Pet_Adoption_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Pet Adoption Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLUNkXTOK-0U"
      },
      "source": [
        "# data analysis and wrangling\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy.stats import uniform\n",
        "from scipy.stats import randint\n",
        "\n",
        "# visualization\n",
        "%matplotlib notebook\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# machine learning\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import f1_score\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, cross_val_score"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fa5MMJH-LhnT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "9b33fa5e-fc3d-4dd9-e00c-bd115e1006b0"
      },
      "source": [
        "train_df = pd.read_csv('/content/drive/My Drive/Pet Adoption Dataset/train.csv')\n",
        "train_df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pet_id</th>\n",
              "      <th>issue_date</th>\n",
              "      <th>listing_date</th>\n",
              "      <th>condition</th>\n",
              "      <th>color_type</th>\n",
              "      <th>length(m)</th>\n",
              "      <th>height(cm)</th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>breed_category</th>\n",
              "      <th>pet_category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ANSL_69903</td>\n",
              "      <td>2016-07-10 00:00:00</td>\n",
              "      <td>2016-09-21 16:25:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Brown Tabby</td>\n",
              "      <td>0.80</td>\n",
              "      <td>7.78</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ANSL_66892</td>\n",
              "      <td>2013-11-21 00:00:00</td>\n",
              "      <td>2018-12-27 17:47:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>White</td>\n",
              "      <td>0.72</td>\n",
              "      <td>14.19</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ANSL_69750</td>\n",
              "      <td>2014-09-28 00:00:00</td>\n",
              "      <td>2016-10-19 08:24:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brown</td>\n",
              "      <td>0.15</td>\n",
              "      <td>40.90</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ANSL_71623</td>\n",
              "      <td>2016-12-31 00:00:00</td>\n",
              "      <td>2019-01-25 18:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>White</td>\n",
              "      <td>0.62</td>\n",
              "      <td>17.82</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ANSL_57969</td>\n",
              "      <td>2017-09-28 00:00:00</td>\n",
              "      <td>2017-11-19 09:38:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Black</td>\n",
              "      <td>0.50</td>\n",
              "      <td>11.06</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       pet_id           issue_date  ... breed_category  pet_category\n",
              "0  ANSL_69903  2016-07-10 00:00:00  ...            0.0             1\n",
              "1  ANSL_66892  2013-11-21 00:00:00  ...            0.0             2\n",
              "2  ANSL_69750  2014-09-28 00:00:00  ...            2.0             4\n",
              "3  ANSL_71623  2016-12-31 00:00:00  ...            0.0             2\n",
              "4  ANSL_57969  2017-09-28 00:00:00  ...            0.0             1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I1IO9aNaL2L8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "2de46ada-ad2b-4c15-d3da-d84110cdaac5"
      },
      "source": [
        "test_df = pd.read_csv('/content/drive/My Drive/Pet Adoption Dataset/test.csv')\n",
        "test_df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pet_id</th>\n",
              "      <th>issue_date</th>\n",
              "      <th>listing_date</th>\n",
              "      <th>condition</th>\n",
              "      <th>color_type</th>\n",
              "      <th>length(m)</th>\n",
              "      <th>height(cm)</th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ANSL_75005</td>\n",
              "      <td>2005-08-17 00:00:00</td>\n",
              "      <td>2017-09-07 15:35:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Black</td>\n",
              "      <td>0.87</td>\n",
              "      <td>42.73</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ANSL_76663</td>\n",
              "      <td>2018-11-15 00:00:00</td>\n",
              "      <td>2019-05-08 17:24:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Orange Tabby</td>\n",
              "      <td>0.06</td>\n",
              "      <td>6.71</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ANSL_58259</td>\n",
              "      <td>2012-10-11 00:00:00</td>\n",
              "      <td>2018-04-02 16:51:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Black</td>\n",
              "      <td>0.24</td>\n",
              "      <td>41.21</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ANSL_67171</td>\n",
              "      <td>2015-02-13 00:00:00</td>\n",
              "      <td>2018-04-06 07:25:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Black</td>\n",
              "      <td>0.29</td>\n",
              "      <td>8.46</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ANSL_72871</td>\n",
              "      <td>2017-01-18 00:00:00</td>\n",
              "      <td>2018-04-26 13:42:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Brown</td>\n",
              "      <td>0.71</td>\n",
              "      <td>30.92</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       pet_id           issue_date         listing_date  ...  height(cm) X1  X2\n",
              "0  ANSL_75005  2005-08-17 00:00:00  2017-09-07 15:35:00  ...       42.73  0   7\n",
              "1  ANSL_76663  2018-11-15 00:00:00  2019-05-08 17:24:00  ...        6.71  0   1\n",
              "2  ANSL_58259  2012-10-11 00:00:00  2018-04-02 16:51:00  ...       41.21  0   7\n",
              "3  ANSL_67171  2015-02-13 00:00:00  2018-04-06 07:25:00  ...        8.46  7   1\n",
              "4  ANSL_72871  2017-01-18 00:00:00  2018-04-26 13:42:00  ...       30.92  0   7\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBlvKPmpMQL-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29c8c0fb-a882-43f5-8cf1-5f12fe670deb"
      },
      "source": [
        "train_df.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 18834 entries, 0 to 18833\n",
            "Data columns (total 11 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   pet_id          18834 non-null  object \n",
            " 1   issue_date      18834 non-null  object \n",
            " 2   listing_date    18834 non-null  object \n",
            " 3   condition       17357 non-null  float64\n",
            " 4   color_type      18834 non-null  object \n",
            " 5   length(m)       18834 non-null  float64\n",
            " 6   height(cm)      18834 non-null  float64\n",
            " 7   X1              18834 non-null  int64  \n",
            " 8   X2              18834 non-null  int64  \n",
            " 9   breed_category  18834 non-null  float64\n",
            " 10  pet_category    18834 non-null  int64  \n",
            "dtypes: float64(4), int64(3), object(4)\n",
            "memory usage: 1.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wb6UvZ5EPOcW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "b756f72b-8de4-4cc8-8577-e705ddb9c8bb"
      },
      "source": [
        "train_df.describe()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>condition</th>\n",
              "      <th>length(m)</th>\n",
              "      <th>height(cm)</th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>breed_category</th>\n",
              "      <th>pet_category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17357.000000</td>\n",
              "      <td>18834.000000</td>\n",
              "      <td>18834.000000</td>\n",
              "      <td>18834.000000</td>\n",
              "      <td>18834.000000</td>\n",
              "      <td>18834.000000</td>\n",
              "      <td>18834.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.883390</td>\n",
              "      <td>0.502636</td>\n",
              "      <td>27.448832</td>\n",
              "      <td>5.369598</td>\n",
              "      <td>4.577307</td>\n",
              "      <td>0.600563</td>\n",
              "      <td>1.709143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.770434</td>\n",
              "      <td>0.288705</td>\n",
              "      <td>13.019781</td>\n",
              "      <td>6.572366</td>\n",
              "      <td>3.517763</td>\n",
              "      <td>0.629883</td>\n",
              "      <td>0.717919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>16.172500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>27.340000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.760000</td>\n",
              "      <td>38.890000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          condition     length(m)  ...  breed_category  pet_category\n",
              "count  17357.000000  18834.000000  ...    18834.000000  18834.000000\n",
              "mean       0.883390      0.502636  ...        0.600563      1.709143\n",
              "std        0.770434      0.288705  ...        0.629883      0.717919\n",
              "min        0.000000      0.000000  ...        0.000000      0.000000\n",
              "25%        0.000000      0.250000  ...        0.000000      1.000000\n",
              "50%        1.000000      0.500000  ...        1.000000      2.000000\n",
              "75%        1.000000      0.760000  ...        1.000000      2.000000\n",
              "max        2.000000      1.000000  ...        2.000000      4.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7qbE6YnY3NI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "cefa5fa6-a0f8-4956-809d-8fe47e8e5b6b"
      },
      "source": [
        "mean_length=round(train_df['length(m)'].mean(),2)\n",
        "data = [train_df, test_df]\n",
        "for dataset in data:\n",
        "    dataset['issue_date']=pd.to_datetime(dataset['issue_date'])\n",
        "    dataset['listing_date']=pd.to_datetime(dataset['listing_date'])\n",
        "\n",
        "    dataset['date_diff']=(dataset['listing_date'] - dataset['issue_date']).dt.days\n",
        "    dataset['issue_day']=pd.to_datetime(dataset['issue_date']).dt.day\n",
        "    dataset['issue_month']=pd.to_datetime(dataset['issue_date']).dt.month\n",
        "    dataset['issue_year']=pd.to_datetime(dataset['issue_date']).dt.year\n",
        "    dataset['listing_day']=pd.to_datetime(dataset['listing_date']).dt.day\n",
        "    dataset['listing_month']=pd.to_datetime(dataset['listing_date']).dt.month\n",
        "    dataset['listing_year']=pd.to_datetime(dataset['listing_date']).dt.year\n",
        "\n",
        "    dataset['listing_hour']=pd.to_datetime(dataset['listing_date']).dt.hour\n",
        "    # dataset['listing_minute']=pd.to_datetime(dataset['listing_date']).dt.minute\n",
        "\n",
        "    # dataset['date_diff']=pd.to_datetime(dataset['listing_date']).dt.date - pd.to_datetime(dataset['issue_date']).dt.date\n",
        "    # dataset['date_diff'] = dataset['date_diff']/np.timedelta64(1, 'D')\n",
        "    # dataset['date_diff'] = dataset['date_diff'].astype(int)\n",
        "\n",
        "    dataset['condition'].fillna(value=3, inplace=True)\n",
        "    dataset['condition'] = dataset['condition'].astype(int)\n",
        "\n",
        "    dataset['length(m)'].replace(0,mean_length, inplace=True)\n",
        "    dataset['height(m)']=dataset['height(cm)']/100\n",
        "    dataset['height_length_ratio']=dataset['height(m)']/dataset['length(m)']\n",
        "    dataset['X1_X2_ratio']=dataset['X1']/dataset['X2']\n",
        "\n",
        "    dataset.drop(columns=['issue_date','listing_date','height(cm)'], inplace=True)\n",
        "\n",
        "train_df['breed_category'] = train_df['breed_category'].astype(int)\n",
        "\n",
        "train_df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pet_id</th>\n",
              "      <th>condition</th>\n",
              "      <th>color_type</th>\n",
              "      <th>length(m)</th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>breed_category</th>\n",
              "      <th>pet_category</th>\n",
              "      <th>date_diff</th>\n",
              "      <th>issue_day</th>\n",
              "      <th>issue_month</th>\n",
              "      <th>issue_year</th>\n",
              "      <th>listing_day</th>\n",
              "      <th>listing_month</th>\n",
              "      <th>listing_year</th>\n",
              "      <th>listing_hour</th>\n",
              "      <th>height(m)</th>\n",
              "      <th>height_length_ratio</th>\n",
              "      <th>X1_X2_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ANSL_69903</td>\n",
              "      <td>2</td>\n",
              "      <td>Brown Tabby</td>\n",
              "      <td>0.80</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>73</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>2016</td>\n",
              "      <td>21</td>\n",
              "      <td>9</td>\n",
              "      <td>2016</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0778</td>\n",
              "      <td>0.097250</td>\n",
              "      <td>1.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ANSL_66892</td>\n",
              "      <td>1</td>\n",
              "      <td>White</td>\n",
              "      <td>0.72</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1862</td>\n",
              "      <td>21</td>\n",
              "      <td>11</td>\n",
              "      <td>2013</td>\n",
              "      <td>27</td>\n",
              "      <td>12</td>\n",
              "      <td>2018</td>\n",
              "      <td>17</td>\n",
              "      <td>0.1419</td>\n",
              "      <td>0.197083</td>\n",
              "      <td>1.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ANSL_69750</td>\n",
              "      <td>3</td>\n",
              "      <td>Brown</td>\n",
              "      <td>0.15</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>752</td>\n",
              "      <td>28</td>\n",
              "      <td>9</td>\n",
              "      <td>2014</td>\n",
              "      <td>19</td>\n",
              "      <td>10</td>\n",
              "      <td>2016</td>\n",
              "      <td>8</td>\n",
              "      <td>0.4090</td>\n",
              "      <td>2.726667</td>\n",
              "      <td>3.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ANSL_71623</td>\n",
              "      <td>1</td>\n",
              "      <td>White</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>755</td>\n",
              "      <td>31</td>\n",
              "      <td>12</td>\n",
              "      <td>2016</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>18</td>\n",
              "      <td>0.1782</td>\n",
              "      <td>0.287419</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ANSL_57969</td>\n",
              "      <td>2</td>\n",
              "      <td>Black</td>\n",
              "      <td>0.50</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>52</td>\n",
              "      <td>28</td>\n",
              "      <td>9</td>\n",
              "      <td>2017</td>\n",
              "      <td>19</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "      <td>9</td>\n",
              "      <td>0.1106</td>\n",
              "      <td>0.221200</td>\n",
              "      <td>4.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       pet_id  condition  ... height_length_ratio  X1_X2_ratio\n",
              "0  ANSL_69903          2  ...            0.097250     1.444444\n",
              "1  ANSL_66892          1  ...            0.197083     1.444444\n",
              "2  ANSL_69750          3  ...            2.726667     3.750000\n",
              "3  ANSL_71623          1  ...            0.287419     0.000000\n",
              "4  ANSL_57969          2  ...            0.221200     4.500000\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3P5CquAmooI4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "b8970c32-ef9c-456e-d39f-a11cf1f52898"
      },
      "source": [
        "test_df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pet_id</th>\n",
              "      <th>condition</th>\n",
              "      <th>color_type</th>\n",
              "      <th>length(m)</th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>date_diff</th>\n",
              "      <th>issue_day</th>\n",
              "      <th>issue_month</th>\n",
              "      <th>issue_year</th>\n",
              "      <th>listing_day</th>\n",
              "      <th>listing_month</th>\n",
              "      <th>listing_year</th>\n",
              "      <th>listing_hour</th>\n",
              "      <th>height(m)</th>\n",
              "      <th>height_length_ratio</th>\n",
              "      <th>X1_X2_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ANSL_75005</td>\n",
              "      <td>0</td>\n",
              "      <td>Black</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>4404</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>2005</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>2017</td>\n",
              "      <td>15</td>\n",
              "      <td>0.4273</td>\n",
              "      <td>0.491149</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ANSL_76663</td>\n",
              "      <td>1</td>\n",
              "      <td>Orange Tabby</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>174</td>\n",
              "      <td>15</td>\n",
              "      <td>11</td>\n",
              "      <td>2018</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2019</td>\n",
              "      <td>17</td>\n",
              "      <td>0.0671</td>\n",
              "      <td>1.118333</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ANSL_58259</td>\n",
              "      <td>1</td>\n",
              "      <td>Black</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1999</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>2012</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2018</td>\n",
              "      <td>16</td>\n",
              "      <td>0.4121</td>\n",
              "      <td>1.717083</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ANSL_67171</td>\n",
              "      <td>1</td>\n",
              "      <td>Black</td>\n",
              "      <td>0.29</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1148</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>2015</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2018</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0846</td>\n",
              "      <td>0.291724</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ANSL_72871</td>\n",
              "      <td>1</td>\n",
              "      <td>Brown</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>463</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "      <td>2018</td>\n",
              "      <td>13</td>\n",
              "      <td>0.3092</td>\n",
              "      <td>0.435493</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       pet_id  condition  ... height_length_ratio  X1_X2_ratio\n",
              "0  ANSL_75005          0  ...            0.491149          0.0\n",
              "1  ANSL_76663          1  ...            1.118333          0.0\n",
              "2  ANSL_58259          1  ...            1.717083          0.0\n",
              "3  ANSL_67171          1  ...            0.291724          7.0\n",
              "4  ANSL_72871          1  ...            0.435493          0.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Df5JsoCRmiBE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "4049d581-2af8-4050-a861-8617e6172d14"
      },
      "source": [
        "train_df = pd.concat([train_df,pd.get_dummies(train_df['color_type'], prefix='color')],axis=1)\n",
        "train_df.drop('color_type', axis = 1, inplace=True)\n",
        "train_df.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pet_id</th>\n",
              "      <th>condition</th>\n",
              "      <th>length(m)</th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>breed_category</th>\n",
              "      <th>pet_category</th>\n",
              "      <th>date_diff</th>\n",
              "      <th>issue_day</th>\n",
              "      <th>issue_month</th>\n",
              "      <th>issue_year</th>\n",
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              "      <th>listing_month</th>\n",
              "      <th>listing_year</th>\n",
              "      <th>listing_hour</th>\n",
              "      <th>height(m)</th>\n",
              "      <th>height_length_ratio</th>\n",
              "      <th>X1_X2_ratio</th>\n",
              "      <th>color_Agouti</th>\n",
              "      <th>color_Apricot</th>\n",
              "      <th>color_Black</th>\n",
              "      <th>color_Black Brindle</th>\n",
              "      <th>color_Black Smoke</th>\n",
              "      <th>color_Black Tabby</th>\n",
              "      <th>color_Black Tiger</th>\n",
              "      <th>color_Blue</th>\n",
              "      <th>color_Blue Cream</th>\n",
              "      <th>color_Blue Merle</th>\n",
              "      <th>color_Blue Point</th>\n",
              "      <th>color_Blue Smoke</th>\n",
              "      <th>color_Blue Tabby</th>\n",
              "      <th>color_Blue Tick</th>\n",
              "      <th>color_Blue Tiger</th>\n",
              "      <th>color_Brown</th>\n",
              "      <th>color_Brown Brindle</th>\n",
              "      <th>color_Brown Merle</th>\n",
              "      <th>color_Brown Tabby</th>\n",
              "      <th>color_Brown Tiger</th>\n",
              "      <th>color_Buff</th>\n",
              "      <th>color_Calico</th>\n",
              "      <th>color_Calico Point</th>\n",
              "      <th>color_Chocolate</th>\n",
              "      <th>color_Chocolate Point</th>\n",
              "      <th>color_Cream</th>\n",
              "      <th>color_Cream Tabby</th>\n",
              "      <th>color_Fawn</th>\n",
              "      <th>color_Flame Point</th>\n",
              "      <th>color_Gold</th>\n",
              "      <th>color_Gray</th>\n",
              "      <th>color_Gray Tabby</th>\n",
              "      <th>color_Green</th>\n",
              "      <th>color_Lilac Point</th>\n",
              "      <th>color_Liver</th>\n",
              "      <th>color_Liver Tick</th>\n",
              "      <th>color_Lynx Point</th>\n",
              "      <th>color_Orange</th>\n",
              "      <th>color_Orange Tabby</th>\n",
              "      <th>color_Pink</th>\n",
              "      <th>color_Red</th>\n",
              "      <th>color_Red Merle</th>\n",
              "      <th>color_Red Tick</th>\n",
              "      <th>color_Sable</th>\n",
              "      <th>color_Seal Point</th>\n",
              "      <th>color_Silver</th>\n",
              "      <th>color_Silver Lynx Point</th>\n",
              "      <th>color_Silver Tabby</th>\n",
              "      <th>color_Tan</th>\n",
              "      <th>color_Torbie</th>\n",
              "      <th>color_Tortie</th>\n",
              "      <th>color_Tortie Point</th>\n",
              "      <th>color_Tricolor</th>\n",
              "      <th>color_White</th>\n",
              "      <th>color_Yellow</th>\n",
              "      <th>color_Yellow Brindle</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ANSL_69903</td>\n",
              "      <td>2</td>\n",
              "      <td>0.80</td>\n",
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              "      <td>9</td>\n",
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              "      <td>1</td>\n",
              "      <td>73</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>2016</td>\n",
              "      <td>21</td>\n",
              "      <td>9</td>\n",
              "      <td>2016</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0778</td>\n",
              "      <td>0.097250</td>\n",
              "      <td>1.444444</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ANSL_66892</td>\n",
              "      <td>1</td>\n",
              "      <td>0.72</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1862</td>\n",
              "      <td>21</td>\n",
              "      <td>11</td>\n",
              "      <td>2013</td>\n",
              "      <td>27</td>\n",
              "      <td>12</td>\n",
              "      <td>2018</td>\n",
              "      <td>17</td>\n",
              "      <td>0.1419</td>\n",
              "      <td>0.197083</td>\n",
              "      <td>1.444444</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ANSL_69750</td>\n",
              "      <td>3</td>\n",
              "      <td>0.15</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>752</td>\n",
              "      <td>28</td>\n",
              "      <td>9</td>\n",
              "      <td>2014</td>\n",
              "      <td>19</td>\n",
              "      <td>10</td>\n",
              "      <td>2016</td>\n",
              "      <td>8</td>\n",
              "      <td>0.4090</td>\n",
              "      <td>2.726667</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ANSL_71623</td>\n",
              "      <td>1</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>755</td>\n",
              "      <td>31</td>\n",
              "      <td>12</td>\n",
              "      <td>2016</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>18</td>\n",
              "      <td>0.1782</td>\n",
              "      <td>0.287419</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ANSL_57969</td>\n",
              "      <td>2</td>\n",
              "      <td>0.50</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>52</td>\n",
              "      <td>28</td>\n",
              "      <td>9</td>\n",
              "      <td>2017</td>\n",
              "      <td>19</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "      <td>9</td>\n",
              "      <td>0.1106</td>\n",
              "      <td>0.221200</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       pet_id  condition  ...  color_Yellow  color_Yellow Brindle\n",
              "0  ANSL_69903          2  ...             0                     0\n",
              "1  ANSL_66892          1  ...             0                     0\n",
              "2  ANSL_69750          3  ...             0                     0\n",
              "3  ANSL_71623          1  ...             0                     0\n",
              "4  ANSL_57969          2  ...             0                     0\n",
              "\n",
              "[5 rows x 74 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_mZKogRnPIE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "341716f2-7a33-4f2b-c0e2-33d74ef61bb6"
      },
      "source": [
        "test_df = pd.concat([test_df,pd.get_dummies(test_df['color_type'], prefix='color')],axis=1)\n",
        "test_df.drop('color_type', axis = 1, inplace=True)\n",
        "test_df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pet_id</th>\n",
              "      <th>condition</th>\n",
              "      <th>length(m)</th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>date_diff</th>\n",
              "      <th>issue_day</th>\n",
              "      <th>issue_month</th>\n",
              "      <th>issue_year</th>\n",
              "      <th>listing_day</th>\n",
              "      <th>listing_month</th>\n",
              "      <th>listing_year</th>\n",
              "      <th>listing_hour</th>\n",
              "      <th>height(m)</th>\n",
              "      <th>height_length_ratio</th>\n",
              "      <th>X1_X2_ratio</th>\n",
              "      <th>color_Agouti</th>\n",
              "      <th>color_Apricot</th>\n",
              "      <th>color_Black</th>\n",
              "      <th>color_Black Brindle</th>\n",
              "      <th>color_Black Smoke</th>\n",
              "      <th>color_Black Tabby</th>\n",
              "      <th>color_Blue</th>\n",
              "      <th>color_Blue Cream</th>\n",
              "      <th>color_Blue Merle</th>\n",
              "      <th>color_Blue Point</th>\n",
              "      <th>color_Blue Smoke</th>\n",
              "      <th>color_Blue Tabby</th>\n",
              "      <th>color_Blue Tick</th>\n",
              "      <th>color_Blue Tiger</th>\n",
              "      <th>color_Brown</th>\n",
              "      <th>color_Brown Brindle</th>\n",
              "      <th>color_Brown Merle</th>\n",
              "      <th>color_Brown Tabby</th>\n",
              "      <th>color_Buff</th>\n",
              "      <th>color_Calico</th>\n",
              "      <th>color_Calico Point</th>\n",
              "      <th>color_Chocolate</th>\n",
              "      <th>color_Chocolate Point</th>\n",
              "      <th>color_Cream</th>\n",
              "      <th>color_Cream Tabby</th>\n",
              "      <th>color_Fawn</th>\n",
              "      <th>color_Flame Point</th>\n",
              "      <th>color_Gold</th>\n",
              "      <th>color_Gray</th>\n",
              "      <th>color_Gray Tabby</th>\n",
              "      <th>color_Green</th>\n",
              "      <th>color_Lilac Point</th>\n",
              "      <th>color_Liver</th>\n",
              "      <th>color_Liver Tick</th>\n",
              "      <th>color_Lynx Point</th>\n",
              "      <th>color_Orange</th>\n",
              "      <th>color_Orange Tabby</th>\n",
              "      <th>color_Pink</th>\n",
              "      <th>color_Red</th>\n",
              "      <th>color_Red Merle</th>\n",
              "      <th>color_Red Tick</th>\n",
              "      <th>color_Sable</th>\n",
              "      <th>color_Seal Point</th>\n",
              "      <th>color_Silver</th>\n",
              "      <th>color_Silver Lynx Point</th>\n",
              "      <th>color_Silver Tabby</th>\n",
              "      <th>color_Tan</th>\n",
              "      <th>color_Torbie</th>\n",
              "      <th>color_Tortie</th>\n",
              "      <th>color_Tortie Point</th>\n",
              "      <th>color_Tricolor</th>\n",
              "      <th>color_White</th>\n",
              "      <th>color_Yellow</th>\n",
              "      <th>color_Yellow Brindle</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ANSL_75005</td>\n",
              "      <td>0</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>4404</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>2005</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>2017</td>\n",
              "      <td>15</td>\n",
              "      <td>0.4273</td>\n",
              "      <td>0.491149</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ANSL_76663</td>\n",
              "      <td>1</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>174</td>\n",
              "      <td>15</td>\n",
              "      <td>11</td>\n",
              "      <td>2018</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2019</td>\n",
              "      <td>17</td>\n",
              "      <td>0.0671</td>\n",
              "      <td>1.118333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ANSL_58259</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1999</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>2012</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2018</td>\n",
              "      <td>16</td>\n",
              "      <td>0.4121</td>\n",
              "      <td>1.717083</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ANSL_67171</td>\n",
              "      <td>1</td>\n",
              "      <td>0.29</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1148</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>2015</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2018</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0846</td>\n",
              "      <td>0.291724</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ANSL_72871</td>\n",
              "      <td>1</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>463</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "      <td>2018</td>\n",
              "      <td>13</td>\n",
              "      <td>0.3092</td>\n",
              "      <td>0.435493</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       pet_id  condition  ...  color_Yellow  color_Yellow Brindle\n",
              "0  ANSL_75005          0  ...             0                     0\n",
              "1  ANSL_76663          1  ...             0                     0\n",
              "2  ANSL_58259          1  ...             0                     0\n",
              "3  ANSL_67171          1  ...             0                     0\n",
              "4  ANSL_72871          1  ...             0                     0\n",
              "\n",
              "[5 rows x 70 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMcqv4QooEpp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fd62f23-3e1a-44f6-fc07-34172d9cfe8a"
      },
      "source": [
        "train_cols = train_df.columns\n",
        "test_cols = test_df.columns\n",
        "\n",
        "not_in_test = train_cols.difference(test_cols)\n",
        "not_in_test"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['breed_category', 'color_Black Tiger', 'color_Brown Tiger',\n",
              "       'pet_category'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rz_c-4qQiTpt"
      },
      "source": [
        "train_df.drop(columns=['color_Black Tiger', 'color_Brown Tiger'], inplace=True)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XHSCNaIPxGb"
      },
      "source": [
        "# train_df.groupby(['condition', 'pet_category'], as_index=False).size()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEiV9qJ0RrYg"
      },
      "source": [
        "# %matplotlib inline\n",
        "# train_df.plot.scatter(x='condition',\n",
        "#                       y='pet_category',\n",
        "#                       c='DarkBlue')"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "df3lODwKimJn"
      },
      "source": [
        "# train_features = ['height(cm)',\t'X1',\t'X2','date_diff']\n",
        "# scaler = StandardScaler()\n",
        "train_df_ss=train_df.copy()\n",
        "# train_df_ss[train_features] = scaler.fit_transform(train_df_ss[train_features])\n",
        "# train_df_ss.head(10)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Gw_pVpTo6f8"
      },
      "source": [
        "test_df_ss=test_df.copy()\n",
        "# test_df_ss[train_features] = scaler.transform(test_df_ss[train_features])\n",
        "# test_df_ss.head(10)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1AgMhj8pjO6"
      },
      "source": [
        "X_train = train_df_ss.drop(columns = ['pet_id','breed_category','pet_category'], inplace= False)\n",
        "Y1_train = train_df_ss['breed_category']\n",
        "Y2_train = train_df_ss['pet_category']\n",
        "X_test = test_df_ss.drop(columns = ['pet_id'], inplace= False)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PRH0a6HtAw5E"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_7N7QK7qef1"
      },
      "source": [
        "# Random Forest\n",
        "\n",
        "# random_forest1 = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)\n",
        "# random_forest1.fit(X_train, Y1_train)\n",
        "# Y1_pred = random_forest1.predict(X_test)\n",
        "# acc_random_forest1 = round(random_forest1.score(X_train, Y1_train) * 100, 2)\n",
        "# acc_random_forest1"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "supRKAHYDckQ"
      },
      "source": [
        "# Random Forest\n",
        "\n",
        "# random_forest2 = RandomForestClassifier(n_estimators=100, max_depth=7, random_state=1)\n",
        "# random_forest2.fit(X_train, Y2_train)\n",
        "# Y2_pred = random_forest2.predict(X_test)\n",
        "# acc_random_forest2 = round(random_forest2.score(X_train, Y2_train) * 100, 2)\n",
        "# acc_random_forest2"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMFiMsYDDmZr"
      },
      "source": [
        "# submission = pd.DataFrame({'pet_id': test_df['pet_id'],'breed_category': Y1_pred,'pet_category': Y2_pred})\n",
        "# submission.to_csv('random_forest_submission.csv', index=False)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1absMTzA464"
      },
      "source": [
        "### SVC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9ua-irEHiLz"
      },
      "source": [
        "# Support Vector Machines\n",
        "\n",
        "# svc1 = SVC()\n",
        "# svc1.fit(X_train, Y1_train)\n",
        "# Y1_pred = svc1.predict(X_test)\n",
        "# acc_svc1 = round(svc1.score(X_train, Y1_train) * 100, 2)\n",
        "# acc_svc1"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4yRKK5cJJvS"
      },
      "source": [
        "# Support Vector Machines\n",
        "\n",
        "# svc2 = SVC()\n",
        "# svc2.fit(X_train, Y2_train)\n",
        "# Y2_pred = svc2.predict(X_test)\n",
        "# acc_svc2 = round(svc2.score(X_train, Y2_train) * 100, 2)\n",
        "# acc_svc2"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3oiQuNIJVOY"
      },
      "source": [
        "# submission = pd.DataFrame({'pet_id': test_df['pet_id'],'breed_category': Y1_pred,'pet_category': Y2_pred})\n",
        "# submission.to_csv('svc_submission.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ec1GfRgLhTBv"
      },
      "source": [
        "# param_grid = {'C': [0.1, 1, 10],  \n",
        "#               'gamma': [1, 0.1, 0.01], \n",
        "#               'kernel': ['rbf','poly']}  \n",
        "# svc = SVC()\n",
        "# clf = GridSearchCV(estimator=svc, param_grid=param_grid, n_jobs=-1)\n",
        "# clf.fit(X_train, Y2_train)\n",
        "# clf.best_params_, clf.best_score_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qNueOQSrA_FX"
      },
      "source": [
        "### XGB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6x2thgA8DYMq"
      },
      "source": [
        "# param_grid = {# 'n_estimators': [300,500,800,1000],\n",
        "#               'learning_rate': [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ],\n",
        "#               # 'subsample': [0.3,0.6,0.9],\n",
        "#               'max_depth': [3, 4, 5, 6, 7, 8, 9],\n",
        "#               # 'colsample_bytree': [0.5,0.7,0.9],\n",
        "#               # 'min_child_weight': [1, 2, 3, 4]\n",
        "#               }\n",
        "# xgb = XGBClassifier()\n",
        "# clf = RandomizedSearchCV(xgb, param_grid, n_jobs=-1, random_state=42)\n",
        "# clf.fit(X_train, Y1_train)\n",
        "# clf.best_params_, clf.best_score_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vftslIsAFgWJ"
      },
      "source": [
        "# param_grid = {'n_estimators': [300,500,800,1000],\n",
        "#               'learning_rate': [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ],\n",
        "#               'subsample': [0.3,0.6,0.9],\n",
        "#               'max_depth': [3, 4, 5, 6, 7, 8, 9],\n",
        "#               'colsample_bytree': [0.5,0.7,0.9],\n",
        "#               'min_child_weight': [1, 2, 3, 4]\n",
        "#               }\n",
        "# xgb = XGBClassifier()\n",
        "# clf = RandomizedSearchCV(xgb, param_grid, n_jobs=-1, random_state=42)\n",
        "# clf.fit(X_train, Y2_train)\n",
        "# clf.best_params_, clf.best_score_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIjMmqRepo2y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0eacb8ee-0be3-4144-c767-6414104ed0a5"
      },
      "source": [
        "# XGBoost # colsample_bytree= 0.9,learning_rate= 0.25,max_depth= 3,min_child_weight= 2,n_estimators= 300,subsample= 0.9\n",
        "\n",
        "xgb1 = XGBClassifier(learning_rate= 0.1, max_depth= 3, n_estimators= 300)\n",
        "xgb1.fit(X_train, Y1_train)\n",
        "Y1_pred = xgb1.predict(X_test)\n",
        "acc_xgb1 = round(xgb1.score(X_train, Y1_train) * 100, 2)\n",
        "acc_xgb1"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.97"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yf2Hr-pUqvPZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af8c0c2e-370c-4e81-f39b-6938870d5acf"
      },
      "source": [
        "# XGBoost # colsample_bytree= 0.9,learning_rate= 0.25,max_depth= 3,min_child_weight= 2,n_estimators= 300,subsample= 0.9\n",
        "\n",
        "xgb2 = XGBClassifier(learning_rate= 0.1, max_depth= 4, n_estimators= 300)\n",
        "xgb2.fit(X_train, Y2_train)\n",
        "Y2_pred = xgb2.predict(X_test)\n",
        "acc_xgb2 = round(xgb2.score(X_train, Y2_train) * 100, 2)\n",
        "acc_xgb2"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "91.81"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ClHrIWfqq55n"
      },
      "source": [
        "# submission = pd.DataFrame({'pet_id': test_df['pet_id'],'breed_category': Y1_pred,'pet_category': Y2_pred})\n",
        "# submission.to_csv('xgb_submission.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eh5c26k5gRRP"
      },
      "source": [
        "### LGBM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDEmq8V4gUTX"
      },
      "source": [
        "# param_grid ={'num_leaves': [2,5,10,15,30,45],\n",
        "#              'min_child_samples': [2,5,7,10,15], \n",
        "#              'min_child_weight': [1e-3, 1e-2, 1e-1, 1, 1e1, 1e2],\n",
        "#              'learning_rate': [0.001,0.01,0.1,1,10],\n",
        "#              'min_data_in_leaf': [5,10,20,30,40,50,60],\n",
        "#             #  'subsample': uniform(loc=0.2, scale=0.8), \n",
        "#             #  'colsample_bytree': uniform(loc=0.4, scale=0.6),\n",
        "#              'reg_alpha': [0.01,0.1,0,1,10],\n",
        "#              'reg_lambda': [0.01,0.1,0,1,10],\n",
        "#              'max_depth': [1,3,5,10,15,20,25,-1]\n",
        "#              }\n",
        "# lgbm = LGBMClassifier()\n",
        "# clf = RandomizedSearchCV(lgbm, param_grid, n_jobs=-1, random_state=42)\n",
        "# clf.fit(X_train, Y1_train)\n",
        "# clf.best_params_, clf.best_score_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAUYRuyBgUZf"
      },
      "source": [
        "# param_grid ={'num_leaves': [6,15,30,45,60,75,90],\n",
        "#              'min_child_samples': [2,5,7,10,15], \n",
        "#              'min_child_weight': [1e-3, 1e-2, 1e-1, 1, 1e1, 1e2],\n",
        "#              'learning_rate': [0.001,0.01,0.1,1,10],\n",
        "#              'min_data_in_leaf': [5,10,20,30,40],\n",
        "#             #  'subsample': uniform(loc=0.2, scale=0.8), \n",
        "#             #  'colsample_bytree': uniform(loc=0.4, scale=0.6),\n",
        "#              'reg_alpha': [0.01,0.1,0,1,10],\n",
        "#              'reg_lambda': [0.01,0.1,0,1,10],\n",
        "#              'max_depth': [1,3,5,10,15,20,-1]\n",
        "#              }\n",
        "# lgbm = LGBMClassifier()\n",
        "# clf = RandomizedSearchCV(lgbm, param_grid, n_jobs=-1, random_state=42)\n",
        "# clf.fit(X_train, Y2_train)\n",
        "# clf.best_params_, clf.best_score_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mf_6GUvZgUh2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb7014b4-240d-4525-8c20-809b2715bcc9"
      },
      "source": [
        "# LGBM # min_child_samples= 20, min_child_weight= 0.01, reg_alpha= 0.1, reg_lambda= 0.01\n",
        "# learning_rate= 0.1, max_depth= 6, min_data_in_leaf= 20, num_leaves= 45\n",
        "\n",
        "lgbm1 = LGBMClassifier(learning_rate= 0.1, max_depth= 6, min_data_in_leaf= 20, num_leaves= 45)\n",
        "lgbm1.fit(X_train, Y1_train)\n",
        "Y1_pred = lgbm1.predict(X_test)\n",
        "acc_lgbm1 = round(lgbm1.score(X_train, Y1_train) * 100, 2)\n",
        "acc_lgbm1"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.89"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Ll1Lk1NgVBy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e997305-4980-4687-92f1-ba26d4300a60"
      },
      "source": [
        "# LGBM # min_child_samples= 20, min_child_weight= 1, num_leaves= 80\n",
        "# learning_rate= 0.1, max_depth= 8, min_data_in_leaf= 20, num_leaves= 150\n",
        "\n",
        "lgbm2 = LGBMClassifier(learning_rate= 0.1, max_depth= 8, min_data_in_leaf= 20, num_leaves= 150)\n",
        "lgbm2.fit(X_train, Y2_train)\n",
        "Y2_pred = lgbm2.predict(X_test)\n",
        "acc_lgbm2 = round(lgbm2.score(X_train, Y2_train) * 100, 2)\n",
        "acc_lgbm2"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "92.42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "foQet-t3hETj"
      },
      "source": [
        "# submission = pd.DataFrame({'pet_id': test_df['pet_id'],'breed_category': Y1_pred,'pet_category': Y2_pred})\n",
        "# submission.to_csv('lgbm_submission.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XrhWPsjBLML"
      },
      "source": [
        "### KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GYk4cLjfdRb9"
      },
      "source": [
        "# KNN or k-Nearest Neighbors\n",
        "\n",
        "# knn1 = KNeighborsClassifier(n_neighbors=7)\n",
        "# knn1.fit(X_train, Y1_train)\n",
        "# Y1_pred = knn1.predict(X_test)\n",
        "# acc_knn1 = round(knn1.score(X_train, Y1_train) * 100, 2)\n",
        "# acc_knn1"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UvsYCe0zdRvr"
      },
      "source": [
        "# KNN or k-Nearest Neighbors\n",
        "\n",
        "# knn2 = KNeighborsClassifier(n_neighbors=7)\n",
        "# knn2.fit(X_train, Y2_train)\n",
        "# Y2_pred = knn2.predict(X_test)\n",
        "# acc_knn2 = round(knn2.score(X_train, Y2_train) * 100, 2)\n",
        "# acc_knn2"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykOsV6AxdR96"
      },
      "source": [
        "# submission = pd.DataFrame({'pet_id': test_df['pet_id'],'breed_category': Y1_pred,'pet_category': Y2_pred})\n",
        "# submission.to_csv('knn_submission.csv', index=False)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zglv3rIvOn39"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}