ML__hw4.ipynb

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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/podo47/Machine-Learning-Assignment-II/blob/main/ML__hw4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import libraries"
      ],
      "metadata": {
        "id": "pomk4OAk17Dt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt # for data visualization purposes\n",
        "import seaborn as sns # for statistical data visualization\n",
        "from collections import Counter\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "IR8sAKvS2J4u"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "sWjrgNAkKn5k"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import data"
      ],
      "metadata": {
        "id": "nswSWBL45jVQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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          "height": 299
        },
        "outputId": "5ede12eb-73ad-46f0-acae-94cfbf5b2e4e"
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      "outputs": [
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          "data": {
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              "  policy_id  policy_tenure  age_of_car  age_of_policyholder area_cluster  \\\n",
              "0   ID00001       0.515874        0.05             0.644231           C1   \n",
              "1   ID00002       0.672619        0.02             0.375000           C2   \n",
              "2   ID00003       0.841110        0.02             0.384615           C3   \n",
              "3   ID00004       0.900277        0.11             0.432692           C4   \n",
              "4   ID00005       0.596403        0.11             0.634615           C5   \n",
              "\n",
              "   population_density  make segment model fuel_type  ... is_brake_assist  \\\n",
              "0                4990     1       A    M1       CNG  ...              No   \n",
              "1               27003     1       A    M1       CNG  ...              No   \n",
              "2                4076     1       A    M1       CNG  ...              No   \n",
              "3               21622     1      C1    M2    Petrol  ...             Yes   \n",
              "4               34738     2       A    M3    Petrol  ...              No   \n",
              "\n",
              "  is_power_door_locks is_central_locking  is_power_steering  \\\n",
              "0                  No                 No                Yes   \n",
              "1                  No                 No                Yes   \n",
              "2                  No                 No                Yes   \n",
              "3                 Yes                Yes                Yes   \n",
              "4                 Yes                Yes                Yes   \n",
              "\n",
              "  is_driver_seat_height_adjustable is_day_night_rear_view_mirror is_ecw  \\\n",
              "0                               No                            No     No   \n",
              "1                               No                            No     No   \n",
              "2                               No                            No     No   \n",
              "3                              Yes                           Yes    Yes   \n",
              "4                               No                           Yes    Yes   \n",
              "\n",
              "  is_speed_alert ncap_rating is_claim  \n",
              "0            Yes           0        0  \n",
              "1            Yes           0        0  \n",
              "2            Yes           0        0  \n",
              "3            Yes           2        0  \n",
              "4            Yes           2        0  \n",
              "\n",
              "[5 rows x 44 columns]"
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              "      <th></th>\n",
              "      <th>policy_id</th>\n",
              "      <th>policy_tenure</th>\n",
              "      <th>age_of_car</th>\n",
              "      <th>age_of_policyholder</th>\n",
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              "      <th>is_power_steering</th>\n",
              "      <th>is_driver_seat_height_adjustable</th>\n",
              "      <th>is_day_night_rear_view_mirror</th>\n",
              "      <th>is_ecw</th>\n",
              "      <th>is_speed_alert</th>\n",
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              "      <td>C1</td>\n",
              "      <td>4990</td>\n",
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              "      <td>0.672619</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>C2</td>\n",
              "      <td>27003</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>M1</td>\n",
              "      <td>CNG</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
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              "      <th>2</th>\n",
              "      <td>ID00003</td>\n",
              "      <td>0.841110</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.384615</td>\n",
              "      <td>C3</td>\n",
              "      <td>4076</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>M1</td>\n",
              "      <td>CNG</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID00004</td>\n",
              "      <td>0.900277</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.432692</td>\n",
              "      <td>C4</td>\n",
              "      <td>21622</td>\n",
              "      <td>1</td>\n",
              "      <td>C1</td>\n",
              "      <td>M2</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>ID00005</td>\n",
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              "      <td>0.11</td>\n",
              "      <td>0.634615</td>\n",
              "      <td>C5</td>\n",
              "      <td>34738</td>\n",
              "      <td>2</td>\n",
              "      <td>A</td>\n",
              "      <td>M3</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
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              "<p>5 rows × 44 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "data=\"/content/drive/MyDrive/ML_hw4/train.csv\"\n",
        "train_df = pd.read_csv(data)\n",
        "train_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory data analysis"
      ],
      "metadata": {
        "id": "A5kJNeOX_QSx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Printing the shape of the train dataset\n",
        "print(train_df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5o5HNmgN_XEd",
        "outputId": "32af0c20-bbac-4a99-81c9-94b4c5962828"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(58592, 44)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View summary of the dataset\n",
        "train_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1VlLuZ7l23HZ",
        "outputId": "3b0e9921-a8f4-4aab-8d22-825631d512cb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 58592 entries, 0 to 58591\n",
            "Data columns (total 44 columns):\n",
            " #   Column                            Non-Null Count  Dtype  \n",
            "---  ------                            --------------  -----  \n",
            " 0   policy_id                         58592 non-null  object \n",
            " 1   policy_tenure                     58592 non-null  float64\n",
            " 2   age_of_car                        58592 non-null  float64\n",
            " 3   age_of_policyholder               58592 non-null  float64\n",
            " 4   area_cluster                      58592 non-null  object \n",
            " 5   population_density                58592 non-null  int64  \n",
            " 6   make                              58592 non-null  int64  \n",
            " 7   segment                           58592 non-null  object \n",
            " 8   model                             58592 non-null  object \n",
            " 9   fuel_type                         58592 non-null  object \n",
            " 10  max_torque                        58592 non-null  object \n",
            " 11  max_power                         58592 non-null  object \n",
            " 12  engine_type                       58592 non-null  object \n",
            " 13  airbags                           58592 non-null  int64  \n",
            " 14  is_esc                            58592 non-null  object \n",
            " 15  is_adjustable_steering            58592 non-null  object \n",
            " 16  is_tpms                           58592 non-null  object \n",
            " 17  is_parking_sensors                58592 non-null  object \n",
            " 18  is_parking_camera                 58592 non-null  object \n",
            " 19  rear_brakes_type                  58592 non-null  object \n",
            " 20  displacement                      58592 non-null  int64  \n",
            " 21  cylinder                          58592 non-null  int64  \n",
            " 22  transmission_type                 58592 non-null  object \n",
            " 23  gear_box                          58592 non-null  int64  \n",
            " 24  steering_type                     58592 non-null  object \n",
            " 25  turning_radius                    58592 non-null  float64\n",
            " 26  length                            58592 non-null  int64  \n",
            " 27  width                             58592 non-null  int64  \n",
            " 28  height                            58592 non-null  int64  \n",
            " 29  gross_weight                      58592 non-null  int64  \n",
            " 30  is_front_fog_lights               58592 non-null  object \n",
            " 31  is_rear_window_wiper              58592 non-null  object \n",
            " 32  is_rear_window_washer             58592 non-null  object \n",
            " 33  is_rear_window_defogger           58592 non-null  object \n",
            " 34  is_brake_assist                   58592 non-null  object \n",
            " 35  is_power_door_locks               58592 non-null  object \n",
            " 36  is_central_locking                58592 non-null  object \n",
            " 37  is_power_steering                 58592 non-null  object \n",
            " 38  is_driver_seat_height_adjustable  58592 non-null  object \n",
            " 39  is_day_night_rear_view_mirror     58592 non-null  object \n",
            " 40  is_ecw                            58592 non-null  object \n",
            " 41  is_speed_alert                    58592 non-null  object \n",
            " 42  ncap_rating                       58592 non-null  int64  \n",
            " 43  is_claim                          58592 non-null  int64  \n",
            "dtypes: float64(4), int64(12), object(28)\n",
            "memory usage: 19.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values:\n",
        "train_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nhVuG-ICAjSY",
        "outputId": "3a095898-b9a6-41e4-89ad-c040cd0c271c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "policy_id                           0\n",
              "policy_tenure                       0\n",
              "age_of_car                          0\n",
              "age_of_policyholder                 0\n",
              "area_cluster                        0\n",
              "population_density                  0\n",
              "make                                0\n",
              "segment                             0\n",
              "model                               0\n",
              "fuel_type                           0\n",
              "max_torque                          0\n",
              "max_power                           0\n",
              "engine_type                         0\n",
              "airbags                             0\n",
              "is_esc                              0\n",
              "is_adjustable_steering              0\n",
              "is_tpms                             0\n",
              "is_parking_sensors                  0\n",
              "is_parking_camera                   0\n",
              "rear_brakes_type                    0\n",
              "displacement                        0\n",
              "cylinder                            0\n",
              "transmission_type                   0\n",
              "gear_box                            0\n",
              "steering_type                       0\n",
              "turning_radius                      0\n",
              "length                              0\n",
              "width                               0\n",
              "height                              0\n",
              "gross_weight                        0\n",
              "is_front_fog_lights                 0\n",
              "is_rear_window_wiper                0\n",
              "is_rear_window_washer               0\n",
              "is_rear_window_defogger             0\n",
              "is_brake_assist                     0\n",
              "is_power_door_locks                 0\n",
              "is_central_locking                  0\n",
              "is_power_steering                   0\n",
              "is_driver_seat_height_adjustable    0\n",
              "is_day_night_rear_view_mirror       0\n",
              "is_ecw                              0\n",
              "is_speed_alert                      0\n",
              "ncap_rating                         0\n",
              "is_claim                            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Explore categorical variables**"
      ],
      "metadata": {
        "id": "AED91TuJA2Yl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical = train_df.select_dtypes(include =[object])\n",
        "print(\"There are {} categorical variables\".format(categorical.shape[1]))\n",
        "print('The categorical variables are :\\n',categorical.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJzRN6__A5GC",
        "outputId": "1df7dd5d-07e2-4cbd-d2bd-9a37a157feed"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 28 categorical variables\n",
            "The categorical variables are :\n",
            " Index(['policy_id', 'area_cluster', 'segment', 'model', 'fuel_type',\n",
            "       'max_torque', 'max_power', 'engine_type', 'is_esc',\n",
            "       'is_adjustable_steering', 'is_tpms', 'is_parking_sensors',\n",
            "       'is_parking_camera', 'rear_brakes_type', 'transmission_type',\n",
            "       'steering_type', 'is_front_fog_lights', 'is_rear_window_wiper',\n",
            "       'is_rear_window_washer', 'is_rear_window_defogger', 'is_brake_assist',\n",
            "       'is_power_door_locks', 'is_central_locking', 'is_power_steering',\n",
            "       'is_driver_seat_height_adjustable', 'is_day_night_rear_view_mirror',\n",
            "       'is_ecw', 'is_speed_alert'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Explore numerical variables**"
      ],
      "metadata": {
        "id": "bkis7AJAIgTY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numerical = train_df.select_dtypes(include =[np.float64,np.int64])\n",
        "print(\"There are {} numerical variables\".format(numerical.shape[1]))\n",
        "print('The numerical variables are :\\n',numerical.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F-N43nzCDRvC",
        "outputId": "27b93154-2f12-443e-9080-00f1dfaa7ff7"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 16 numerical variables\n",
            "The numerical variables are :\n",
            " Index(['policy_tenure', 'age_of_car', 'age_of_policyholder',\n",
            "       'population_density', 'make', 'airbags', 'displacement', 'cylinder',\n",
            "       'gear_box', 'turning_radius', 'length', 'width', 'height',\n",
            "       'gross_weight', 'ncap_rating', 'is_claim'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Check if data is imbalanced**"
      ],
      "metadata": {
        "id": "izeWdJu0JnKX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_df.is_claim.value_counts())\n",
        "sns.barplot(x = [0, 1], y = [54844, 3784])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "75o3uHN_KCB1",
        "outputId": "4d9f8b7c-561f-4e0f-ae53-ed7f7eed397f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    54844\n",
            "1     3748\n",
            "Name: is_claim, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fafa0bab4f0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data preprocessing"
      ],
      "metadata": {
        "id": "v_UzkFokMrxz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Drop useless features**"
      ],
      "metadata": {
        "id": "Fp-z53CMnmGc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = train_df.copy()\n",
        "train.drop(['policy_id'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "nftKdmpSNDJi"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Label Encoding**"
      ],
      "metadata": {
        "id": "z0xQgfLBm90q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ordinal variables\n",
        "train['transmission_type'] = train['transmission_type'].map({'Manual': 0, 'Automatic': 1})\n",
        "train['steering_type'] = train['steering_type'].map({'Manual': 0, 'Power': 1, 'Electric': 2})\n",
        "\n",
        "train[['max_torque_Nm','max_torque_rpm']] = train[\"max_torque\"].apply(lambda n: pd.Series(str(n).split(\"@\")))\n",
        "train.drop([\"max_torque\"], axis=1, inplace= True)\n",
        "train['max_torque_Nm'] = train['max_torque_Nm'].str[:-2].astype(float)\n",
        "train['max_torque_rpm'] = train['max_torque_rpm'].str[:-3].astype(int)\n",
        "\n",
        "train[['max_power_bhp','max_power_rpm']] = train[\"max_power\"].apply(lambda n: pd.Series(str(n).split(\"@\")))\n",
        "train.drop([\"max_power\"], axis=1, inplace= True)\n",
        "train['max_power_rpm'] = train['max_power_rpm'].str[:-3].astype(int)\n",
        "train['max_power_bhp'] = train['max_power_bhp'].str[:-3].astype(float)"
      ],
      "metadata": {
        "id": "dLQi0suXm8zC"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**One-hot Encoding**"
      ],
      "metadata": {
        "id": "xiV9RkhnoF-F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.get_dummies(train, drop_first=True)"
      ],
      "metadata": {
        "id": "X8q73q7l7slS"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "HU4SyKQqDdBa",
        "outputId": "a19cd59f-777c-4ffd-fb73-22d925b3b35e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   policy_tenure  age_of_car  age_of_policyholder  population_density  make  \\\n",
              "0       0.515874        0.05             0.644231                4990     1   \n",
              "1       0.672619        0.02             0.375000               27003     1   \n",
              "2       0.841110        0.02             0.384615                4076     1   \n",
              "3       0.900277        0.11             0.432692               21622     1   \n",
              "4       0.596403        0.11             0.634615               34738     2   \n",
              "\n",
              "   airbags  displacement  cylinder  transmission_type  gear_box  ...  \\\n",
              "0        2           796         3                  0         5  ...   \n",
              "1        2           796         3                  0         5  ...   \n",
              "2        2           796         3                  0         5  ...   \n",
              "3        2          1197         4                  1         5  ...   \n",
              "4        2           999         3                  1         5  ...   \n",
              "\n",
              "   is_rear_window_washer_Yes  is_rear_window_defogger_Yes  \\\n",
              "0                          0                            0   \n",
              "1                          0                            0   \n",
              "2                          0                            0   \n",
              "3                          0                            1   \n",
              "4                          0                            0   \n",
              "\n",
              "   is_brake_assist_Yes  is_power_door_locks_Yes  is_central_locking_Yes  \\\n",
              "0                    0                        0                       0   \n",
              "1                    0                        0                       0   \n",
              "2                    0                        0                       0   \n",
              "3                    1                        1                       1   \n",
              "4                    0                        1                       1   \n",
              "\n",
              "   is_power_steering_Yes  is_driver_seat_height_adjustable_Yes  \\\n",
              "0                      1                                     0   \n",
              "1                      1                                     0   \n",
              "2                      1                                     0   \n",
              "3                      1                                     1   \n",
              "4                      1                                     0   \n",
              "\n",
              "   is_day_night_rear_view_mirror_Yes  is_ecw_Yes  is_speed_alert_Yes  \n",
              "0                                  0           0                   1  \n",
              "1                                  0           0                   1  \n",
              "2                                  0           0                   1  \n",
              "3                                  1           1                   1  \n",
              "4                                  1           1                   1  \n",
              "\n",
              "[5 rows x 88 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a6d6951b-b361-4605-b534-0f663d02ac44\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>policy_tenure</th>\n",
              "      <th>age_of_car</th>\n",
              "      <th>age_of_policyholder</th>\n",
              "      <th>population_density</th>\n",
              "      <th>make</th>\n",
              "      <th>airbags</th>\n",
              "      <th>displacement</th>\n",
              "      <th>cylinder</th>\n",
              "      <th>transmission_type</th>\n",
              "      <th>gear_box</th>\n",
              "      <th>...</th>\n",
              "      <th>is_rear_window_washer_Yes</th>\n",
              "      <th>is_rear_window_defogger_Yes</th>\n",
              "      <th>is_brake_assist_Yes</th>\n",
              "      <th>is_power_door_locks_Yes</th>\n",
              "      <th>is_central_locking_Yes</th>\n",
              "      <th>is_power_steering_Yes</th>\n",
              "      <th>is_driver_seat_height_adjustable_Yes</th>\n",
              "      <th>is_day_night_rear_view_mirror_Yes</th>\n",
              "      <th>is_ecw_Yes</th>\n",
              "      <th>is_speed_alert_Yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.515874</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.644231</td>\n",
              "      <td>4990</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>796</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.596403</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.634615</td>\n",
              "      <td>34738</td>\n",
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              "      <td>1</td>\n",
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              "<p>5 rows × 88 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train test split**"
      ],
      "metadata": {
        "id": "MMGRgMzIJa5f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Shuffle dataset \n",
        "shuffle_train = train.sample(frac=1,random_state=1).reset_index(drop=True)\n",
        "\n",
        "# Define size for train set \n",
        "train_size = int(0.7 * len(train))\n",
        "\n",
        "# Split dataset \n",
        "train_set = shuffle_train[:train_size]\n",
        "test_set = shuffle_train[train_size:]"
      ],
      "metadata": {
        "id": "XLPqpEDkJdPi"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "Owl42VsxKHea",
        "outputId": "a5e2ef61-3522-4529-8f6a-6673b33ebb4c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   policy_tenure  age_of_car  age_of_policyholder  population_density  make  \\\n",
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              "4       0.893027        0.07             0.461538                8794     3   \n",
              "\n",
              "   airbags  displacement  cylinder  transmission_type  gear_box  ...  \\\n",
              "0        2          1197         4                  0         5  ...   \n",
              "1        2           999         3                  1         5  ...   \n",
              "2        2          1197         4                  0         5  ...   \n",
              "3        2           998         3                  0         5  ...   \n",
              "4        6          1493         4                  1         6  ...   \n",
              "\n",
              "   is_rear_window_washer_Yes  is_rear_window_defogger_Yes  \\\n",
              "0                          0                            0   \n",
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              "   is_brake_assist_Yes  is_power_door_locks_Yes  is_central_locking_Yes  \\\n",
              "0                    1                        1                       1   \n",
              "1                    0                        1                       1   \n",
              "2                    1                        1                       1   \n",
              "3                    0                        1                       1   \n",
              "4                    1                        1                       1   \n",
              "\n",
              "   is_power_steering_Yes  is_driver_seat_height_adjustable_Yes  \\\n",
              "0                      1                                     1   \n",
              "1                      1                                     0   \n",
              "2                      1                                     1   \n",
              "3                      1                                     0   \n",
              "4                      1                                     1   \n",
              "\n",
              "   is_day_night_rear_view_mirror_Yes  is_ecw_Yes  is_speed_alert_Yes  \n",
              "0                                  1           1                   1  \n",
              "1                                  1           1                   1  \n",
              "2                                  1           1                   1  \n",
              "3                                  0           1                   1  \n",
              "4                                  0           1                   1  \n",
              "\n",
              "[5 rows x 88 columns]"
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.shape,test_set.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-iA6qCgiK2A-",
        "outputId": "d7b461c2-656a-42a9-feb2-da9e1685a887"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((41014, 88), (17578, 88))"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Declare feature vector and target variable**"
      ],
      "metadata": {
        "id": "Y5DYb9DmI_J1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = train_set.drop(['is_claim'], axis=1)\n",
        "y = train_set['is_claim']\n",
        "X_test_init = test_set.copy()\n",
        "X_test = X_test_init.drop(['is_claim'], axis=1)\n",
        "y_test = X_test_init['is_claim']"
      ],
      "metadata": {
        "id": "i8cQTZhlJAdE"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Use SMOTE to handle imbalanced data**"
      ],
      "metadata": {
        "id": "ZR5CirDuNX3m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE"
      ],
      "metadata": {
        "id": "8Z1GrWAENZpn"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "balance = SMOTE(random_state=7, sampling_strategy=0.8)\n",
        "X_train, y_train = balance.fit_resample(X,y)\n",
        "print('Original training dataset shape :\\n{}'.format(y.value_counts()))\n",
        "print('Resampled training dataset shape :\\n{}'.format(y_train.value_counts()))\n",
        "sns.barplot(x = [0, 1], y = [38411, 30728])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "t--1nW5ms59v",
        "outputId": "54ff8352-46cc-49b8-abc5-019e325baa8c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original training dataset shape :\n",
            "0    38411\n",
            "1     2603\n",
            "Name: is_claim, dtype: int64\n",
            "Resampled training dataset shape :\n",
            "0    38411\n",
            "1    30728\n",
            "Name: is_claim, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faf90e7f730>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Naïve Bayer classifier"
      ],
      "metadata": {
        "id": "yLlU5tRB24RG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model training**"
      ],
      "metadata": {
        "id": "9Sx_Cuhv3bjU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "class Gaussian_Bayes():\n",
        "  def __init__(self):\n",
        "    pass\n",
        "  # Build the model\n",
        "  def fit(self,X_train,y_train):\n",
        "    self.training_data = X_train.copy()\n",
        "    self.gb_y_train = pd.DataFrame(y_train.values, columns = ['claim'])\n",
        "    self.training_data['claim'] = y_train.values\n",
        "    self.find_mean = self.training_data.groupby(\"claim\").mean() # Mean for gaussian\n",
        "    self.find_var = self.training_data.groupby(\"claim\").var() # Variance for gaussian\n",
        "    self.prior_prob = self.Prior()\n",
        "    return self\n",
        "  \n",
        "  # Prior probability\n",
        "  def Prior(self):\n",
        "    label = self.gb_y_train['claim'].value_counts().sort_index()\n",
        "    pri = []\n",
        "    for c in label :\n",
        "      pri.append(c/sum(label))\n",
        "    prior_prob = np.array(pri)\n",
        "    return prior_prob\n",
        "    \n",
        "  # Calculate with gaussian distribution\n",
        "  def Distribution(self,mu,variance,feature):\n",
        "    norm = 1/np.sqrt(2*math.pi*variance)*np.exp(-(feature-mu)**2/(2*variance)) # Normal distribution\n",
        "    norm = pd.DataFrame(norm).prod(axis=1) # P(x1,x2,...,xn|yi)\n",
        "    return norm\n",
        "\n",
        "  # Prediction\n",
        "  def predict(self,X_test):\n",
        "    final_prob = []\n",
        "    for feature in X_test.values :\n",
        "      prob=self.Distribution(self.find_mean,self.find_var,feature)*self.prior_prob\n",
        "      final_prob.append(prob)\n",
        "    result=np.argmax(final_prob, axis=1)  # argmax for prediction\n",
        "    return result"
      ],
      "metadata": {
        "id": "x3j5GSeo1fOY"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nb = Gaussian_Bayes()"
      ],
      "metadata": {
        "id": "Fyug0gWUWdmY"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nb.fit(X_train,y_train)\n",
        "# Test set prediction\n",
        "y_test_nb = nb.predict(X_test)\n",
        "# Train set prediction\n",
        "y_train_nb = nb.predict(X_train)"
      ],
      "metadata": {
        "id": "s9fti-xHqH4J"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Check accuracy score**"
      ],
      "metadata": {
        "id": "fe2cSXZyaJIz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "3nUjvjlWaLfo"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train set accuracy\n",
        "train_accuracy_nb = accuracy_score(y_train,y_train_nb)\n",
        "# Test set accuracy\n",
        "test_accuracy_nb = accuracy_score(y_test,y_test_nb)\n",
        "\n",
        "print('Test set accuracy : {0:0.4f}'. format(test_accuracy_nb))\n",
        "print('Train set accuracy : {0:0.4f}'. format(train_accuracy_nb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-SzwEsefJsC",
        "outputId": "732410cc-e8b5-427d-c98b-736bbbcca5e2"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set accuracy : 0.2218\n",
            "Train set accuracy : 0.4895\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Print the Confusion Matrix and slice it into four pieces\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "pCVWq0ZXS1vc"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm_nb = confusion_matrix(y_test, y_test_nb)\n",
        "# Visualize confusion matrix \n",
        "sns.set(rc = {'figure.figsize':(5,4)})\n",
        "confusion_matrix_nb = pd.DataFrame(data=cm_nb, columns=['Predict Negative:0', 'Predict Positive:1'], index=['Actual Negative:0', 'Actual Positive:1'])\n",
        "sns.heatmap(confusion_matrix_nb, annot=True, fmt='d', cmap='YlGnBu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "Kb2MxEHUnR6B",
        "outputId": "ec98b8d8-1524-4b68-fe04-1bddcef8cb0f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faf90dc3c70>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest Classifier"
      ],
      "metadata": {
        "id": "UNczXK34svkm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Entropy and information_gain**"
      ],
      "metadata": {
        "id": "g4RlzKpEyy4Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Node:\n",
        "    def __init__(self,predicted_class):\n",
        "        self.predicted_class = predicted_class\n",
        "        self.feature_index = 0\n",
        "        self.threshold = 0\n",
        "        self.left = None\n",
        "        self.right = None\n",
        "\n",
        "class DecisionTree:\n",
        "    def __init__(self,max_depth=None,max_features=None,random_state=None):\n",
        "        self.max_depth = max_depth\n",
        "        self.max_features = max_features\n",
        "        self.random_state = random_state\n",
        "        self.tree = None\n",
        "    \n",
        "    def fit(self, X, y):\n",
        "        # store number of classes and features of the dataset into model object\n",
        "        if isinstance(X, pd.core.frame.DataFrame):\n",
        "            X = X.values\n",
        "        if isinstance(y, pd.core.series.Series):\n",
        "            y = y.values\n",
        "\n",
        "        self.n_classes = len(set(y))\n",
        "        self.n_features = X.shape[1]\n",
        "        if self.max_features==None:\n",
        "            self.max_features = self.n_features\n",
        "\n",
        "        if isinstance(self.max_features,float) and self.max_features<=1:\n",
        "            self.max_features = int(self.max_features*self.n_features)\n",
        "\n",
        "        # create tree for the dataset\n",
        "        self.tree = self.grow_tree(X,y,self.random_state)\n",
        "\n",
        "    def grow_tree(self, X, y, random_state, depth=0):\n",
        "    \n",
        "        num_samples_per_class = [np.sum(y == i) for i in range(self.n_classes)]\n",
        "        predicted_class = np.argmax(num_samples_per_class)\n",
        "\n",
        "        node = Node(predicted_class=predicted_class)\n",
        "\n",
        "        if (self.max_depth is None) or (depth < self.max_depth):\n",
        "            id, thr = self.best_split(X, y, random_state)\n",
        "\n",
        "            if id is not None:\n",
        "                if random_state is not None:\n",
        "                    random_state += 1\n",
        "\n",
        "                indices_left = X[:, id] < thr\n",
        "                X_left, y_left = X[indices_left], y[indices_left]\n",
        "                X_right, y_right = X[~indices_left], y[~indices_left]\n",
        "                \n",
        "                node.feature_index = id\n",
        "                node.threshold = thr\n",
        "                node.left = self.grow_tree(X_left, y_left, random_state, depth + 1)\n",
        "                node.right = self.grow_tree(X_right, y_right, random_state, depth + 1)\n",
        "\n",
        "        return node  \n",
        "      \n",
        "    def best_split(self, X, y, random_state):\n",
        "\n",
        "        m = len(y)\n",
        "        if m <= 1:\n",
        "            return None, None\n",
        "\n",
        "        num_class_parent = [np.sum(y==c) for c in range(self.n_classes)]\n",
        "        best_gini = 1.0 - sum((n / m) ** 2 for n in num_class_parent)\n",
        "        if best_gini == 0:\n",
        "            return None, None\n",
        "\n",
        "        best_feat_id, best_threshold = None, None\n",
        "\n",
        "        random.seed(random_state)\n",
        "        feat_indices = random.sample(range(self.n_features),self.max_features)\n",
        "\n",
        "        for feat_id in feat_indices:\n",
        "\n",
        "            sorted_column = sorted(set(X[:,feat_id]))\n",
        "            threshold_values = [np.mean([a,b]) for a,b in zip(sorted_column,sorted_column[1:])]\n",
        "\n",
        "            for threshold in threshold_values:\n",
        "\n",
        "                left_y = y[X[:,feat_id]<threshold]\n",
        "                right_y = y[X[:,feat_id]>threshold]\n",
        "\n",
        "                num_class_left = [np.sum(left_y==c) for c in range(self.n_classes)]\n",
        "                num_class_right = [np.sum(right_y==c) for c in range(self.n_classes)]\n",
        "\n",
        "                gini_left = 1.0 - sum((n / len(left_y)) ** 2 for n in num_class_left)\n",
        "                gini_right = 1.0 - sum((n / len(right_y)) ** 2 for n in num_class_right)\n",
        "\n",
        "                gini = (len(left_y)/m)*gini_left + (len(right_y)/m)*gini_right\n",
        "\n",
        "                if gini < best_gini:\n",
        "                    best_gini = gini\n",
        "                    best_feat_id = feat_id\n",
        "                    best_threshold = threshold\n",
        "\n",
        "        return best_feat_id, best_threshold\n",
        "\n",
        "    def predict(self,X):\n",
        "    \n",
        "        if isinstance(X, pd.core.frame.DataFrame):\n",
        "            X = X.values\n",
        "\n",
        "        predicted_classes = np.array([self.predict_example(inputs) for inputs in X])\n",
        "\n",
        "        return predicted_classes\n",
        "\n",
        "    def predict_example(self, inputs):\n",
        "\n",
        "        node = self.tree\n",
        "\n",
        "        while node.left:\n",
        "            if inputs[node.feature_index] < node.threshold:\n",
        "                node = node.left\n",
        "            else:\n",
        "                node = node.right\n",
        "\n",
        "        return node.predicted_class "
      ],
      "metadata": {
        "id": "-XM1GbjBMz7l"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "import random\n",
        "\n",
        "class RandomForest:\n",
        "    def __init__(self, num_trees=5, subsample_size=None, max_depth=5, max_features=5, bootstrap=True, random_state=None):\n",
        "        self.num_trees = num_trees\n",
        "        self.subsample_size = subsample_size\n",
        "        self.max_depth = max_depth\n",
        "        self.max_features = max_features\n",
        "        self.bootstrap = bootstrap\n",
        "        self.random_state = random_state\n",
        "        # Will store individually trained decision trees\n",
        "        self.decision_trees = []\n",
        "    def fit(self, X, y):\n",
        "        # Reset\n",
        "        if len(self.decision_trees) > 0:\n",
        "            self.decision_trees = []\n",
        "\n",
        "        if isinstance(X, pd.core.frame.DataFrame):\n",
        "            X = X.values\n",
        "        if isinstance(y, pd.core.series.Series):\n",
        "            y = y.values\n",
        "            \n",
        "        # Build each tree of the forest\n",
        "        num_built = 0\n",
        "\n",
        "        while num_built < self.num_trees:\n",
        "\n",
        "            clf = DecisionTree(\n",
        "                max_depth=self.max_depth,\n",
        "                max_features=self.max_features,\n",
        "                random_state=self.random_state\n",
        "            )\n",
        "\n",
        "            # Obtain data sample\n",
        "            _X, _y = self.sample(X, y, self.random_state)\n",
        "            # Train\n",
        "            clf.fit(_X, _y)\n",
        "            # Save the classifier\n",
        "            self.decision_trees.append(clf)\n",
        "            \n",
        "            num_built += 1\n",
        "\n",
        "            if self.random_state is not None:\n",
        "                self.random_state += 1\n",
        "\n",
        "    def sample(self, X, y, random_state):\n",
        "\n",
        "        n_rows, n_cols = X.shape\n",
        "\n",
        "        # Sample with replacement\n",
        "        if self.subsample_size is None:\n",
        "            sample_size = n_rows\n",
        "        else:\n",
        "            sample_size = int(n_rows*self.subsample_size)\n",
        "\n",
        "        np.random.seed(random_state)\n",
        "        samples = np.random.choice(a=n_rows, size=sample_size, replace=self.bootstrap)\n",
        "\n",
        "        return X[samples], y[samples]\n",
        "    \n",
        "    def predict(self, X):\n",
        "        # Make predictions with every tree in the forest\n",
        "        y = []\n",
        "        for tree in self.decision_trees:\n",
        "            y.append(tree.predict(X))\n",
        "        \n",
        "        # Reshape so we can find the most common value\n",
        "        y = np.swapaxes(y, axis1=0, axis2=1)\n",
        "        \n",
        "        # Use majority voting for the final prediction\n",
        "        predicted_classes = stats.mode(y,axis=1)[0].reshape(-1)\n",
        "\n",
        "        return predicted_classes\n",
        "\n",
        "    "
      ],
      "metadata": {
        "id": "h6DWIKL6N4KQ"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rfc_s = RandomForest()"
      ],
      "metadata": {
        "id": "nAGeSrTsPCQ3"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rfc_s.fit(X_train,y_train)\n",
        "# Test set prediction\n",
        "y_test_rfc_s = rfc_s.predict(X_test)\n",
        "# Train set prediction\n",
        "y_train_rfc_s = rfc_s.predict(X_train)"
      ],
      "metadata": {
        "id": "we4ppfLZOfBH"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train set accuracy\n",
        "train_accuracy_rfc_s = accuracy_score(y_train,y_train_rfc_s)\n",
        "# Test set accuracy\n",
        "test_accuracy_rfc_s = accuracy_score(y_test,y_test_rfc_s)\n",
        "\n",
        "print('Test set accuracy : {0:0.4f}'. format(test_accuracy_rfc_s))\n",
        "print('Train set accuracy : {0:0.4f}'. format(train_accuracy_rfc_s))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CiX_NxQMQ6DC",
        "outputId": "ca29d52e-c676-40ef-8eef-3120526932e0"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set accuracy : 0.9312\n",
            "Train set accuracy : 0.5569\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize confusion matrix \n",
        "cm_rfc_s = confusion_matrix(y_test, y_test_rfc_s)\n",
        "sns.set(rc = {'figure.figsize':(5,4)})\n",
        "confusion_matrix_rfc_s = pd.DataFrame(data=cm_rfc_s, columns=['Predict Negative:0', 'Predict Positive:1'], index=['Actual Negative:0', 'Actual Positive:1'])\n",
        "sns.heatmap(confusion_matrix_rfc_s, annot=True, fmt='d', cmap='YlGnBu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "PCesxhLKRJBE",
        "outputId": "fdf10350-5583-414f-aff8-cf376b20a11c"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faf90e72fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest Classifier (sklearn)"
      ],
      "metadata": {
        "id": "xSyGBWUZTmTz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "RCTlVIjdTmxB"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rfc = RandomForestClassifier(n_estimators=100,\n",
        "              criterion='gini',max_depth=10,max_features='log2',\n",
        "              min_samples_leaf=3,min_samples_split=10,random_state=17)                      "
      ],
      "metadata": {
        "id": "xTLk7iqJUkTw"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model training\n",
        "rfc.fit(X_train,y_train)\n",
        "# Test set prediction\n",
        "y_test_rfc = rfc.predict(X_test)\n",
        "# Train set prediction\n",
        "y_train_rfc = rfc.predict(X_train)"
      ],
      "metadata": {
        "id": "pPxpRRvNVKag"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train set accuracy\n",
        "train_accuracy_rfc = accuracy_score(y_train,y_train_rfc)\n",
        "# Test set accuracy\n",
        "test_accuracy_rfc = accuracy_score(y_test,y_test_rfc)\n",
        "\n",
        "print('Test set accuracy : {0:0.4f}'. format(test_accuracy_rfc))\n",
        "print('Train set accuracy : {0:0.4f}'. format(train_accuracy_rfc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xtWoA2zzb1qx",
        "outputId": "0a800616-3682-417e-bde3-21ab4e269119"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set accuracy : 0.7858\n",
            "Train set accuracy : 0.6963\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize confusion matrix \n",
        "cm_rfc = confusion_matrix(y_test, y_test_rfc)\n",
        "confusion_matrix_rfc = pd.DataFrame(data=cm_rfc, columns=['Predict Negative:0', 'Predict Positive:1'], index=['Actual Negative:0', 'Actual Positive:1'])\n",
        "sns.heatmap(confusion_matrix_rfc, annot=True, fmt='d', cmap='YlGnBu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "Z7sVRuqxANwz",
        "outputId": "6db1086f-fbb6-4f97-e15a-cae98f8f313c"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faf90e19520>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# XGBoost"
      ],
      "metadata": {
        "id": "A0GLuzukr52u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier"
      ],
      "metadata": {
        "id": "sBug1RMTr4_l"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb = XGBClassifier(n_estimators=100, learning_rate= 0.4)"
      ],
      "metadata": {
        "id": "wsSyGEuqPWja"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model training\n",
        "xgb.fit(X_train, y_train)\n",
        "# Test set prediction\n",
        "y_test_xgb = xgb.predict(X_test)\n",
        "# Train set prediction\n",
        "y_train_xgb = xgb.predict(X_train)"
      ],
      "metadata": {
        "id": "jIAwQKYUsHs4"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train set accuracy\n",
        "train_accuracy_xgb = accuracy_score(y_train,y_train_xgb)\n",
        "# Test set accuracy\n",
        "test_accuracy_xgb = accuracy_score(y_test,y_test_xgb)\n",
        "\n",
        "print('Test set accuracy : {0:0.4f}'. format(test_accuracy_xgb))\n",
        "print('Train set accuracy : {0:0.4f}'. format(train_accuracy_xgb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WoyOv7SN0tie",
        "outputId": "05d096df-494b-440d-a680-091e32853bfb"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set accuracy : 0.9225\n",
            "Train set accuracy : 0.9353\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize confusion matrix \n",
        "cm_xgb = confusion_matrix(y_test, y_test_xgb)\n",
        "confusion_matrix_xgb = pd.DataFrame(data=cm_xgb, columns=['Predict Negative:0', 'Predict Positive:1'], index=['Actual Negative:0', 'Actual Positive:1'])\n",
        "sns.heatmap(confusion_matrix_xgb, annot=True, fmt='d', cmap='YlGnBu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "T533bg9zJ-0m",
        "outputId": "396c92c1-4485-44c7-9a08-dd4450e91266"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faf90e60dc0>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Catboost"
      ],
      "metadata": {
        "id": "YoYAfsgQZ9OE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install catboost\n",
        "from catboost import CatBoostClassifier"
      ],
      "metadata": {
        "id": "NM8T8adUaAdj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8ffa8a3-0d41-40e9-bf6e-9b19b448b679"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting catboost\n",
            "  Downloading catboost-1.1.1-cp38-none-manylinux1_x86_64.whl (76.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 76.6 MB 1.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.8/dist-packages (from catboost) (1.21.6)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.8/dist-packages (from catboost) (5.5.0)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.8/dist-packages (from catboost) (1.3.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from catboost) (1.7.3)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.8/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.24.0->catboost) (2022.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->catboost) (1.4.4)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->catboost) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly->catboost) (8.1.0)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-1.1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cbc = CatBoostClassifier(iterations=100,learning_rate=0.4,verbose=0 )  "
      ],
      "metadata": {
        "id": "hiAAB_6zPaaC"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model training\n",
        "cbc.fit(X_train,y_train)\n",
        "# Test set prediction\n",
        "y_test_cbc = cbc.predict(X_test)\n",
        "# Train set prediction\n",
        "y_train_cbc = cbc.predict(X_train)"
      ],
      "metadata": {
        "id": "fYXraZ-BbROl"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train set accuracy\n",
        "train_accuracy_cbc = accuracy_score(y_train,y_train_cbc)\n",
        "# Test set accuracy\n",
        "test_accuracy_cbc = accuracy_score(y_test,y_test_cbc)\n",
        "\n",
        "print('Test set accuracy : {0:0.4f}'. format(test_accuracy_cbc))\n",
        "print('Train set accuracy : {0:0.4f}'. format(train_accuracy_cbc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4bZfFpQdohL",
        "outputId": "3bcd6a9e-5d24-4230-d5b0-b7cf804511f9"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set accuracy : 0.9331\n",
            "Train set accuracy : 0.9587\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize confusion matrix \n",
        "cm_cbc = confusion_matrix(y_test, y_test_cbc)\n",
        "confusion_matrix_cbc = pd.DataFrame(data=cm_cbc, columns=['Predict Negative:0', 'Predict Positive:1'], index=['Actual Negative:0', 'Actual Positive:1'])\n",
        "sns.heatmap(confusion_matrix_cbc, annot=True, fmt='d', cmap='YlGnBu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "v400s3jbeNmz",
        "outputId": "e8db74c0-e1e7-42ac-aabd-8dc29c836b89"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faf8f385430>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LightGBM"
      ],
      "metadata": {
        "id": "1FJX5I3feadW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import lightgbm as lgb\n",
        "from lightgbm import LGBMClassifier"
      ],
      "metadata": {
        "id": "A5hD2rlAedZc"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm = lgb.LGBMClassifier(n_estimators = 100,learning_rate = 0.4)"
      ],
      "metadata": {
        "id": "eLrdqOgpPd56"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model training\n",
        "lgbm.fit(X_train, y_train)\n",
        "# Test set prediction\n",
        "y_test_lgbm = lgbm.predict(X_test)\n",
        "# Train set prediction\n",
        "y_train_lgbm = lgbm.predict(X_train)"
      ],
      "metadata": {
        "id": "4bh0f7rBfbLa"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train set accuracy\n",
        "train_accuracy_lgbm = accuracy_score(y_train,y_train_lgbm)\n",
        "# Test set accuracy\n",
        "test_accuracy_lgbm = accuracy_score(y_test,y_test_lgbm)\n",
        "\n",
        "print('Test set accuracy : {0:0.4f}'. format(test_accuracy_lgbm))\n",
        "print('Train set accuracy : {0:0.4f}'. format(train_accuracy_lgbm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L7DtadL9f7BW",
        "outputId": "fea174dc-27c6-4143-999f-f1a7565ccc6b"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set accuracy : 0.9316\n",
            "Train set accuracy : 0.9624\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize confusion matrix \n",
        "cm_lgbm = confusion_matrix(y_test, y_test_lgbm)\n",
        "confusion_matrix_lgbm = pd.DataFrame(data=cm_lgbm, columns=['Predict Negative:0', 'Predict Positive:1'], index=['Actual Negative:0', 'Actual Positive:1'])\n",
        "sns.heatmap(confusion_matrix_lgbm, annot=True, fmt='d', cmap='YlGnBu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "rCcRSxcVgFND",
        "outputId": "f3f226a9-8983-4312-fd2d-51f934d64e71"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faf8f14a280>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# K-Fold Cross-Validation"
      ],
      "metadata": {
        "id": "MzOGo6oSbIK7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_validate\n",
        "from sklearn.model_selection import KFold, cross_val_score\n",
        "from sklearn.metrics import precision_score,recall_score,f1_score"
      ],
      "metadata": {
        "id": "XHMKxP3wbHVq"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_cvb = train.drop(['is_claim'], axis=1)\n",
        "y_cvb = train['is_claim']"
      ],
      "metadata": {
        "id": "CfA4b6DbcSg_"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Cross_validation(model_name,k):\n",
        "  kf_model = KFold(n_splits=k)\n",
        "  acc_score = []\n",
        "  pre_score = []\n",
        "  rec_score = []\n",
        "  f_score = []\n",
        "  for train_index, test_index in kf_model.split(X_cvb):\n",
        "    X_train_model_kv, X_test_model_kv = X_cvb.iloc[train_index,:], X_cvb.iloc[test_index,:]\n",
        "    y_train_model_kv, y_test_model_kv = y_cvb[train_index], y_cvb[test_index]\n",
        "    \n",
        "    cv_sm = SMOTE(random_state=97, sampling_strategy=0.8)\n",
        "    X_train_model_kv,y_train_model_kv = cv_sm.fit_resample(X_train_model_kv,y_train_model_kv)\n",
        "    \n",
        "    model_name.fit(X_train_model_kv,y_train_model_kv)\n",
        "    model_kv_pred = model_name.predict(X_test_model_kv)\n",
        "\n",
        "    acc_kv = accuracy_score(y_test_model_kv,model_kv_pred)\n",
        "    acc_score.append(acc_kv)\n",
        "    pre_kv = precision_score(y_test_model_kv,model_kv_pred)\n",
        "    pre_score.append(pre_kv)\n",
        "    rec_kv = recall_score(y_test_model_kv,model_kv_pred)\n",
        "    rec_score.append(rec_kv)\n",
        "    f_kv = f1_score(y_test_model_kv,model_kv_pred)\n",
        "    f_score.append(f_kv)\n",
        "\n",
        "  avg_acc_score = sum(acc_score)/k*100\n",
        "  avg_pre_score = sum(pre_score)/k\n",
        "  avg_rec_score = sum(rec_score)/k\n",
        "  avg_f_score = sum(f_score)/k\n",
        "  print(\"{} Fold Mean Training Accuracy : \".format(k),avg_acc_score)\n",
        "  print(\"{} Fold Mean Training Precision : \".format(k),avg_pre_score)\n",
        "  print(\"{} Fold Mean Training Recall : \".format(k),avg_rec_score)\n",
        "  print(\"{} Fold Mean Training F1 : \".format(k),avg_f_score)\n",
        "  return [avg_acc_score,avg_pre_score,avg_rec_score,avg_f_score]"
      ],
      "metadata": {
        "id": "gOQT1_reLuEj"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random forest classifier (sklearn)**"
      ],
      "metadata": {
        "id": "GEFMR7PSdhPI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rfc_result_3 = Cross_validation(rfc,3)\n",
        "rfc_result_5 = Cross_validation(rfc,5)\n",
        "rfc_result_10 = Cross_validation(rfc,10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-ShzyDTM--a",
        "outputId": "eddcd981-45ce-4e5f-be89-94310140600c"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3 Fold Mean Training Accuracy :  78.09254960204403\n",
            "3 Fold Mean Training Precision :  0.10356933173315892\n",
            "3 Fold Mean Training Recall :  0.3155314259535095\n",
            "3 Fold Mean Training F1 :  0.1557992470950356\n",
            "5 Fold Mean Training Accuracy :  77.19824106686907\n",
            "5 Fold Mean Training Precision :  0.10420460162147585\n",
            "5 Fold Mean Training Recall :  0.33748637620993377\n",
            "5 Fold Mean Training F1 :  0.15918418395220124\n",
            "10 Fold Mean Training Accuracy :  76.79548630589036\n",
            "10 Fold Mean Training Precision :  0.1027652420416024\n",
            "10 Fold Mean Training Recall :  0.33976560639103437\n",
            "10 Fold Mean Training F1 :  0.15760525284023946\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**XGBoost**"
      ],
      "metadata": {
        "id": "7XZP-W2bes-5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_result_3 = Cross_validation(xgb,3)\n",
        "xgb_result_5 = Cross_validation(xgb,5)\n",
        "xgb_result_10 = Cross_validation(xgb,10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPSEUUaker2Z",
        "outputId": "d2de5e71-4ba4-496b-b96c-4244b91d9d13"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3 Fold Mean Training Accuracy :  93.14240618559145\n",
            "3 Fold Mean Training Precision :  0.11222453753759658\n",
            "3 Fold Mean Training Recall :  0.011208180455770123\n",
            "3 Fold Mean Training F1 :  0.020109784683205924\n",
            "5 Fold Mean Training Accuracy :  93.1577690249987\n",
            "5 Fold Mean Training Precision :  0.13059468128953639\n",
            "5 Fold Mean Training Recall :  0.012552465704513852\n",
            "5 Fold Mean Training F1 :  0.02281637545333886\n",
            "10 Fold Mean Training Accuracy :  93.08609432004786\n",
            "10 Fold Mean Training Precision :  0.12940399637062935\n",
            "10 Fold Mean Training Recall :  0.013204514210136662\n",
            "10 Fold Mean Training F1 :  0.02358591039307685\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**CatBoost**"
      ],
      "metadata": {
        "id": "od-8KTBIgW44"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cbc_result_3 = Cross_validation(cbc,3)\n",
        "cbc_result_5 = Cross_validation(cbc,5)\n",
        "cbc_result_10 = Cross_validation(cbc,10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0zlE4sNgZ7i",
        "outputId": "ab27e470-dfc4-4eb6-bc1a-8b70eba821f8"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3 Fold Mean Training Accuracy :  93.47863133788238\n",
            "3 Fold Mean Training Precision :  0.14337356224148678\n",
            "3 Fold Mean Training Recall :  0.00429313136404949\n",
            "3 Fold Mean Training F1 :  0.00829049061999932\n",
            "5 Fold Mean Training Accuracy :  93.50764745271597\n",
            "5 Fold Mean Training Precision :  0.14144796380090502\n",
            "5 Fold Mean Training Recall :  0.003211894478791613\n",
            "5 Fold Mean Training F1 :  0.006266777677489901\n",
            "10 Fold Mean Training Accuracy :  93.48887566574454\n",
            "10 Fold Mean Training Precision :  0.11666666666666667\n",
            "10 Fold Mean Training Recall :  0.0029670501248123355\n",
            "10 Fold Mean Training F1 :  0.005776362760441749\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LightGBM**"
      ],
      "metadata": {
        "id": "Qt2UzW-0gnkj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm_result_3 = Cross_validation(lgbm,3)\n",
        "lgbm_result_5 = Cross_validation(lgbm,5)\n",
        "lgbm_result_10 = Cross_validation(lgbm,10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6Aiqj_ygsjf",
        "outputId": "24391c2b-40a9-43b7-9205-0438b00cfcaf"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3 Fold Mean Training Accuracy :  93.22774349501091\n",
            "3 Fold Mean Training Precision :  0.10466661078215456\n",
            "3 Fold Mean Training Recall :  0.007740769450094126\n",
            "3 Fold Mean Training F1 :  0.014408368864528802\n",
            "5 Fold Mean Training Accuracy :  93.24822450667163\n",
            "5 Fold Mean Training Precision :  0.10135954926381023\n",
            "5 Fold Mean Training Recall :  0.006936749428503079\n",
            "5 Fold Mean Training F1 :  0.012980928003995992\n",
            "10 Fold Mean Training Accuracy :  93.28235869439216\n",
            "10 Fold Mean Training Precision :  0.09993074189780102\n",
            "10 Fold Mean Training Recall :  0.00613876457493379\n",
            "10 Fold Mean Training F1 :  0.01154880788358814\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Naive Bayer**"
      ],
      "metadata": {
        "id": "P63wsfDug007"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nb_kv3 = Cross_validation(nb,3)\n",
        "nb_kv5 = Cross_validation(nb,5)\n",
        "nb_kv10 = Cross_validation(nb,10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4_4OmKNNjf8",
        "outputId": "3149c93f-d29e-48a2-d8c3-64bc4addac06"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3 Fold Mean Training Accuracy :  25.047788807634607\n",
            "3 Fold Mean Training Precision :  0.06598983430477313\n",
            "3 Fold Mean Training Recall :  0.8143710357066004\n",
            "3 Fold Mean Training F1 :  0.12205422186290756\n",
            "5 Fold Mean Training Accuracy :  25.94881527775175\n",
            "5 Fold Mean Training Precision :  0.06620444844084622\n",
            "5 Fold Mean Training Recall :  0.8066774765492462\n",
            "5 Fold Mean Training F1 :  0.12233323088826405\n",
            "10 Fold Mean Training Accuracy :  26.97121344776304\n",
            "10 Fold Mean Training Precision :  0.06681723450476497\n",
            "10 Fold Mean Training Recall :  0.8033712659900974\n",
            "10 Fold Mean Training F1 :  0.12333635503397153\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random Forest Classifier**"
      ],
      "metadata": {
        "id": "RFThAEmzRTfs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rfc_s_kv3 = Cross_validation(rfc_s,3)\n",
        "rfc_s_kv5 = Cross_validation(rfc_s,5)\n",
        "rfc_s_kv10 = Cross_validation(rfc_s,10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HfQsmnxBSJl0",
        "outputId": "a726db9a-f321-4129-9019-ce9d743aa528"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3 Fold Mean Training Accuracy :  85.89043117427273\n",
            "3 Fold Mean Training Precision :  0.08569098084356354\n",
            "3 Fold Mean Training Recall :  0.12989346350971007\n",
            "3 Fold Mean Training F1 :  0.0886753898470775\n",
            "5 Fold Mean Training Accuracy :  92.63206995943193\n",
            "5 Fold Mean Training Precision :  0.05659711078733045\n",
            "5 Fold Mean Training Recall :  0.023986289388606648\n",
            "5 Fold Mean Training F1 :  0.0281752546071301\n",
            "10 Fold Mean Training Accuracy :  88.98981235367891\n",
            "10 Fold Mean Training Precision :  0.11595655586545324\n",
            "10 Fold Mean Training Recall :  0.07835729018686227\n",
            "10 Fold Mean Training F1 :  0.05423170215654509\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conclusion - kfold**"
      ],
      "metadata": {
        "id": "8wxbAtiOgR_P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kfold_df = pd.DataFrame([\n",
        "    ['Naive Bayer', 'k=3', nb_kv3[0]],\n",
        "    ['Naive Bayer', 'k=5', nb_kv5[0]],\n",
        "    ['Naive Bayer', 'k=10', nb_kv10[0]],\n",
        "    ['Random forest', 'k=3', rfc_s_kv3[0]],\n",
        "    ['Random forest', 'k=5', rfc_s_kv5[0]],\n",
        "    ['Random forest', 'k=10', rfc_s_kv10[0]],\n",
        "    ['Random forest(sklearn)', 'k=3', rfc_result_3[0]],\n",
        "    ['Random forest(sklearn)', 'k=5', rfc_result_5[0]],\n",
        "    ['Random forest(sklearn)', 'k=10', rfc_result_10[0]],\n",
        "    ['XGBoost', 'k=3', xgb_result_3[0]],\n",
        "    ['XGBoost', 'k=5', xgb_result_5[0]],\n",
        "    ['XGBoost', 'k=10', xgb_result_10[0]],\n",
        "    ['Catboost', 'k=3', cbc_result_3[0]],\n",
        "    ['Catboost', 'k=5', cbc_result_5[0]],\n",
        "    ['Catboost', 'k=10', cbc_result_10[0]],\n",
        "    ['LightGBM', 'k=3', lgbm_result_3[0]],\n",
        "    ['LightGBM', 'k=5', lgbm_result_5[0]],\n",
        "    ['LightGBM', 'k=10', lgbm_result_10[0]]], columns=['Models', 'k fold', 'Accuracy'])\n",
        "plt.xticks(rotation=-15,size=15)\n",
        "plt.yticks(size=15)\n",
        "sns.set(rc = {'figure.figsize':(25,12)})\n",
        "kf_acc_plot = sns.barplot(x='Models', y='Accuracy', hue='k fold', data=kfold_df)\n",
        "kf_acc_plot.set_title('Comparison of accuracy with different k cross-validation',size=25)\n",
        "for p in kf_acc_plot.patches:\n",
        "    kf_acc_plot.annotate(f'\\n{round(p.get_height(),2)}', (p.get_x(), p.get_height()), color='black', size=14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "nOn60pNDgUX8",
        "outputId": "267e2b20-4e4c-409d-a37d-35c58ebcc7d1"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kfold_rec_df = pd.DataFrame([\n",
        "    ['Naive Bayer', 'k=3', nb_kv3[2]],\n",
        "    ['Naive Bayer', 'k=5', nb_kv5[2]],\n",
        "    ['Naive Bayer', 'k=10', nb_kv10[2]],\n",
        "    ['Random forest', 'k=3', rfc_s_kv3[2]],\n",
        "    ['Random forest', 'k=5', rfc_s_kv5[2]],\n",
        "    ['Random forest', 'k=10', rfc_s_kv10[2]],\n",
        "    ['Random forest(sklearn)', 'k=3', rfc_result_3[2]],\n",
        "    ['Random forest(sklearn)', 'k=5', rfc_result_5[2]],\n",
        "    ['Random forest(sklearn)', 'k=10', rfc_result_10[2]],\n",
        "    ['XGBoost', 'k=3', xgb_result_3[2]],\n",
        "    ['XGBoost', 'k=5', xgb_result_5[2]],\n",
        "    ['XGBoost', 'k=10', xgb_result_10[2]],\n",
        "    ['Catboost', 'k=3', cbc_result_3[2]],\n",
        "    ['Catboost', 'k=5', cbc_result_5[2]],\n",
        "    ['Catboost', 'k=10', cbc_result_10[2]],\n",
        "    ['LightGBM', 'k=3', lgbm_result_3[2]],\n",
        "    ['LightGBM', 'k=5', lgbm_result_5[2]],\n",
        "    ['LightGBM', 'k=10', lgbm_result_10[2]]], columns=['Models', 'k fold', 'Recall'])\n",
        "plt.xticks(rotation=-15,size=15)\n",
        "plt.yticks(size=15)\n",
        "sns.set(rc = {'figure.figsize':(25,12)})\n",
        "kf_rec_plot = sns.barplot(x='Models', y='Recall', hue='k fold', data=kfold_rec_df)\n",
        "kf_rec_plot.set_title('Comparison of recall with different k cross-validation',size=25)\n",
        "for p in kf_rec_plot.patches:\n",
        "    kf_rec_plot.annotate(f'\\n{round(p.get_height(),4)}', (p.get_x(), p.get_height()), color='black', size=14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "wDycKKuJuNeU",
        "outputId": "f5a7ba1a-686d-4b53-f534-2a3e7dc709df"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x864 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conclusion - 5 fold cross-validation and model accuracy"
      ],
      "metadata": {
        "id": "NL0mM4h1mF6e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "perform_df = pd.DataFrame([\n",
        "    ['Naive Bayer', 'k=1', test_accuracy_nb],\n",
        "    ['Naive Bayer', 'k=5', nb_kv5[0]*0.01],\n",
        "    ['Random forest', 'k=1', test_accuracy_rfc_s],\n",
        "    ['Random forest', 'k=5', rfc_s_kv5[0]*0.01],\n",
        "    ['Random forest(sklearn)', 'k=1', test_accuracy_rfc],\n",
        "    ['Random forest(sklearn)', 'k=5', rfc_result_5[0]*0.01],\n",
        "    ['XGBoost', 'k=1', test_accuracy_xgb],\n",
        "    ['XGBoost', 'k=5', xgb_result_5[0]*0.01],\n",
        "    ['Catboost', 'k=1', test_accuracy_cbc],\n",
        "    ['Catboost', 'k=5', cbc_result_5[0]*0.01],\n",
        "    ['LightGBM', 'k=1', test_accuracy_lgbm],\n",
        "    ['LightGBM', 'k=5', lgbm_result_10[0]*0.01]], columns=['Models', 'k fold', 'Accuracy'])\n",
        "\n",
        "plt.xticks(rotation=-15,size=15)\n",
        "plt.yticks(size=15)\n",
        "sns.set(rc = {'figure.figsize':(15,8)})\n",
        "model_perform_plot = sns.lineplot(x='Models', y='Accuracy', hue='k fold',marker='o', data=perform_df)\n",
        "model_perform_plot.set_title(\"Comparison of model's accuracy with 5 fold cross-validation\",size=25)\n",
        "for a,b in zip(perform_df['Models'],perform_df['Accuracy']):\n",
        "  if b not in [test_accuracy_rfc_s,test_accuracy_rfc,test_accuracy_xgb , test_accuracy_cbc , test_accuracy_lgbm]:\n",
        "    plt.text(a, b, '%.4f' % b, ha='center', va= 'bottom',fontsize=12)\n",
        "  else:\n",
        "    plt.text(a, b-0.05, '%.4f' % b, ha='center', va= 'bottom',fontsize=12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "ERwYKxqxmkV4",
        "outputId": "1040532c-ed17-4c3b-a195-056be4b5e171"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AUC"
      ],
      "metadata": {
        "id": "P0WDFmDEa7OF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conclusion**"
      ],
      "metadata": {
        "id": "pxuNPAaLbCwG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "from sklearn.metrics import roc_curve, roc_auc_score, auc"
      ],
      "metadata": {
        "id": "4dBz3ETAYWnO"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def AUC(model_name,test_data_y,test_pred_y):  \n",
        "  auc = roc_auc_score(test_data_y,test_pred_y)\n",
        "  print(\"AUC of {} : \".format(model_name),auc)\n",
        "  return auc"
      ],
      "metadata": {
        "id": "u0cWe8CdbOxE"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nb_auc = AUC('Naive Bayer',y_test,y_test_nb)\n",
        "rfc_s_auc = AUC('Random Forest ',y_test,y_test_rfc_s)\n",
        "rfc_auc = AUC('Random Forest sklearn',y_test,y_test_rfc)\n",
        "xgb_auc = AUC('XGBoost',y_test,y_test_xgb)\n",
        "cbc_auc = AUC('Catboost',y_test,y_test_cbc)\n",
        "lgbm_auc = AUC('Light GBM',y_test,y_test_lgbm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzlrmaLfdSVi",
        "outputId": "6ff54564-d649-4482-d494-73083b705470"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC of Naive Bayer :  0.5203886524000991\n",
            "AUC of Random Forest  :  0.49927146276384426\n",
            "AUC of Random Forest sklearn :  0.566503603224633\n",
            "AUC of XGBoost :  0.5047725619738959\n",
            "AUC of Catboost :  0.5019005584938391\n",
            "AUC of Light GBM :  0.5031407406068894\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "auc_df = pd.DataFrame([\n",
        "    ['Naive Bayer', nb_auc],\n",
        "    ['Random forest',rfc_s_auc],\n",
        "    ['Random forest(sklearn)',rfc_auc],\n",
        "    ['XGBoost',xgb_auc],\n",
        "    ['Catboost',cbc_auc],\n",
        "    ['LightGBM',lgbm_auc]], columns=['Models','AUC'])\n",
        "plt.xticks(rotation=-15,size=15)\n",
        "plt.yticks(size=15)\n",
        "sns.set(rc = {'figure.figsize':(15,8)})\n",
        "auc_plot = sns.lineplot(x='Models', y='AUC', data=auc_df)\n",
        "auc_plot.set_title(\"Comparison of model's AUC \",size=25)\n",
        "for a,b in zip(auc_df['Models'],auc_df['AUC']):\n",
        "  plt.text(a, b, '%.4f' % b, ha='center', va= 'bottom',fontsize=12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "1GXRp2efYKiz",
        "outputId": "bdab7818-5be1-4a7d-8503-a95fb06e6ded"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recall"
      ],
      "metadata": {
        "id": "JPsdUyn6EY-D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1 fold recall**"
      ],
      "metadata": {
        "id": "C_5E4Fw20bHa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Recall(model_name,test_data_y,test_pred_y):  \n",
        "  recall = metrics.recall_score(test_data_y,test_pred_y)\n",
        "  print(\"Recall of {} : \".format(model_name),recall)\n",
        "  return recall"
      ],
      "metadata": {
        "id": "kUqiyjqgEdwk"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nb_rec = Recall('Naive Bayer',y_test,y_test_nb)\n",
        "rfc_s_rec = Recall('Random Forest ',y_test,y_test_rfc_s)\n",
        "rfc_rec = Recall('Random Forest sklearn',y_test,y_test_rfc)\n",
        "xgb_rec = Recall('XGBoost',y_test,y_test_xgb)\n",
        "cbc_rec = Recall('Catboost',y_test,y_test_cbc)\n",
        "lgbm_rec = Recall('Light GBM',y_test,y_test_lgbm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g-6cnUObFOK-",
        "outputId": "574c2526-b550-4ef5-8648-f2eddde160fa"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall of Naive Bayer :  0.8637554585152838\n",
            "Recall of Random Forest  :  0.0026200873362445414\n",
            "Recall of Random Forest sklearn :  0.314410480349345\n",
            "Recall of XGBoost :  0.02445414847161572\n",
            "Recall of Catboost :  0.00611353711790393\n",
            "Recall of Light GBM :  0.010480349344978166\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conclusion - 5 fold cross-validation and model recall"
      ],
      "metadata": {
        "id": "OfKAVjOnxtXC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rec_perform_df = pd.DataFrame([\n",
        "    ['Naive Bayer', 'k=1', nb_rec],\n",
        "    ['Naive Bayer', 'k=5', nb_kv5[2]],\n",
        "    ['Random forest', 'k=1', rfc_s_rec],\n",
        "    ['Random forest', 'k=5', rfc_s_kv5[2]],\n",
        "    ['Random forest(sklearn)', 'k=1', rfc_rec],\n",
        "    ['Random forest(sklearn)', 'k=5', rfc_result_5[2]],\n",
        "    ['XGBoost', 'k=1', xgb_rec],\n",
        "    ['XGBoost', 'k=5', xgb_result_5[2]],\n",
        "    ['Catboost', 'k=1', cbc_rec],\n",
        "    ['Catboost', 'k=5', cbc_result_5[2]],\n",
        "    ['LightGBM', 'k=1', lgbm_rec],\n",
        "    ['LightGBM', 'k=5', lgbm_result_10[2]]], columns=['Models', 'k fold', 'Recall'])\n",
        "\n",
        "plt.xticks(rotation=-15,size=15)\n",
        "plt.yticks(size=15)\n",
        "sns.set(rc = {'figure.figsize':(15,8)})\n",
        "model_rec_perform_plot = sns.lineplot(x='Models', y='Recall', hue='k fold',marker='o', data=rec_perform_df)\n",
        "model_rec_perform_plot.set_title(\"Comparison of model's recall with 5 fold cross-validation\",size=25)\n",
        "for a,b in zip(rec_perform_df['Models'],rec_perform_df['Recall']):\n",
        "  if b not in [nb_rec,xgb_rec,cbc_rec ,lgbm_rec]:\n",
        "    plt.text(a, b-0.02, '%.4f' % b, ha='center', va= 'bottom',fontsize=12)\n",
        "  else:\n",
        "    plt.text(a, b+0.01, '%.4f' % b, ha='center', va= 'bottom',fontsize=12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "2oggs7q_yYrx",
        "outputId": "ac76831c-575b-4cd1-bb75-2269d32c93ba"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}