"
      ]
     },
     "execution_count": 74,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_val, predictions_gs1, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6ygjbEVOCL1T",
    "outputId": "045103b0-4ca4-454a-fcee-bc5ec1574b5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6666666666666666\n",
      "0.0008081896551724138\n"
     ]
    }
   ],
   "source": [
    "Precision_score = precision_score(y_val, predictions_gs1)\n",
    "Recall_score = recall_score(y_val, predictions_gs1)\n",
    "print(Precision_score)\n",
    "print(Recall_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zOc07MI6O75c",
    "outputId": "09145db2-5b26-4424-9010-354350a37f99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      1.00      0.94     53554\n",
      "           1       0.67      0.00      0.00      7424\n",
      "\n",
      "    accuracy                           0.88     60978\n",
      "   macro avg       0.77      0.50      0.47     60978\n",
      "weighted avg       0.85      0.88      0.82     60978\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, predictions_gs1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wmnulvuvPXBG"
   },
   "source": [
    "## GridSearchCV XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6djxK4vZPMp6"
   },
   "outputs": [],
   "source": [
    "param_grid_2 = {\n",
    "    'max_depth': [5, 10, 20],\n",
    "    'learning_rate': [0.1, 0.01],\n",
    "    'n_estimators': [100, 200, 300]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JCvhcz3WPMp7"
   },
   "outputs": [],
   "source": [
    "xgb_model = XGBClassifier()\n",
    "# Instantiate the grid search model\n",
    "grid_search_xgb = GridSearchCV(estimator = xgb_model, param_grid = param_grid_2, \n",
    "                          cv = 3, n_jobs = -1, verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nyS4oTG8PMp8",
    "outputId": "36b4a26f-0828-4ccd-ee3a-6cd96c1e183c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 18 candidates, totalling 54 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed: 24.1min\n",
      "[Parallel(n_jobs=-1)]: Done  54 out of  54 | elapsed: 43.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
       "                                     colsample_bylevel=1, colsample_bynode=1,\n",
       "                                     colsample_bytree=1, gamma=0,\n",
       "                                     learning_rate=0.1, max_delta_step=0,\n",
       "                                     max_depth=3, min_child_weight=1,\n",
       "                                     missing=None, n_estimators=100, n_jobs=1,\n",
       "                                     nthread=None, objective='binary:logistic',\n",
       "                                     random_state=0, reg_alpha=0, reg_lambda=1,\n",
       "                                     scale_pos_weight=1, seed=None, silent=None,\n",
       "                                     subsample=1, verbosity=1),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'learning_rate': [0.1, 0.01], 'max_depth': [5, 10, 20],\n",
       "                         'n_estimators': [100, 200, 300]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=2)"
      ]
     },
     "execution_count": 84,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0IDawoxwPMp8",
    "outputId": "1b099bd4-d44e-4752-dda9-1eeda45dfb4a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}"
      ]
     },
     "execution_count": 85,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_xgb.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1Gl2_d7dPMp9"
   },
   "outputs": [],
   "source": [
    "best_grid_xgb = grid_search_xgb.best_estimator_\n",
    "predictions_gs_xgb = best_grid_xgb.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sXsPQLcQPMp9",
    "outputId": "3a780478-a427-4433-8150-0a2d9ccea4e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4783624627199828"
      ]
     },
     "execution_count": 87,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_val, predictions_gs_xgb, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "blKuqxyER_H4",
    "outputId": "81909339-6f0c-406a-d91b-ec0a04ca4c69"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4486486486486487\n",
      "0.011179956896551725\n"
     ]
    }
   ],
   "source": [
    "Precision_score = precision_score(y_val, predictions_gs_xgb)\n",
    "Recall_score = recall_score(y_val, predictions_gs_xgb)\n",
    "print(Precision_score)\n",
    "print(Recall_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G_sP_O6mCkfm",
    "outputId": "1db68de9-d1b8-4131-fe15-2407c35e544a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      1.00      0.93     53554\n",
      "           1       0.45      0.01      0.02      7424\n",
      "\n",
      "    accuracy                           0.88     60978\n",
      "   macro avg       0.66      0.50      0.48     60978\n",
      "weighted avg       0.83      0.88      0.82     60978\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, predictions_gs_xgb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4lmj626B0Mmm"
   },
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oe7eZczj0OMH",
    "outputId": "6cf463dd-cef9-480c-ac47-e12d82ffe7c2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 108,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = rf.predict(test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "52F1If0_1Yzz"
   },
   "outputs": [],
   "source": [
    "preds = pd.DataFrame({'id': test_df.id, 'Response': predictions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "vHMFRk0x5Akl",
    "outputId": "e01f3b29-e473-4aef-97d9-01618df43b6e"
   },
   "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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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>291378</td>\n",
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       "      <th>1</th>\n",
       "      <td>154746</td>\n",
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       "      <th>2</th>\n",
       "      <td>218512</td>\n",
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       "      <th>3</th>\n",
       "      <td>173930</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>241471</td>\n",
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       "    </tr>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>76217</th>\n",
       "      <td>311352</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76218</th>\n",
       "      <td>35090</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76219</th>\n",
       "      <td>52767</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76220</th>\n",
       "      <td>298399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76221</th>\n",
       "      <td>364035</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76222 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  Response\n",
       "0      291378         0\n",
       "1      154746         0\n",
       "2      218512         0\n",
       "3      173930         0\n",
       "4      241471         0\n",
       "...       ...       ...\n",
       "76217  311352         0\n",
       "76218   35090         0\n",
       "76219   52767         0\n",
       "76220  298399         0\n",
       "76221  364035         0\n",
       "\n",
       "[76222 rows x 2 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
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   "collapsed_sections": [],
   "name": "Learn ML Insurance Prediction AI Challenge.ipynb",
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