adopt a buddy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "%matplotlib inline \n",
    "from sklearn.preprocessing import LabelEncoder,OneHotEncoder\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from numpy import asarray\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "from sklearn.metrics import classification_report\n",
    "from lightgbm import LGBMClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>10-07-2016 00:00</td>\n",
       "      <td>21-09-2016 16:25</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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANSL_66892</td>\n",
       "      <td>21-11-2013 00:00</td>\n",
       "      <td>27-12-2018 17:47</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</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANSL_69750</td>\n",
       "      <td>28-09-2014 00:00</td>\n",
       "      <td>19-10-2016 08:24</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</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANSL_71623</td>\n",
       "      <td>31-12-2016 00:00</td>\n",
       "      <td>25-01-2019 18:30</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</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANSL_57969</td>\n",
       "      <td>28-09-2017 00:00</td>\n",
       "      <td>19-11-2017 09:38</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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pet_id        issue_date      listing_date  condition   color_type  \\\n",
       "0  ANSL_69903  10-07-2016 00:00  21-09-2016 16:25        2.0  Brown Tabby   \n",
       "1  ANSL_66892  21-11-2013 00:00  27-12-2018 17:47        1.0        White   \n",
       "2  ANSL_69750  28-09-2014 00:00  19-10-2016 08:24        NaN        Brown   \n",
       "3  ANSL_71623  31-12-2016 00:00  25-01-2019 18:30        1.0        White   \n",
       "4  ANSL_57969  28-09-2017 00:00  19-11-2017 09:38        2.0        Black   \n",
       "\n",
       "   length(m)  height(cm)  X1  X2  breed_category  pet_category  \n",
       "0       0.80        7.78  13   9               0             1  \n",
       "1       0.72       14.19  13   9               0             2  \n",
       "2       0.15       40.90  15   4               2             4  \n",
       "3       0.62       17.82   0   1               0             2  \n",
       "4       0.50       11.06  18   4               0             1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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  condition  \\\n",
       "0  ANSL_75005  2005-08-17 00:00:00  2017-09-07 15:35:00        0.0   \n",
       "1  ANSL_76663  2018-11-15 00:00:00  2019-05-08 17:24:00        1.0   \n",
       "2  ANSL_58259  2012-10-11 00:00:00  2018-04-02 16:51:00        1.0   \n",
       "3  ANSL_67171  2015-02-13 00:00:00  2018-04-06 07:25:00        1.0   \n",
       "4  ANSL_72871  2017-01-18 00:00:00  2018-04-26 13:42:00        1.0   \n",
       "\n",
       "     color_type  length(m)  height(cm)  X1  X2  \n",
       "0         Black       0.87       42.73   0   7  \n",
       "1  Orange Tabby       0.06        6.71   0   1  \n",
       "2         Black       0.24       41.21   0   7  \n",
       "3         Black       0.29        8.46   7   1  \n",
       "4         Brown       0.71       30.92   0   7  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    10621\n",
       "1     7184\n",
       "4      941\n",
       "0       88\n",
       "Name: pet_category, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['pet_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9000\n",
       "1    8357\n",
       "2    1477\n",
       "Name: breed_category, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['breed_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    6819\n",
       "0.0    6281\n",
       "2.0    4257\n",
       "Name: condition, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['condition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    2928\n",
       "0.0    2685\n",
       "2.0    1840\n",
       "Name: condition, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['condition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  1., nan,  0.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['condition'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pet_id               0\n",
       "issue_date           0\n",
       "listing_date         0\n",
       "condition         1477\n",
       "color_type           0\n",
       "length(m)            0\n",
       "height(cm)           0\n",
       "X1                   0\n",
       "X2                   0\n",
       "breed_category       0\n",
       "pet_category         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pet_id            0\n",
       "issue_date        0\n",
       "listing_date      0\n",
       "condition       619\n",
       "color_type        0\n",
       "length(m)         0\n",
       "height(cm)        0\n",
       "X1                0\n",
       "X2                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "breed_category  pet_category\n",
       "0               0                 30\n",
       "                1               3195\n",
       "                2               5692\n",
       "                4                 83\n",
       "1               0                  7\n",
       "                1               3406\n",
       "                2               4869\n",
       "                4                 75\n",
       "2               0                 51\n",
       "                1                583\n",
       "                2                 60\n",
       "                4                783\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(['breed_category', 'pet_category']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# date_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['issue_date'] = pd.to_datetime(train['issue_date'])\n",
    "train['listing_date'] = pd.to_datetime(train['listing_date'])\n",
    "train['duration'] = (train['listing_date'] - train['issue_date']).dt.days  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      16\n",
       "1    1862\n",
       "2     752\n",
       "3     755\n",
       "4      52\n",
       "Name: duration, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['duration'] = np.absolute(np.array(train.duration).flatten())\n",
    "train['duration'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['issue_date'] = pd.to_datetime(test['issue_date'])\n",
    "test['listing_date'] = pd.to_datetime(test['listing_date'])\n",
    "test['duration'] = (test['listing_date'] - test['issue_date']).dt.days  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['duration'] = np.absolute(np.array(test.duration).flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# length_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93\n",
      "44\n"
     ]
    }
   ],
   "source": [
    "print(len(train[train['length(m)'] == 0]))\n",
    "print(len(test[test['length(m)']==0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['length(cm)'] = train['length(m)'].apply(lambda x: x*100)\n",
    "test['length(cm)'] = test['length(m)'].apply(lambda x: x*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace all 0 length with mean of lengths\n",
    "val = train['length(cm)'].mean()\n",
    "train['length(cm)'] = train['length(cm)'].replace(to_replace=0, value=val)\n",
    "test['length(cm)'] = test['length(cm)'].replace(to_replace=0, value=val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new feature\n",
    "train['ratio_len_height'] = train['length(cm)']/train['height(cm)']\n",
    "test['ratio_len_height'] = test['length(cm)']/test['height(cm)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "length(cm)  pet_category\n",
       "1.0         1                65\n",
       "            2                90\n",
       "            4                10\n",
       "2.0         0                 3\n",
       "            1                67\n",
       "            2                99\n",
       "            4                 7\n",
       "3.0         1                64\n",
       "            2               100\n",
       "            4                12\n",
       "4.0         1                65\n",
       "            2               111\n",
       "            4                11\n",
       "5.0         1                58\n",
       "            2               103\n",
       "            4                10\n",
       "6.0         0                 2\n",
       "            1                74\n",
       "            2               115\n",
       "            4                 3\n",
       "7.0         1                76\n",
       "            2               106\n",
       "            4                 2\n",
       "8.0         0                 1\n",
       "            1                86\n",
       "            2               115\n",
       "            4                14\n",
       "9.0         1                74\n",
       "            2               101\n",
       "            4                 5\n",
       "                           ... \n",
       "93.0        1                76\n",
       "            2               101\n",
       "            4                15\n",
       "94.0        0                 1\n",
       "            1                71\n",
       "            2               118\n",
       "            4                 6\n",
       "95.0        0                 2\n",
       "            1                68\n",
       "            2               103\n",
       "            4                12\n",
       "96.0        0                 2\n",
       "            1                63\n",
       "            2               104\n",
       "            4                 8\n",
       "97.0        0                 1\n",
       "            1                78\n",
       "            2               113\n",
       "            4                12\n",
       "98.0        0                 1\n",
       "            1                72\n",
       "            2               113\n",
       "            4                11\n",
       "99.0        0                 1\n",
       "            1                69\n",
       "            2               113\n",
       "            4                13\n",
       "100.0       1                41\n",
       "            2                63\n",
       "            4                 3\n",
       "Length: 357, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(['length(cm)', 'pet_category']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# clubbing train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([train,test],axis=0,sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>duration</th>\n",
       "      <th>length(cm)</th>\n",
       "      <th>ratio_len_height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANSL_69903</td>\n",
       "      <td>2016-10-07</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>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.282776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANSL_66892</td>\n",
       "      <td>2013-11-21</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>2.0</td>\n",
       "      <td>1862</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.073996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANSL_69750</td>\n",
       "      <td>2014-09-28</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>4.0</td>\n",
       "      <td>752</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.366748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANSL_71623</td>\n",
       "      <td>2016-12-31</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>2.0</td>\n",
       "      <td>755</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3.479237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANSL_57969</td>\n",
       "      <td>2017-09-28</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>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.520796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ANSL_52200</td>\n",
       "      <td>2017-10-25</td>\n",
       "      <td>2019-10-03 13:18:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Red</td>\n",
       "      <td>0.92</td>\n",
       "      <td>29.56</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>708</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3.112314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ANSL_75444</td>\n",
       "      <td>2018-06-11</td>\n",
       "      <td>2019-09-04 16:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Brown Tabby</td>\n",
       "      <td>0.14</td>\n",
       "      <td>40.24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>450</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.347913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ANSL_52759</td>\n",
       "      <td>2018-05-06</td>\n",
       "      <td>2018-02-09 17:04:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>White</td>\n",
       "      <td>0.15</td>\n",
       "      <td>25.48</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>86</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.588697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ANSL_74632</td>\n",
       "      <td>2015-10-10</td>\n",
       "      <td>2016-04-08 16:29:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Brown</td>\n",
       "      <td>0.05</td>\n",
       "      <td>38.22</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>181</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.130822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ANSL_56464</td>\n",
       "      <td>2014-02-18</td>\n",
       "      <td>2017-03-17 11:38:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Brown</td>\n",
       "      <td>0.55</td>\n",
       "      <td>21.26</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1123</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2.587018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ANSL_69485</td>\n",
       "      <td>2004-11-17</td>\n",
       "      <td>2016-12-15 13:33:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Tricolor</td>\n",
       "      <td>0.30</td>\n",
       "      <td>23.06</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4411</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.300954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ANSL_51014</td>\n",
       "      <td>2017-06-17</td>\n",
       "      <td>2018-08-02 09:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Brown Tabby</td>\n",
       "      <td>0.44</td>\n",
       "      <td>40.32</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>411</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.091270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ANSL_76771</td>\n",
       "      <td>2017-07-02</td>\n",
       "      <td>2019-10-05 16:42:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.20</td>\n",
       "      <td>30.78</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>825</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.649773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ANSL_69252</td>\n",
       "      <td>2016-11-29</td>\n",
       "      <td>2017-05-31 00:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>White</td>\n",
       "      <td>0.45</td>\n",
       "      <td>5.39</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>183</td>\n",
       "      <td>45.0</td>\n",
       "      <td>8.348794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ANSL_58085</td>\n",
       "      <td>2018-01-29</td>\n",
       "      <td>2018-02-05 17:46:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Calico</td>\n",
       "      <td>0.48</td>\n",
       "      <td>30.54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.571709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ANSL_63686</td>\n",
       "      <td>2016-07-20</td>\n",
       "      <td>2016-10-10 12:14:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.18</td>\n",
       "      <td>49.05</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.366972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ANSL_66127</td>\n",
       "      <td>2016-03-20</td>\n",
       "      <td>2016-03-11 13:55:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Red</td>\n",
       "      <td>0.88</td>\n",
       "      <td>49.50</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ANSL_52366</td>\n",
       "      <td>2009-06-01</td>\n",
       "      <td>2019-01-27 11:56:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.93</td>\n",
       "      <td>14.83</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3527</td>\n",
       "      <td>93.0</td>\n",
       "      <td>6.271072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ANSL_55607</td>\n",
       "      <td>2013-11-28</td>\n",
       "      <td>2017-12-28 00:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Brown Brindle</td>\n",
       "      <td>0.76</td>\n",
       "      <td>38.99</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1491</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.949218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ANSL_72451</td>\n",
       "      <td>2018-08-23</td>\n",
       "      <td>2018-11-29 11:46:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.96</td>\n",
       "      <td>24.33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98</td>\n",
       "      <td>96.0</td>\n",
       "      <td>3.945746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ANSL_66055</td>\n",
       "      <td>2008-01-25</td>\n",
       "      <td>2016-08-15 11:27:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brown Tabby</td>\n",
       "      <td>0.66</td>\n",
       "      <td>36.01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3125</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.832824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ANSL_50488</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>2018-07-28 12:36:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown</td>\n",
       "      <td>0.45</td>\n",
       "      <td>14.00</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ANSL_68751</td>\n",
       "      <td>2018-03-14</td>\n",
       "      <td>2018-05-24 17:10:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>White</td>\n",
       "      <td>0.92</td>\n",
       "      <td>49.50</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>71</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.858586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ANSL_57387</td>\n",
       "      <td>2014-11-22</td>\n",
       "      <td>2017-10-04 16:55:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Blue</td>\n",
       "      <td>0.31</td>\n",
       "      <td>49.04</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1047</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.632137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ANSL_67782</td>\n",
       "      <td>2012-02-08</td>\n",
       "      <td>2019-07-01 00:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.51</td>\n",
       "      <td>45.53</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2700</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.120141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ANSL_76397</td>\n",
       "      <td>2018-11-04</td>\n",
       "      <td>2019-02-05 14:16:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.80</td>\n",
       "      <td>37.17</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>93</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.152273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ANSL_59210</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>2018-04-25 17:41:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Tortie</td>\n",
       "      <td>0.53</td>\n",
       "      <td>35.06</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>116</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.511694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ANSL_56018</td>\n",
       "      <td>2011-01-25</td>\n",
       "      <td>2017-10-03 12:21:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gray</td>\n",
       "      <td>0.32</td>\n",
       "      <td>48.92</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2443</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.654129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ANSL_53325</td>\n",
       "      <td>2015-01-09</td>\n",
       "      <td>2016-09-23 18:16:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Fawn</td>\n",
       "      <td>0.98</td>\n",
       "      <td>14.02</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>623</td>\n",
       "      <td>98.0</td>\n",
       "      <td>6.990014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ANSL_52456</td>\n",
       "      <td>2010-07-18</td>\n",
       "      <td>2018-02-18 14:46:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Tricolor</td>\n",
       "      <td>0.68</td>\n",
       "      <td>22.33</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2772</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3.045231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8042</th>\n",
       "      <td>ANSL_54807</td>\n",
       "      <td>2018-12-23</td>\n",
       "      <td>2019-03-18 14:55:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brown Brindle</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12.96</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>85.0</td>\n",
       "      <td>6.558642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8043</th>\n",
       "      <td>ANSL_51407</td>\n",
       "      <td>2009-08-01</td>\n",
       "      <td>2017-02-03 19:09:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tan</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.77</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2743</td>\n",
       "      <td>97.0</td>\n",
       "      <td>16.811092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8044</th>\n",
       "      <td>ANSL_68531</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>2018-04-03 13:32:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brown Brindle</td>\n",
       "      <td>0.82</td>\n",
       "      <td>17.72</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2979</td>\n",
       "      <td>82.0</td>\n",
       "      <td>4.627540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8045</th>\n",
       "      <td>ANSL_63603</td>\n",
       "      <td>2016-08-29</td>\n",
       "      <td>2017-09-20 15:11:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Blue</td>\n",
       "      <td>0.66</td>\n",
       "      <td>18.02</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>387</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3.662597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8046</th>\n",
       "      <td>ANSL_58426</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>2018-04-15 16:31:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Tan</td>\n",
       "      <td>0.68</td>\n",
       "      <td>35.33</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>389</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.924710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8047</th>\n",
       "      <td>ANSL_68768</td>\n",
       "      <td>2017-08-14</td>\n",
       "      <td>2017-11-19 19:52:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown Tabby</td>\n",
       "      <td>0.90</td>\n",
       "      <td>37.05</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2.429150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8048</th>\n",
       "      <td>ANSL_66647</td>\n",
       "      <td>2017-04-29</td>\n",
       "      <td>2018-05-19 11:17:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Fawn</td>\n",
       "      <td>0.63</td>\n",
       "      <td>41.40</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>385</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8049</th>\n",
       "      <td>ANSL_53916</td>\n",
       "      <td>2015-08-28</td>\n",
       "      <td>2017-09-26 13:41:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tan</td>\n",
       "      <td>0.38</td>\n",
       "      <td>32.44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>760</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.171393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8050</th>\n",
       "      <td>ANSL_62236</td>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>2017-09-28 14:28:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Brown</td>\n",
       "      <td>0.86</td>\n",
       "      <td>13.15</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "      <td>86.0</td>\n",
       "      <td>6.539924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8051</th>\n",
       "      <td>ANSL_71438</td>\n",
       "      <td>2018-08-15</td>\n",
       "      <td>2018-12-01 16:58:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Seal Point</td>\n",
       "      <td>0.81</td>\n",
       "      <td>19.02</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108</td>\n",
       "      <td>81.0</td>\n",
       "      <td>4.258675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8052</th>\n",
       "      <td>ANSL_70272</td>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>2016-07-31 16:38:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Chocolate</td>\n",
       "      <td>0.99</td>\n",
       "      <td>27.48</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>392</td>\n",
       "      <td>99.0</td>\n",
       "      <td>3.602620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8053</th>\n",
       "      <td>ANSL_52080</td>\n",
       "      <td>2016-11-05</td>\n",
       "      <td>2017-12-13 12:36:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.04</td>\n",
       "      <td>20.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>403</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.196367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8054</th>\n",
       "      <td>ANSL_51166</td>\n",
       "      <td>2005-12-14</td>\n",
       "      <td>2017-04-16 14:16:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brown</td>\n",
       "      <td>0.26</td>\n",
       "      <td>46.19</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4141</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.562892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8055</th>\n",
       "      <td>ANSL_61035</td>\n",
       "      <td>2016-07-09</td>\n",
       "      <td>2016-10-12 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Blue Tabby</td>\n",
       "      <td>0.41</td>\n",
       "      <td>18.15</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.258953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8056</th>\n",
       "      <td>ANSL_76431</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>2019-05-02 19:01:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Chocolate</td>\n",
       "      <td>0.93</td>\n",
       "      <td>8.44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11.018957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8057</th>\n",
       "      <td>ANSL_68218</td>\n",
       "      <td>2016-09-03</td>\n",
       "      <td>2016-12-28 17:12:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fawn</td>\n",
       "      <td>0.93</td>\n",
       "      <td>11.76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116</td>\n",
       "      <td>93.0</td>\n",
       "      <td>7.908163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8058</th>\n",
       "      <td>ANSL_75248</td>\n",
       "      <td>2008-03-15</td>\n",
       "      <td>2019-04-05 15:49:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>White</td>\n",
       "      <td>0.68</td>\n",
       "      <td>7.66</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4038</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8.877285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8059</th>\n",
       "      <td>ANSL_76802</td>\n",
       "      <td>2012-12-09</td>\n",
       "      <td>2019-05-10 15:53:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Buff</td>\n",
       "      <td>0.61</td>\n",
       "      <td>31.17</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2343</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.957010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8060</th>\n",
       "      <td>ANSL_56329</td>\n",
       "      <td>2011-07-19</td>\n",
       "      <td>2016-08-12 18:20:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Blue</td>\n",
       "      <td>0.93</td>\n",
       "      <td>7.27</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1851</td>\n",
       "      <td>93.0</td>\n",
       "      <td>12.792297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8061</th>\n",
       "      <td>ANSL_52255</td>\n",
       "      <td>2016-12-10</td>\n",
       "      <td>2017-03-16 08:54:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Blue</td>\n",
       "      <td>0.24</td>\n",
       "      <td>46.26</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.518807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8062</th>\n",
       "      <td>ANSL_61204</td>\n",
       "      <td>2001-08-27</td>\n",
       "      <td>2016-09-17 14:58:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.75</td>\n",
       "      <td>31.70</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5500</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.365931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8063</th>\n",
       "      <td>ANSL_73450</td>\n",
       "      <td>2018-11-07</td>\n",
       "      <td>2019-01-24 16:25:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Calico</td>\n",
       "      <td>0.27</td>\n",
       "      <td>17.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.549053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8064</th>\n",
       "      <td>ANSL_76013</td>\n",
       "      <td>2017-01-25</td>\n",
       "      <td>2019-04-23 16:34:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.96</td>\n",
       "      <td>37.48</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>818</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.561366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8065</th>\n",
       "      <td>ANSL_74798</td>\n",
       "      <td>2009-09-10</td>\n",
       "      <td>2017-10-08 12:01:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>White</td>\n",
       "      <td>0.67</td>\n",
       "      <td>30.39</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2950</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.204673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8066</th>\n",
       "      <td>ANSL_59444</td>\n",
       "      <td>2016-04-04</td>\n",
       "      <td>2018-10-23 11:49:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brown Tabby</td>\n",
       "      <td>0.70</td>\n",
       "      <td>14.04</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>932</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.985755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8067</th>\n",
       "      <td>ANSL_66809</td>\n",
       "      <td>2016-02-10</td>\n",
       "      <td>2017-03-10 14:56:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Brown</td>\n",
       "      <td>0.82</td>\n",
       "      <td>36.08</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>394</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8068</th>\n",
       "      <td>ANSL_59041</td>\n",
       "      <td>2015-12-07</td>\n",
       "      <td>2018-02-12 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Tan</td>\n",
       "      <td>0.49</td>\n",
       "      <td>27.54</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>798</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1.779230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8069</th>\n",
       "      <td>ANSL_60034</td>\n",
       "      <td>2015-12-08</td>\n",
       "      <td>2017-01-04 17:19:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.98</td>\n",
       "      <td>37.19</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>393</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.635117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8070</th>\n",
       "      <td>ANSL_58066</td>\n",
       "      <td>2016-06-28</td>\n",
       "      <td>2017-07-20 18:19:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.79</td>\n",
       "      <td>23.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>387</td>\n",
       "      <td>79.0</td>\n",
       "      <td>3.315149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8071</th>\n",
       "      <td>ANSL_69436</td>\n",
       "      <td>2016-02-02</td>\n",
       "      <td>2017-02-28 16:47:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.64</td>\n",
       "      <td>24.51</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>392</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.611179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26906 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          pet_id issue_date        listing_date  condition     color_type  \\\n",
       "0     ANSL_69903 2016-10-07 2016-09-21 16:25:00        2.0    Brown Tabby   \n",
       "1     ANSL_66892 2013-11-21 2018-12-27 17:47:00        1.0          White   \n",
       "2     ANSL_69750 2014-09-28 2016-10-19 08:24:00        NaN          Brown   \n",
       "3     ANSL_71623 2016-12-31 2019-01-25 18:30:00        1.0          White   \n",
       "4     ANSL_57969 2017-09-28 2017-11-19 09:38:00        2.0          Black   \n",
       "5     ANSL_52200 2017-10-25 2019-10-03 13:18:00        0.0            Red   \n",
       "6     ANSL_75444 2018-06-11 2019-09-04 16:00:00        2.0    Brown Tabby   \n",
       "7     ANSL_52759 2018-05-06 2018-02-09 17:04:00        0.0          White   \n",
       "8     ANSL_74632 2015-10-10 2016-04-08 16:29:00        2.0          Brown   \n",
       "9     ANSL_56464 2014-02-18 2017-03-17 11:38:00        2.0          Brown   \n",
       "10    ANSL_69485 2004-11-17 2016-12-15 13:33:00        0.0       Tricolor   \n",
       "11    ANSL_51014 2017-06-17 2018-08-02 09:00:00        2.0    Brown Tabby   \n",
       "12    ANSL_76771 2017-07-02 2019-10-05 16:42:00        1.0          Black   \n",
       "13    ANSL_69252 2016-11-29 2017-05-31 00:00:00        1.0          White   \n",
       "14    ANSL_58085 2018-01-29 2018-02-05 17:46:00        0.0         Calico   \n",
       "15    ANSL_63686 2016-07-20 2016-10-10 12:14:00        1.0          Black   \n",
       "16    ANSL_66127 2016-03-20 2016-03-11 13:55:00        1.0            Red   \n",
       "17    ANSL_52366 2009-06-01 2019-01-27 11:56:00        1.0          Black   \n",
       "18    ANSL_55607 2013-11-28 2017-12-28 00:00:00        2.0  Brown Brindle   \n",
       "19    ANSL_72451 2018-08-23 2018-11-29 11:46:00        0.0          Black   \n",
       "20    ANSL_66055 2008-01-25 2016-08-15 11:27:00        1.0    Brown Tabby   \n",
       "21    ANSL_50488 2018-07-01 2018-07-28 12:36:00        NaN          Brown   \n",
       "22    ANSL_68751 2018-03-14 2018-05-24 17:10:00        0.0          White   \n",
       "23    ANSL_57387 2014-11-22 2017-10-04 16:55:00        0.0           Blue   \n",
       "24    ANSL_67782 2012-02-08 2019-07-01 00:00:00        2.0          Black   \n",
       "25    ANSL_76397 2018-11-04 2019-02-05 14:16:00        1.0          Black   \n",
       "26    ANSL_59210 2017-12-30 2018-04-25 17:41:00        0.0         Tortie   \n",
       "27    ANSL_56018 2011-01-25 2017-10-03 12:21:00        1.0           Gray   \n",
       "28    ANSL_53325 2015-01-09 2016-09-23 18:16:00        1.0           Fawn   \n",
       "29    ANSL_52456 2010-07-18 2018-02-18 14:46:00        0.0       Tricolor   \n",
       "...          ...        ...                 ...        ...            ...   \n",
       "8042  ANSL_54807 2018-12-23 2019-03-18 14:55:00        0.0  Brown Brindle   \n",
       "8043  ANSL_51407 2009-08-01 2017-02-03 19:09:00        1.0            Tan   \n",
       "8044  ANSL_68531 2010-02-05 2018-04-03 13:32:00        1.0  Brown Brindle   \n",
       "8045  ANSL_63603 2016-08-29 2017-09-20 15:11:00        2.0           Blue   \n",
       "8046  ANSL_58426 2017-03-22 2018-04-15 16:31:00        2.0            Tan   \n",
       "8047  ANSL_68768 2017-08-14 2017-11-19 19:52:00        NaN    Brown Tabby   \n",
       "8048  ANSL_66647 2017-04-29 2018-05-19 11:17:00        2.0           Fawn   \n",
       "8049  ANSL_53916 2015-08-28 2017-09-26 13:41:00        1.0            Tan   \n",
       "8050  ANSL_62236 2017-07-07 2017-09-28 14:28:00        2.0          Brown   \n",
       "8051  ANSL_71438 2018-08-15 2018-12-01 16:58:00        1.0     Seal Point   \n",
       "8052  ANSL_70272 2015-07-05 2016-07-31 16:38:00        0.0      Chocolate   \n",
       "8053  ANSL_52080 2016-11-05 2017-12-13 12:36:00        0.0          Black   \n",
       "8054  ANSL_51166 2005-12-14 2017-04-16 14:16:00        0.0          Brown   \n",
       "8055  ANSL_61035 2016-07-09 2016-10-12 00:00:00        0.0     Blue Tabby   \n",
       "8056  ANSL_76431 2018-12-24 2019-05-02 19:01:00        0.0      Chocolate   \n",
       "8057  ANSL_68218 2016-09-03 2016-12-28 17:12:00        0.0           Fawn   \n",
       "8058  ANSL_75248 2008-03-15 2019-04-05 15:49:00        1.0          White   \n",
       "8059  ANSL_76802 2012-12-09 2019-05-10 15:53:00        0.0           Buff   \n",
       "8060  ANSL_56329 2011-07-19 2016-08-12 18:20:00        0.0           Blue   \n",
       "8061  ANSL_52255 2016-12-10 2017-03-16 08:54:00        0.0           Blue   \n",
       "8062  ANSL_61204 2001-08-27 2016-09-17 14:58:00        0.0          Black   \n",
       "8063  ANSL_73450 2018-11-07 2019-01-24 16:25:00        0.0         Calico   \n",
       "8064  ANSL_76013 2017-01-25 2019-04-23 16:34:00        1.0          Black   \n",
       "8065  ANSL_74798 2009-09-10 2017-10-08 12:01:00        1.0          White   \n",
       "8066  ANSL_59444 2016-04-04 2018-10-23 11:49:00        1.0    Brown Tabby   \n",
       "8067  ANSL_66809 2016-02-10 2017-03-10 14:56:00        2.0          Brown   \n",
       "8068  ANSL_59041 2015-12-07 2018-02-12 00:00:00        0.0            Tan   \n",
       "8069  ANSL_60034 2015-12-08 2017-01-04 17:19:00        0.0          Black   \n",
       "8070  ANSL_58066 2016-06-28 2017-07-20 18:19:00        NaN          Black   \n",
       "8071  ANSL_69436 2016-02-02 2017-02-28 16:47:00        0.0          Black   \n",
       "\n",
       "      length(m)  height(cm)  X1  X2  breed_category  pet_category  duration  \\\n",
       "0          0.80        7.78  13   9             0.0           1.0        16   \n",
       "1          0.72       14.19  13   9             0.0           2.0      1862   \n",
       "2          0.15       40.90  15   4             2.0           4.0       752   \n",
       "3          0.62       17.82   0   1             0.0           2.0       755   \n",
       "4          0.50       11.06  18   4             0.0           1.0        52   \n",
       "5          0.92       29.56   0   7             1.0           2.0       708   \n",
       "6          0.14       40.24   0   1             0.0           1.0       450   \n",
       "7          0.15       25.48   7   1             1.0           1.0        86   \n",
       "8          0.05       38.22  13   9             1.0           2.0       181   \n",
       "9          0.55       21.26  13   9             1.0           2.0      1123   \n",
       "10         0.30       23.06   0   1             1.0           2.0      4411   \n",
       "11         0.44       40.32  16   9             1.0           1.0       411   \n",
       "12         0.20       30.78  13   9             0.0           2.0       825   \n",
       "13         0.45        5.39  13   9             0.0           2.0       183   \n",
       "14         0.48       30.54   0   1             1.0           1.0         7   \n",
       "15         0.18       49.05  13   9             0.0           1.0        82   \n",
       "16         0.88       49.50   7   1             0.0           2.0         9   \n",
       "17         0.93       14.83   0   7             0.0           2.0      3527   \n",
       "18         0.76       38.99  13   9             1.0           2.0      1491   \n",
       "19         0.96       24.33   0   1             1.0           1.0        98   \n",
       "20         0.66       36.01   0   1             0.0           1.0      3125   \n",
       "21         0.45       14.00  11   4             2.0           4.0        27   \n",
       "22         0.92       49.50   7   1             1.0           2.0        71   \n",
       "23         0.31       49.04  13   9             1.0           2.0      1047   \n",
       "24         0.51       45.53  13   9             0.0           2.0      2700   \n",
       "25         0.80       37.17   0   7             0.0           2.0        93   \n",
       "26         0.53       35.06   0   1             1.0           1.0       116   \n",
       "27         0.32       48.92   0   7             0.0           2.0      2443   \n",
       "28         0.98       14.02   0   7             0.0           2.0       623   \n",
       "29         0.68       22.33   0   7             1.0           2.0      2772   \n",
       "...         ...         ...  ..  ..             ...           ...       ...   \n",
       "8042       0.85       12.96   0   1             NaN           NaN        85   \n",
       "8043       0.97        5.77   0   1             NaN           NaN      2743   \n",
       "8044       0.82       17.72   0   1             NaN           NaN      2979   \n",
       "8045       0.66       18.02  13   9             NaN           NaN       387   \n",
       "8046       0.68       35.33  13   9             NaN           NaN       389   \n",
       "8047       0.90       37.05  13   9             NaN           NaN        97   \n",
       "8048       0.63       41.40   0   7             NaN           NaN       385   \n",
       "8049       0.38       32.44   0   1             NaN           NaN       760   \n",
       "8050       0.86       13.15  13   9             NaN           NaN        83   \n",
       "8051       0.81       19.02   7   1             NaN           NaN       108   \n",
       "8052       0.99       27.48   0   7             NaN           NaN       392   \n",
       "8053       0.04       20.37   0   1             NaN           NaN       403   \n",
       "8054       0.26       46.19   0   7             NaN           NaN      4141   \n",
       "8055       0.41       18.15  13   9             NaN           NaN        95   \n",
       "8056       0.93        8.44   0   1             NaN           NaN       129   \n",
       "8057       0.93       11.76   0   1             NaN           NaN       116   \n",
       "8058       0.68        7.66   0   7             NaN           NaN      4038   \n",
       "8059       0.61       31.17   0   7             NaN           NaN      2343   \n",
       "8060       0.93        7.27   0   7             NaN           NaN      1851   \n",
       "8061       0.24       46.26   7   1             NaN           NaN        96   \n",
       "8062       0.75       31.70   0   7             NaN           NaN      5500   \n",
       "8063       0.27       17.43   0   1             NaN           NaN        78   \n",
       "8064       0.96       37.48  13   9             NaN           NaN       818   \n",
       "8065       0.67       30.39   0   1             NaN           NaN      2950   \n",
       "8066       0.70       14.04  13   9             NaN           NaN       932   \n",
       "8067       0.82       36.08  13   9             NaN           NaN       394   \n",
       "8068       0.49       27.54  13   9             NaN           NaN       798   \n",
       "8069       0.98       37.19   0   7             NaN           NaN       393   \n",
       "8070       0.79       23.83   0   2             NaN           NaN       387   \n",
       "8071       0.64       24.51   0   1             NaN           NaN       392   \n",
       "\n",
       "      length(cm)  ratio_len_height  \n",
       "0           80.0         10.282776  \n",
       "1           72.0          5.073996  \n",
       "2           15.0          0.366748  \n",
       "3           62.0          3.479237  \n",
       "4           50.0          4.520796  \n",
       "5           92.0          3.112314  \n",
       "6           14.0          0.347913  \n",
       "7           15.0          0.588697  \n",
       "8            5.0          0.130822  \n",
       "9           55.0          2.587018  \n",
       "10          30.0          1.300954  \n",
       "11          44.0          1.091270  \n",
       "12          20.0          0.649773  \n",
       "13          45.0          8.348794  \n",
       "14          48.0          1.571709  \n",
       "15          18.0          0.366972  \n",
       "16          88.0          1.777778  \n",
       "17          93.0          6.271072  \n",
       "18          76.0          1.949218  \n",
       "19          96.0          3.945746  \n",
       "20          66.0          1.832824  \n",
       "21          45.0          3.214286  \n",
       "22          92.0          1.858586  \n",
       "23          31.0          0.632137  \n",
       "24          51.0          1.120141  \n",
       "25          80.0          2.152273  \n",
       "26          53.0          1.511694  \n",
       "27          32.0          0.654129  \n",
       "28          98.0          6.990014  \n",
       "29          68.0          3.045231  \n",
       "...          ...               ...  \n",
       "8042        85.0          6.558642  \n",
       "8043        97.0         16.811092  \n",
       "8044        82.0          4.627540  \n",
       "8045        66.0          3.662597  \n",
       "8046        68.0          1.924710  \n",
       "8047        90.0          2.429150  \n",
       "8048        63.0          1.521739  \n",
       "8049        38.0          1.171393  \n",
       "8050        86.0          6.539924  \n",
       "8051        81.0          4.258675  \n",
       "8052        99.0          3.602620  \n",
       "8053         4.0          0.196367  \n",
       "8054        26.0          0.562892  \n",
       "8055        41.0          2.258953  \n",
       "8056        93.0         11.018957  \n",
       "8057        93.0          7.908163  \n",
       "8058        68.0          8.877285  \n",
       "8059        61.0          1.957010  \n",
       "8060        93.0         12.792297  \n",
       "8061        24.0          0.518807  \n",
       "8062        75.0          2.365931  \n",
       "8063        27.0          1.549053  \n",
       "8064        96.0          2.561366  \n",
       "8065        67.0          2.204673  \n",
       "8066        70.0          4.985755  \n",
       "8067        82.0          2.272727  \n",
       "8068        49.0          1.779230  \n",
       "8069        98.0          2.635117  \n",
       "8070        79.0          3.315149  \n",
       "8071        64.0          2.611179  \n",
       "\n",
       "[26906 rows x 14 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# missing values filling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['condition'].fillna(-1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# categorical values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['color_number'] = LabelEncoder().fit_transform(df['color_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_and_bind(original_dataframe, feature_to_encode):\n",
    "    dummies = pd.get_dummies(original_dataframe[[feature_to_encode]])\n",
    "    res = pd.concat([original_dataframe, dummies], axis=1)\n",
    "    return(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = encode_and_bind(df, 'color_type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# drop non required cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['pet_category','breed_category','pet_id','issue_date','listing_date','color_type',\n",
    "              'length(m)']\n",
    "             ,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>height(cm)</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>duration</th>\n",
       "      <th>length(cm)</th>\n",
       "      <th>ratio_len_height</th>\n",
       "      <th>color_type_Agouti</th>\n",
       "      <th>color_type_Apricot</th>\n",
       "      <th>color_type_Black</th>\n",
       "      <th>...</th>\n",
       "      <th>color_type_Silver Lynx Point</th>\n",
       "      <th>color_type_Silver Tabby</th>\n",
       "      <th>color_type_Tan</th>\n",
       "      <th>color_type_Torbie</th>\n",
       "      <th>color_type_Tortie</th>\n",
       "      <th>color_type_Tortie Point</th>\n",
       "      <th>color_type_Tricolor</th>\n",
       "      <th>color_type_White</th>\n",
       "      <th>color_type_Yellow</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8070</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>23.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>387</td>\n",
       "      <td>79.0</td>\n",
       "      <td>3.315149</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8071</th>\n",
       "      <td>0.0</td>\n",
       "      <td>24.51</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>392</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.611179</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      condition  height(cm)  X1  X2  duration  length(cm)  ratio_len_height  \\\n",
       "8067        2.0       36.08  13   9       394        82.0          2.272727   \n",
       "8068        0.0       27.54  13   9       798        49.0          1.779230   \n",
       "8069        0.0       37.19   0   7       393        98.0          2.635117   \n",
       "8070       -1.0       23.83   0   2       387        79.0          3.315149   \n",
       "8071        0.0       24.51   0   1       392        64.0          2.611179   \n",
       "\n",
       "      color_type_Agouti  color_type_Apricot  color_type_Black  ...  \\\n",
       "8067                  0                   0                 0  ...   \n",
       "8068                  0                   0                 0  ...   \n",
       "8069                  0                   0                 1  ...   \n",
       "8070                  0                   0                 1  ...   \n",
       "8071                  0                   0                 1  ...   \n",
       "\n",
       "      color_type_Silver Lynx Point  color_type_Silver Tabby  color_type_Tan  \\\n",
       "8067                             0                        0               0   \n",
       "8068                             0                        0               1   \n",
       "8069                             0                        0               0   \n",
       "8070                             0                        0               0   \n",
       "8071                             0                        0               0   \n",
       "\n",
       "      color_type_Torbie  color_type_Tortie  color_type_Tortie Point  \\\n",
       "8067                  0                  0                        0   \n",
       "8068                  0                  0                        0   \n",
       "8069                  0                  0                        0   \n",
       "8070                  0                  0                        0   \n",
       "8071                  0                  0                        0   \n",
       "\n",
       "      color_type_Tricolor  color_type_White  color_type_Yellow  \\\n",
       "8067                    0                 0                  0   \n",
       "8068                    0                 0                  0   \n",
       "8069                    0                 0                  0   \n",
       "8070                    0                 0                  0   \n",
       "8071                    0                 0                  0   \n",
       "\n",
       "      color_type_Yellow Brindle  \n",
       "8067                          0  \n",
       "8068                          0  \n",
       "8069                          0  \n",
       "8070                          0  \n",
       "8071                          0  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# splitting train-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df[:18834]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18834, 63)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = train['breed_category'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18834,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = train['pet_category'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18834,)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX = pd.concat([X_train,train['pet_category']],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18834, 64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = df[18834:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8072, 63)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL_1 PET CATEGORY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_train, X_valid, y_train, y_valid = train_test_split(X_train, Z, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:53:47] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { scale_pos_weight, silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8950686931705051"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier=XGBClassifier(silent=False, \n",
    "                      scale_pos_weight=1,\n",
    "                      learning_rate=0.4,  \n",
    "                      colsample_bytree = 0.4,\n",
    "                      subsample = 0.8,\n",
    "                      objective='binary:logistic', \n",
    "                      n_estimators=1000, \n",
    "                      reg_alpha = 0.3,\n",
    "                      max_depth=4, \n",
    "                      gamma=4)\n",
    "\n",
    "classifier.fit(new_train,y_train)\n",
    "classifier.score(new_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   9    2    4    7]\n",
      " [   0 1166  229    6]\n",
      " [   0  144 2005    1]\n",
      " [   0    9   33  152]]\n",
      "0.8845234934961508\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.41      0.58        22\n",
      "           1       0.88      0.83      0.86      1401\n",
      "           2       0.88      0.93      0.91      2150\n",
      "           4       0.92      0.78      0.84       194\n",
      "\n",
      "    accuracy                           0.88      3767\n",
      "   macro avg       0.92      0.74      0.80      3767\n",
      "weighted avg       0.89      0.88      0.88      3767\n",
      "\n",
      "0.8831934541877808\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,f1_score\n",
    "y_pred_1=classifier.predict(X_valid)\n",
    "print(confusion_matrix(y_valid,y_pred_1))\n",
    "print(accuracy_score(y_valid,y_pred_1))\n",
    "print(classification_report(y_valid,y_pred_1))\n",
    "f1 = f1_score(y_valid,y_pred_1,average='weighted')\n",
    "print(f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {        \n",
    "          'min_child_weight': [1,2,3,4 ,5,6,7,8,9,10],\n",
    "        'gamma': [0.5, 1, 1.5, 2, 5],\n",
    "        'subsample': [0.6, 0.8, 1.0],\n",
    "        'colsample_bytree': [0.6, 0.8, 1.0],\n",
    "        'max_depth': [1,2,3, 4, 5],\n",
    "          }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV,GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "folds = 5\n",
    "param_comb = 5\n",
    "\n",
    "skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001)\n",
    "\n",
    "random_search_1 = RandomizedSearchCV(classifier, \n",
    "                                   param_distributions=params, \n",
    "                                   n_iter=param_comb, \n",
    "                                   n_jobs=4, \n",
    "                                   cv=skf.split(new_train,y_train),\n",
    "                                   verbose=3,\n",
    "                                   random_state=1001 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  25 out of  25 | elapsed: 31.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:30:20] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { scale_pos_weight, silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=<generator object _BaseKFold.split at 0x000001A695AD1A20>,\n",
       "                   estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
       "                                           colsample_bylevel=1,\n",
       "                                           colsample_bynode=1,\n",
       "                                           colsample_bytree=0.4, gamma=4,\n",
       "                                           gpu_id=-1, importance_type='gain',\n",
       "                                           interaction_constraints='',\n",
       "                                           learning_rate=0.4, max_delta_step=0,\n",
       "                                           max_depth=4, min_child_weight=1,\n",
       "                                           missing=nan,\n",
       "                                           monotone_con...\n",
       "                                           random_state=0, reg_alpha=0.3,\n",
       "                                           reg_lambda=1, scale_pos_weight=1,\n",
       "                                           silent=False, subsample=0.8,\n",
       "                                           tree_method='exact',\n",
       "                                           validate_parameters=1,\n",
       "                                           verbosity=None),\n",
       "                   n_iter=5, n_jobs=4,\n",
       "                   param_distributions={'colsample_bytree': [0.6, 0.8, 1.0],\n",
       "                                        'gamma': [0.5, 1, 1.5, 2, 5],\n",
       "                                        'max_depth': [1, 2, 3, 4, 5],\n",
       "                                        'min_child_weight': [1, 2, 3, 4, 5, 6,\n",
       "                                                             7, 8, 9, 10],\n",
       "                                        'subsample': [0.6, 0.8, 1.0]},\n",
       "                   random_state=1001, verbose=3)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search_1.fit(new_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9128559102674719"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search_1.score(new_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subsample': 1.0,\n",
       " 'min_child_weight': 6,\n",
       " 'max_depth': 3,\n",
       " 'gamma': 1,\n",
       " 'colsample_bytree': 0.6}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search_1.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8842580302628086\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.36      0.53        22\n",
      "           1       0.89      0.83      0.86      1401\n",
      "           2       0.88      0.93      0.91      2150\n",
      "           4       0.90      0.80      0.85       194\n",
      "\n",
      "    accuracy                           0.88      3767\n",
      "   macro avg       0.92      0.73      0.79      3767\n",
      "weighted avg       0.88      0.88      0.88      3767\n",
      "\n",
      "0.8828037850401712\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y_pred_1 = random_search_1.predict(X_valid) \n",
    "print(accuracy_score(y_valid,y_pred_1))\n",
    "print(classification_report(y_valid, y_pred_1))\n",
    "f1 = f1_score(y_valid,y_pred_1,average='weighted')\n",
    "print(f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 2, 2, 2], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_1 = random_search_1.predict(X_test)\n",
    "pred_1 = np.array(pred_1).flatten()\n",
    "pred_1[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    4866\n",
       "1    2847\n",
       "4     355\n",
       "0       4\n",
       "Name: pet_category, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.concat([test['pet_id'],pd.Series(pred_1)],axis=1)\n",
    "result.columns = ['pet_id','pet_category']\n",
    "result['pet_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jayita\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_test['pet_category'] = pred_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>height(cm)</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>duration</th>\n",
       "      <th>length(cm)</th>\n",
       "      <th>ratio_len_height</th>\n",
       "      <th>color_type_Agouti</th>\n",
       "      <th>color_type_Apricot</th>\n",
       "      <th>color_type_Black</th>\n",
       "      <th>...</th>\n",
       "      <th>color_type_Silver Tabby</th>\n",
       "      <th>color_type_Tan</th>\n",
       "      <th>color_type_Torbie</th>\n",
       "      <th>color_type_Tortie</th>\n",
       "      <th>color_type_Tortie Point</th>\n",
       "      <th>color_type_Tricolor</th>\n",
       "      <th>color_type_White</th>\n",
       "      <th>color_type_Yellow</th>\n",
       "      <th>color_type_Yellow Brindle</th>\n",
       "      <th>pet_category</th>\n",
       "    </tr>\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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.894188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>41.21</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1999</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.582383</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.46</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1148</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.427896</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>30.92</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>463</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.296248</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   condition  height(cm)  X1  X2  duration  length(cm)  ratio_len_height  \\\n",
       "0        0.0       42.73   0   7      4404        87.0          2.036040   \n",
       "1        1.0        6.71   0   1       174         6.0          0.894188   \n",
       "2        1.0       41.21   0   7      1999        24.0          0.582383   \n",
       "3        1.0        8.46   7   1      1148        29.0          3.427896   \n",
       "4        1.0       30.92   0   7       463        71.0          2.296248   \n",
       "\n",
       "   color_type_Agouti  color_type_Apricot  color_type_Black  ...  \\\n",
       "0                  0                   0                 1  ...   \n",
       "1                  0                   0                 0  ...   \n",
       "2                  0                   0                 1  ...   \n",
       "3                  0                   0                 1  ...   \n",
       "4                  0                   0                 0  ...   \n",
       "\n",
       "   color_type_Silver Tabby  color_type_Tan  color_type_Torbie  \\\n",
       "0                        0               0                  0   \n",
       "1                        0               0                  0   \n",
       "2                        0               0                  0   \n",
       "3                        0               0                  0   \n",
       "4                        0               0                  0   \n",
       "\n",
       "   color_type_Tortie  color_type_Tortie Point  color_type_Tricolor  \\\n",
       "0                  0                        0                    0   \n",
       "1                  0                        0                    0   \n",
       "2                  0                        0                    0   \n",
       "3                  0                        0                    0   \n",
       "4                  0                        0                    0   \n",
       "\n",
       "   color_type_White  color_type_Yellow  color_type_Yellow Brindle  \\\n",
       "0                 0                  0                          0   \n",
       "1                 0                  0                          0   \n",
       "2                 0                  0                          0   \n",
       "3                 0                  0                          0   \n",
       "4                 0                  0                          0   \n",
       "\n",
       "   pet_category  \n",
       "0             2  \n",
       "1             1  \n",
       "2             2  \n",
       "3             2  \n",
       "4             2  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL_2 Breed Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((18834, 64), (18834,), (8072, 64))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape,Y.shape,X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_new, validX, train_y, validy = train_test_split(trainX, Y, test_size=0.2, random_state=44)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:31:48] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { scale_pos_weight, silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9265281741554391"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_2 = XGBClassifier(silent=False, \n",
    "                      scale_pos_weight=1,\n",
    "                      learning_rate=0.4,  \n",
    "                      colsample_bytree = 0.4,\n",
    "                      subsample = 0.8,\n",
    "                      objective='binary:logistic', \n",
    "                      n_estimators=1000, \n",
    "                      reg_alpha = 0.3,\n",
    "                      max_depth=4, \n",
    "                      gamma=4)\n",
    "\n",
    "classifier_2.fit(train_new,train_y)\n",
    "classifier_2.score(train_new,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1648  140    0]\n",
      " [ 216 1475    0]\n",
      " [   0    0  288]]\n",
      "0.9054950889301832\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.92      0.90      1788\n",
      "           1       0.91      0.87      0.89      1691\n",
      "           2       1.00      1.00      1.00       288\n",
      "\n",
      "    accuracy                           0.91      3767\n",
      "   macro avg       0.93      0.93      0.93      3767\n",
      "weighted avg       0.91      0.91      0.91      3767\n",
      "\n",
      "0.9053921735091569\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,f1_score\n",
    "\n",
    "y_pred=classifier_2.predict(validX)\n",
    "print(confusion_matrix(validy,y_pred))\n",
    "print(accuracy_score(validy,y_pred))\n",
    "print(classification_report(validy,y_pred))\n",
    "print(f1_score(validy,y_pred,average='weighted'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_rs = {        \n",
    "          'min_child_weight': [1,2,3,4 ,5,6,7,8,9,10],\n",
    "        'gamma': [0.5, 1, 1.5, 2, 5],\n",
    "        'subsample': [0.6, 0.8, 1.0],\n",
    "        'colsample_bytree': [0.6, 0.8, 1.0],\n",
    "        'max_depth': [1,2,3, 4, 5],\n",
    "          }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import RandomizedSearchCV,GridSearchCV\n",
    "\n",
    "folds = 5\n",
    "param_comb = 5\n",
    "\n",
    "skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 100)\n",
    "\n",
    "random_search_2 = RandomizedSearchCV(classifier_2, \n",
    "                                   param_distributions=params_rs,\n",
    "                                   n_iter=param_comb, \n",
    "                                   n_jobs=4, \n",
    "                                   cv=skf.split(train_new,train_y), \n",
    "                                   verbose=3, \n",
    "                                   random_state=1001 )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  25 out of  25 | elapsed: 20.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:53:27] WARNING: C:\\Users\\Administrator\\workspace\\xgboost-win64_release_1.1.0\\src\\learner.cc:480: \n",
      "Parameters: { scale_pos_weight, silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9122585783500365"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search_2.fit(train_new,train_y)\n",
    "random_search_2.score(train_new,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subsample': 1.0,\n",
       " 'min_child_weight': 5,\n",
       " 'max_depth': 3,\n",
       " 'gamma': 2,\n",
       " 'colsample_bytree': 1.0}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search_2.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1683  105    0]\n",
      " [ 258 1433    0]\n",
      " [   0    0  288]]\n",
      "0.9036368462967879\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.94      0.90      1788\n",
      "           1       0.93      0.85      0.89      1691\n",
      "           2       1.00      1.00      1.00       288\n",
      "\n",
      "    accuracy                           0.90      3767\n",
      "   macro avg       0.93      0.93      0.93      3767\n",
      "weighted avg       0.91      0.90      0.90      3767\n",
      "\n",
      "0.9033307331259695\n"
     ]
    }
   ],
   "source": [
    "y_pred = random_search_2.predict(validX)\n",
    "print(confusion_matrix(validy,y_pred))\n",
    "print(accuracy_score(validy,y_pred))\n",
    "print(classification_report(validy,y_pred))\n",
    "f2 = f1_score(validy,y_pred,average='weighted')\n",
    "print(f2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_2 = random_search_2.predict(X_test)\n",
    "pred_2 = np.array(pred_2).flatten()\n",
    "pred_2[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4242\n",
       "1    3211\n",
       "2     619\n",
       "Name: breed_category, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.concat([test['pet_id'],pd.Series(pred_2)],axis=1)\n",
    "result.columns = ['pet_id','breed_category']\n",
    "result['breed_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.30672590830704"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy=100*((f1+f2)/2)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df = pd.DataFrame(data={'pet_id':test['pet_id'], 'breed_category':pred_2,'pet_category':pred_1})\n",
    "results_df.to_csv('FINAL.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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