Emmanuel_project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Emmanuel_project",
      "provenance": [],
      "authorship_tag": "ABX9TyOsLjs250T43cyNwMntw7fK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dmarinere/ds/blob/master/Emmanuel_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qi319_iaxAAP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns \n",
        "import matplotlib.pyplot as plt "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dkd1wGyBy7Jo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.read_csv('train_data.csv')\n",
        "test = pd.read_csv('test_data.csv')\n",
        "sample = pd.read_csv('sample_submission.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2SRgYyDzLW0",
        "colab_type": "code",
        "outputId": "faaa584a-6e7e-412d-8c38-74d41e504216",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train.shape, test.shape\n",
        "#i want to find the shape of the data"
      ],
      "execution_count": 318,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7160, 14), (3069, 13))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 318
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pUxAstq5Ea-H",
        "colab_type": "code",
        "outputId": "4c792ca6-66a1-4418-a588-49de5e798bac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 319,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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>2013</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>1960.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>H11921</td>\n",
              "      <td>2016</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>1960.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>H9805</td>\n",
              "      <td>2013</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>1960.0</td>\n",
              "      <td>.</td>\n",
              "      <td>3310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>H7493</td>\n",
              "      <td>2014</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>2004.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>H7494</td>\n",
              "      <td>2016</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>2004.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3321</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Customer Id  YearOfObservation  ...  NumberOfWindows  Geo_Code\n",
              "0      H11920               2013  ...                3      3310\n",
              "1      H11921               2016  ...                3      3310\n",
              "2       H9805               2013  ...                .      3310\n",
              "3       H7493               2014  ...                3      3321\n",
              "4       H7494               2016  ...                3      3321\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 319
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jsfes8G6EbmC",
        "colab_type": "code",
        "outputId": "2cbed07f-17e6-41b5-f6d1-5c428d657093",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 320,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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",
              "      <th>Claim</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>H14663</td>\n",
              "      <td>2013</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>V</td>\n",
              "      <td>V</td>\n",
              "      <td>U</td>\n",
              "      <td>290.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1960.0</td>\n",
              "      <td>.</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>H2037</td>\n",
              "      <td>2015</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>V</td>\n",
              "      <td>N</td>\n",
              "      <td>O</td>\n",
              "      <td>R</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1850.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>H3802</td>\n",
              "      <td>2014</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>V</td>\n",
              "      <td>V</td>\n",
              "      <td>U</td>\n",
              "      <td>595.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1960.0</td>\n",
              "      <td>.</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>H3834</td>\n",
              "      <td>2013</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>V</td>\n",
              "      <td>V</td>\n",
              "      <td>V</td>\n",
              "      <td>U</td>\n",
              "      <td>2840.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1960.0</td>\n",
              "      <td>.</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>H5053</td>\n",
              "      <td>2014</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>V</td>\n",
              "      <td>N</td>\n",
              "      <td>O</td>\n",
              "      <td>R</td>\n",
              "      <td>680.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1800.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Customer Id  YearOfObservation  ...  Geo_Code  Claim\n",
              "0      H14663               2013  ...      1053      0\n",
              "1       H2037               2015  ...      1053      0\n",
              "2       H3802               2014  ...      1053      0\n",
              "3       H3834               2013  ...      1053      0\n",
              "4       H5053               2014  ...      1053      0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 320
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AbA7lQso5Olx",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMJLC6NBzaQ8",
        "colab_type": "code",
        "outputId": "74406733-1af1-44f5-9154-fd95aed9e752",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "source": [
        "train.describe(include='all')"
      ],
      "execution_count": 321,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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",
              "      <th>Claim</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7160</td>\n",
              "      <td>7160.000000</td>\n",
              "      <td>7160.000000</td>\n",
              "      <td>7160.000000</td>\n",
              "      <td>7160</td>\n",
              "      <td>7160</td>\n",
              "      <td>7153</td>\n",
              "      <td>7160</td>\n",
              "      <td>7054.000000</td>\n",
              "      <td>7160.000000</td>\n",
              "      <td>6652.000000</td>\n",
              "      <td>7160</td>\n",
              "      <td>7058</td>\n",
              "      <td>7160.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>7160</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>1307</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>H19120</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>V</td>\n",
              "      <td>N</td>\n",
              "      <td>O</td>\n",
              "      <td>R</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>.</td>\n",
              "      <td>6088</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5382</td>\n",
              "      <td>3608</td>\n",
              "      <td>3602</td>\n",
              "      <td>3610</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3551</td>\n",
              "      <td>143</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2013.669553</td>\n",
              "      <td>0.909758</td>\n",
              "      <td>0.305447</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1883.727530</td>\n",
              "      <td>2.186034</td>\n",
              "      <td>1964.456404</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.228212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.383769</td>\n",
              "      <td>0.239756</td>\n",
              "      <td>0.460629</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2278.157745</td>\n",
              "      <td>0.940632</td>\n",
              "      <td>36.002014</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.419709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2012.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1545.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2012.000000</td>\n",
              "      <td>0.997268</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>528.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1960.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2013.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1083.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1970.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2289.750000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1980.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20940.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Customer Id  YearOfObservation  ...  Geo_Code        Claim\n",
              "count         7160        7160.000000  ...      7058  7160.000000\n",
              "unique        7160                NaN  ...      1307          NaN\n",
              "top         H19120                NaN  ...      6088          NaN\n",
              "freq             1                NaN  ...       143          NaN\n",
              "mean           NaN        2013.669553  ...       NaN     0.228212\n",
              "std            NaN           1.383769  ...       NaN     0.419709\n",
              "min            NaN        2012.000000  ...       NaN     0.000000\n",
              "25%            NaN        2012.000000  ...       NaN     0.000000\n",
              "50%            NaN        2013.000000  ...       NaN     0.000000\n",
              "75%            NaN        2015.000000  ...       NaN     0.000000\n",
              "max            NaN        2016.000000  ...       NaN     1.000000\n",
              "\n",
              "[11 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 321
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIaRJNTxzw9I",
        "colab_type": "code",
        "outputId": "9677ff10-c0aa-40aa-cdd5-0e3ca218136f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "test.describe()"
      ],
      "execution_count": 322,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>YearOfObservation</th>\n",
              "      <th>Insured_Period</th>\n",
              "      <th>Residential</th>\n",
              "      <th>Building Dimension</th>\n",
              "      <th>Building_Type</th>\n",
              "      <th>Date_of_Occupancy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3069.000000</td>\n",
              "      <td>3069.000000</td>\n",
              "      <td>3069.000000</td>\n",
              "      <td>3056.000000</td>\n",
              "      <td>3069.000000</td>\n",
              "      <td>2341.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2013.593679</td>\n",
              "      <td>0.922806</td>\n",
              "      <td>0.224177</td>\n",
              "      <td>1666.790576</td>\n",
              "      <td>2.359400</td>\n",
              "      <td>1966.781717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.372138</td>\n",
              "      <td>0.219066</td>\n",
              "      <td>0.417107</td>\n",
              "      <td>2251.180599</td>\n",
              "      <td>0.998464</td>\n",
              "      <td>25.238702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2012.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1750.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2012.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>470.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1960.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2013.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>900.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1968.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2015.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1957.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1980.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>30745.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2012.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       YearOfObservation  Insured_Period  ...  Building_Type  Date_of_Occupancy\n",
              "count        3069.000000     3069.000000  ...    3069.000000        2341.000000\n",
              "mean         2013.593679        0.922806  ...       2.359400        1966.781717\n",
              "std             1.372138        0.219066  ...       0.998464          25.238702\n",
              "min          2012.000000        0.000000  ...       1.000000        1750.000000\n",
              "25%          2012.000000        1.000000  ...       2.000000        1960.000000\n",
              "50%          2013.000000        1.000000  ...       2.000000        1968.000000\n",
              "75%          2015.000000        1.000000  ...       3.000000        1980.000000\n",
              "max          2016.000000        1.000000  ...       4.000000        2012.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 322
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0O43K5qLzzQe",
        "colab_type": "code",
        "outputId": "2aec3009-bdd4-43d3-8606-4a6b927a8c14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "train.dtypes"
      ],
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Customer Id            object\n",
              "YearOfObservation       int64\n",
              "Insured_Period        float64\n",
              "Residential             int64\n",
              "Building_Painted       object\n",
              "Building_Fenced        object\n",
              "Garden                 object\n",
              "Settlement             object\n",
              "Building Dimension    float64\n",
              "Building_Type           int64\n",
              "Date_of_Occupancy     float64\n",
              "NumberOfWindows        object\n",
              "Geo_Code               object\n",
              "Claim                   int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 323
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xoOFclon1Qea",
        "colab_type": "code",
        "outputId": "eec2019e-03db-48e5-cc4a-dc7e28c330c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "test.dtypes"
      ],
      "execution_count": 324,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Customer Id            object\n",
              "YearOfObservation       int64\n",
              "Insured_Period        float64\n",
              "Residential             int64\n",
              "Building_Painted       object\n",
              "Building_Fenced        object\n",
              "Garden                 object\n",
              "Settlement             object\n",
              "Building Dimension    float64\n",
              "Building_Type           int64\n",
              "Date_of_Occupancy     float64\n",
              "NumberOfWindows        object\n",
              "Geo_Code               object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 324
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8U_7tek1b1a",
        "colab_type": "code",
        "outputId": "c998487d-51f5-42f4-b6f2-636599889dc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7160 entries, 0 to 7159\n",
            "Data columns (total 14 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Customer Id         7160 non-null   object \n",
            " 1   YearOfObservation   7160 non-null   int64  \n",
            " 2   Insured_Period      7160 non-null   float64\n",
            " 3   Residential         7160 non-null   int64  \n",
            " 4   Building_Painted    7160 non-null   object \n",
            " 5   Building_Fenced     7160 non-null   object \n",
            " 6   Garden              7153 non-null   object \n",
            " 7   Settlement          7160 non-null   object \n",
            " 8   Building Dimension  7054 non-null   float64\n",
            " 9   Building_Type       7160 non-null   int64  \n",
            " 10  Date_of_Occupancy   6652 non-null   float64\n",
            " 11  NumberOfWindows     7160 non-null   object \n",
            " 12  Geo_Code            7058 non-null   object \n",
            " 13  Claim               7160 non-null   int64  \n",
            "dtypes: float64(3), int64(4), object(7)\n",
            "memory usage: 783.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqLJYTF32mbG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train['Building_Fenced'] = train['Building_Fenced'].replace({'N':1, 'V':0})\n",
        "train['Building_Painted'] = train['Building_Painted'].replace({'N':1, 'V':0})\n",
        "train['Garden'] = train['Garden'].replace({'V':1, 'O':0})\n",
        "train['Settlement'] = train['Settlement'].replace({'U':1, 'R':0})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwiOI9BI37a1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test['Building_Fenced'] = test['Building_Fenced'].replace({'N':1, 'V':0})\n",
        "test['Building_Painted'] = test['Building_Painted'].replace({'N':1, 'V':0})\n",
        "test['Garden'] = test['Garden'].replace({'V':1, 'O':0})\n",
        "test['Settlement'] = test['Settlement'].replace({'U':1, 'R':0})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0w-uyGYmEzOC",
        "colab_type": "code",
        "outputId": "88193949-8f56-499b-a2e8-175b02fd2cd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "train['NumberOfWindows'].value_counts()"
      ],
      "execution_count": 328,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 328
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EsCzBm9Fj4Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "window_train = pd.get_dummies(train['NumberOfWindows'])\n",
        "window_test = pd.get_dummies(test['NumberOfWindows'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HDPCmKRjFu_c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.concat([train, window_train], axis=1)\n",
        "test = pd.concat([test, window_test], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXdcJA64GUVJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "train = train.drop('NumberOfWindows', axis=1)\n",
        "test = test.drop('NumberOfWindows', axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rpbg1ptb4Jeu",
        "colab_type": "code",
        "outputId": "eb005e09-4a6e-41cc-890f-2284e762dad1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "test.isnull().sum()"
      ],
      "execution_count": 332,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Customer Id             0\n",
              "YearOfObservation       0\n",
              "Insured_Period          0\n",
              "Residential             0\n",
              "Building_Painted        0\n",
              "Building_Fenced         0\n",
              "Garden                  4\n",
              "Settlement              0\n",
              "Building Dimension     13\n",
              "Building_Type           0\n",
              "Date_of_Occupancy     728\n",
              "Geo_Code               13\n",
              "   .                    0\n",
              "1                       0\n",
              "2                       0\n",
              "3                       0\n",
              "4                       0\n",
              "5                       0\n",
              "6                       0\n",
              "7                       0\n",
              "8                       0\n",
              "9                       0\n",
              ">=10                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 332
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SMiP1ra54L1h",
        "colab_type": "code",
        "outputId": "92e11d4a-478d-482e-894a-ec16e6f1816c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "train.isnull().sum()"
      ],
      "execution_count": 333,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Customer Id             0\n",
              "YearOfObservation       0\n",
              "Insured_Period          0\n",
              "Residential             0\n",
              "Building_Painted        0\n",
              "Building_Fenced         0\n",
              "Garden                  7\n",
              "Settlement              0\n",
              "Building Dimension    106\n",
              "Building_Type           0\n",
              "Date_of_Occupancy     508\n",
              "Geo_Code              102\n",
              "Claim                   0\n",
              "   .                    0\n",
              "1                       0\n",
              "2                       0\n",
              "3                       0\n",
              "4                       0\n",
              "5                       0\n",
              "6                       0\n",
              "7                       0\n",
              "8                       0\n",
              "9                       0\n",
              ">=10                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 333
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2vPQ4vBEPe6",
        "colab_type": "code",
        "outputId": "96697e9a-7ac8-415b-dc39-cb691762d0a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 334,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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>Geo_Code</th>\n",
              "      <th>Claim</th>\n",
              "      <th>.</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>&gt;=10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>H14663</td>\n",
              "      <td>2013</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>290.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1960.0</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>1</th>\n",
              "      <td>H2037</td>\n",
              "      <td>2015</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1850.0</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>2</th>\n",
              "      <td>H3802</td>\n",
              "      <td>2014</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>595.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1960.0</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>3</th>\n",
              "      <td>H3834</td>\n",
              "      <td>2013</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2840.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1960.0</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>4</th>\n",
              "      <td>H5053</td>\n",
              "      <td>2014</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>680.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1800.0</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "</div>"
            ],
            "text/plain": [
              "  Customer Id  YearOfObservation  Insured_Period  Residential  ...  7  8  9  >=10\n",
              "0      H14663               2013             1.0            0  ...  0  0  0     0\n",
              "1       H2037               2015             1.0            0  ...  0  0  0     0\n",
              "2       H3802               2014             1.0            0  ...  0  0  0     0\n",
              "3       H3834               2013             1.0            0  ...  0  0  0     0\n",
              "4       H5053               2014             1.0            0  ...  0  0  0     0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 334
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vrPA4w8r6zJY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train['Date_of_Occupancy']= train['Date_of_Occupancy'].fillna(value=1980)\n",
        "test['Date_of_Occupancy']= test['Date_of_Occupancy'].fillna(value=1980)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RadupRS085zk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train['Garden']= train['Garden'].fillna(value=0)\n",
        "test['Garden']= test['Garden'].fillna(value=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhXWHAa285nk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train['Building Dimension'] = train['Building Dimension'].fillna(value=1083)\n",
        "test['Building Dimension']= test['Building Dimension'].fillna(value=900)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECll3ZV085cN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train=train.drop(['Geo_Code'], axis=1)\n",
        "test=test.drop(['Geo_Code'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "msN18uliDmLs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#modelling our data\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import linear_model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import  confusion_matrix, accuracy_score, f1_score\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_8l9z_084-x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_id = test[\"Customer Id\"]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vzc85Ucd8nt1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = train.drop(\"Customer Id\", axis=1)\n",
        "test  = test.drop(\"Customer Id\", axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4vn6mr8XwVe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "a1469ea2-ce72-43b8-cb75-bc7240fa0d96"
      },
      "source": [
        "X_test.info()"
      ],
      "execution_count": 342,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2148 entries, 1894 to 4559\n",
            "Data columns (total 22 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   YearOfObservation   2148 non-null   int64  \n",
            " 1   Insured_Period      2148 non-null   float64\n",
            " 2   Residential         2148 non-null   int64  \n",
            " 3   Building_Painted    2148 non-null   int64  \n",
            " 4   Building_Fenced     2148 non-null   int64  \n",
            " 5   Garden              2148 non-null   float64\n",
            " 6   Settlement          2148 non-null   int64  \n",
            " 7   Building Dimension  2148 non-null   float64\n",
            " 8   Building_Type       2148 non-null   int64  \n",
            " 9   Date_of_Occupancy   2148 non-null   float64\n",
            " 10  Geo_Code            2148 non-null   float64\n",
            " 11     .                2148 non-null   uint8  \n",
            " 12  1                   2148 non-null   uint8  \n",
            " 13  2                   2148 non-null   uint8  \n",
            " 14  3                   2148 non-null   uint8  \n",
            " 15  4                   2148 non-null   uint8  \n",
            " 16  5                   2148 non-null   uint8  \n",
            " 17  6                   2148 non-null   uint8  \n",
            " 18  7                   2148 non-null   uint8  \n",
            " 19  8                   2148 non-null   uint8  \n",
            " 20  9                   2148 non-null   uint8  \n",
            " 21  >=10                2148 non-null   uint8  \n",
            "dtypes: float64(5), int64(6), uint8(11)\n",
            "memory usage: 224.4 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QmY-zBViTne1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = train.drop('Claim', axis=1)\n",
        "y = train.Claim"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxMtzkqxWIfs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lmp3ZsS2XqaI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6afd59e0-4e2f-4889-828c-01164993777d"
      },
      "source": [
        "# Random Forest\n",
        "random_forest = RandomForestClassifier(n_estimators=100)\n",
        "random_forest.fit(X_test, y_test)\n",
        "\n",
        "Y_pred = random_forest.predict(X_test)\n",
        "\n",
        "random_forest.score(X_test, y_test)\n",
        "acc_random_forest = round(random_forest.score(X_test, y_test) * 100, 2)\n",
        "print(round(acc_random_forest,2), \"%\")"
      ],
      "execution_count": 345,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "99.58 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HLVI4jUYm2CC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_pred2 = random_forest.predict(test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eNwodsRzvmgJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "0eaf0be1-717e-4c31-833a-04c89a9369f4"
      },
      "source": [
        "sample.head()"
      ],
      "execution_count": 347,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>H10000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>H10001</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>H10002</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>H10003</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 347
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gsv_XXCzxD7F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "43ae1d6a-cfdb-42ff-8c45-e51944c84988"
      },
      "source": [
        "sample"
      ],
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>H10000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>H10001</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>H10002</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>H10003</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\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",
              "<p>3068 rows × 2 columns</p>\n",
              "</div>"
            ],
            "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\n",
              "...          ...    ...\n",
              "3063       H9987      1\n",
              "3064       H9988      1\n",
              "3065       H9994      1\n",
              "3066       H9996      1\n",
              "3067       H9998      1\n",
              "\n",
              "[3068 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 348
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5iFCGB-qu4F1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample = pd.DataFrame()\n",
        "sample['Customer Id'] = test_id\n",
        "sample['Claim'] = Y_pred2\n",
        "sample.to_csv('answer5.csv',index=False )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "weNosHtTm9iJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2403c59c-26c6-4776-8dd0-dd2e23c51891"
      },
      "source": [
        "knn = KNeighborsClassifier(n_neighbors = 3)\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "Y_pred3 = knn.predict(test)\n",
        "\n",
        "acc_knn = round(knn.score(X_train, y_train) * 100, 2)\n",
        "print(round(acc_knn,2,), \"%\")"
      ],
      "execution_count": 350,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "84.84 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQdE1Yc62kAN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample = pd.DataFrame()\n",
        "sample['Customer Id'] = test_id\n",
        "sample['Claim'] = Y_pred3\n",
        "sample.to_csv('answer3.csv',index=False )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KOUPSFyZ7Gz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "54b73a3a-9d1c-4dd2-c10b-5f21538eec2c"
      },
      "source": [
        "# Logistic Regression\n",
        "logreg = LogisticRegression()\n",
        "logreg.fit(X_test, y_test)\n",
        "\n",
        "Y_pred = logreg.predict(test)\n",
        "\n",
        "acc_log = round(logreg.score(X_test, y_test) * 100, 2)\n",
        "print(round(acc_log,2,), \"%\")"
      ],
      "execution_count": 352,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "78.72 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBqET6vdqpZd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HnNXwmn-ZG8p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from xgboost import XGBClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zndOxgNlLSJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=XGBClassifier()\n",
        "model.fit(X_train,y_train)\n",
        "Y_xgb = model.predict(test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xxXp0eP_pBkR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample = pd.DataFrame()\n",
        "sample['Customer Id'] = test_id\n",
        "sample['Claim'] = Y_xgb\n",
        "sample.to_csv('answer6.csv',index=False )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4U_ynG8dKfz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "2f293d9b-683d-49c3-85f6-1bf72f61b1a3"
      },
      "source": [
        "print(\"Scores:\", scores)\n",
        "print(\"Mean:\", scores.mean())\n",
        "print(\"Standard Deviation:\", scores.std())"
      ],
      "execution_count": 356,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [0.72908367 0.74900398 0.74850299 0.75449102 0.73453094 0.73652695\n",
            " 0.76447106 0.750499   0.78043912 0.74451098]\n",
            "Mean: 0.7492059705290615\n",
            "Standard Deviation: 0.014288931435978106\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zRCryVfvlqUO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}