Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# importing libraries/importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#reading data\n",
    "\n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# saving id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of training data=(18834, 11)\n",
      "size of test data=(8072, 9)\n"
     ]
    }
   ],
   "source": [
    "train_ID= train['pet_id']\n",
    "test_ID=test['pet_id']\n",
    "print('size of training data={}'.format(train.shape))\n",
    "print('size of test data={}'.format(test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['pet_id'],axis = 1,inplace = True)\n",
    "test.drop(['pet_id'],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>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>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>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>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>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": [
       "            issue_date         listing_date  condition    color_type  \\\n",
       "0  2005-08-17 00:00:00  2017-09-07 15:35:00        0.0         Black   \n",
       "1  2018-11-15 00:00:00  2019-05-08 17:24:00        1.0  Orange Tabby   \n",
       "2  2012-10-11 00:00:00  2018-04-02 16:51:00        1.0         Black   \n",
       "3  2015-02-13 00:00:00  2018-04-06 07:25:00        1.0         Black   \n",
       "4  2017-01-18 00:00:00  2018-04-26 13:42:00        1.0         Brown   \n",
       "\n",
       "   length(m)  height(cm)  X1  X2  \n",
       "0       0.87       42.73   0   7  \n",
       "1       0.06        6.71   0   1  \n",
       "2       0.24       41.21   0   7  \n",
       "3       0.29        8.46   7   1  \n",
       "4       0.71       30.92   0   7  "
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>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>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>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>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>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": [
       "         issue_date      listing_date  condition   color_type  length(m)  \\\n",
       "0  10-07-2016 00:00  21-09-2016 16:25        2.0  Brown Tabby       0.80   \n",
       "1  21-11-2013 00:00  27-12-2018 17:47        1.0        White       0.72   \n",
       "2  28-09-2014 00:00  19-10-2016 08:24        NaN        Brown       0.15   \n",
       "3  31-12-2016 00:00  25-01-2019 18:30        1.0        White       0.62   \n",
       "4  28-09-2017 00:00  19-11-2017 09:38        2.0        Black       0.50   \n",
       "\n",
       "   height(cm)  X1  X2  breed_category  pet_category  \n",
       "0        7.78  13   9               0             1  \n",
       "1       14.19  13   9               0             2  \n",
       "2       40.90  15   4               2             4  \n",
       "3       17.82   0   1               0             2  \n",
       "4       11.06  18   4               0             1  "
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# target labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntrain = train.shape[0]\n",
    "ntest = test.shape[0]\n",
    "y_1 = train.breed_category\n",
    "y_2 = train.pet_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# feature enginerring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "combining test and train datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.concat((train,test)).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.drop(['breed_category','pet_category'],axis =1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
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       "      <th>0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         issue_date      listing_date  condition   color_type  length(m)  \\\n",
       "0  10-07-2016 00:00  21-09-2016 16:25        2.0  Brown Tabby       0.80   \n",
       "1  21-11-2013 00:00  27-12-2018 17:47        1.0        White       0.72   \n",
       "2  28-09-2014 00:00  19-10-2016 08:24        NaN        Brown       0.15   \n",
       "3  31-12-2016 00:00  25-01-2019 18:30        1.0        White       0.62   \n",
       "4  28-09-2017 00:00  19-11-2017 09:38        2.0        Black       0.50   \n",
       "\n",
       "   height(cm)  X1  X2  \n",
       "0        7.78  13   9  \n",
       "1       14.19  13   9  \n",
       "2       40.90  15   4  \n",
       "3       17.82   0   1  \n",
       "4       11.06  18   4  "
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.drop(['issue_date','listing_date'],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   condition   color_type  length(m)  height(cm)  X1  X2\n",
       "0        2.0  Brown Tabby       0.80        7.78  13   9\n",
       "1        1.0        White       0.72       14.19  13   9\n",
       "2        NaN        Brown       0.15       40.90  15   4\n",
       "3        1.0        White       0.62       17.82   0   1\n",
       "4        2.0        Black       0.50       11.06  18   4"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26906 entries, 0 to 26905\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   condition   24810 non-null  float64\n",
      " 1   color_type  26906 non-null  object \n",
      " 2   length(m)   26906 non-null  float64\n",
      " 3   height(cm)  26906 non-null  float64\n",
      " 4   X1          26906 non-null  int64  \n",
      " 5   X2          26906 non-null  int64  \n",
      "dtypes: float64(3), int64(2), object(1)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "all_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    9747\n",
       "0.0    8966\n",
       "2.0    6097\n",
       "Name: condition, dtype: int64"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['condition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = all_data.fillna((1.0+0.0+2.0)/3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26906 entries, 0 to 26905\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   condition   26906 non-null  float64\n",
      " 1   color_type  26906 non-null  object \n",
      " 2   length(m)   26906 non-null  float64\n",
      " 3   height(cm)  26906 non-null  float64\n",
      " 4   X1          26906 non-null  int64  \n",
      " 5   X2          26906 non-null  int64  \n",
      "dtypes: float64(3), int64(2), object(1)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "all_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99    310\n",
       "0.76    301\n",
       "0.85    299\n",
       "0.19    296\n",
       "0.20    296\n",
       "       ... \n",
       "0.26    233\n",
       "0.34    224\n",
       "0.17    219\n",
       "1.00    142\n",
       "0.00    137\n",
       "Name: length(m), Length: 101, dtype: int64"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['length(m)'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "137\n"
     ]
    }
   ],
   "source": [
    "print(len(all_data[all_data['length(m)'] == 0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "converting length(m) to length(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['length(cm)'] = all_data['length(m)'].apply(lambda x:x*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.drop('length(m)',axis = 1,inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26906 entries, 0 to 26905\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   condition   26906 non-null  float64\n",
      " 1   color_type  26906 non-null  object \n",
      " 2   height(cm)  26906 non-null  float64\n",
      " 3   X1          26906 non-null  int64  \n",
      " 4   X2          26906 non-null  int64  \n",
      " 5   length(cm)  26906 non-null  float64\n",
      "dtypes: float64(3), int64(2), object(1)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "all_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "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>condition</th>\n",
       "      <th>color_type</th>\n",
       "      <th>height(cm)</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>length(cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Brown Tabby</td>\n",
       "      <td>7.78</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>White</td>\n",
       "      <td>14.19</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Brown</td>\n",
       "      <td>40.90</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>White</td>\n",
       "      <td>17.82</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>11.06</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>50.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26901</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Brown</td>\n",
       "      <td>36.08</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26902</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Tan</td>\n",
       "      <td>27.54</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26903</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>37.19</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26904</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>23.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26905</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>24.51</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26906 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       condition   color_type  height(cm)  X1  X2  length(cm)\n",
       "0            2.0  Brown Tabby        7.78  13   9        80.0\n",
       "1            1.0        White       14.19  13   9        72.0\n",
       "2            1.0        Brown       40.90  15   4        15.0\n",
       "3            1.0        White       17.82   0   1        62.0\n",
       "4            2.0        Black       11.06  18   4        50.0\n",
       "...          ...          ...         ...  ..  ..         ...\n",
       "26901        2.0        Brown       36.08  13   9        82.0\n",
       "26902        0.0          Tan       27.54  13   9        49.0\n",
       "26903        0.0        Black       37.19   0   7        98.0\n",
       "26904        1.0        Black       23.83   0   2        79.0\n",
       "26905        0.0        Black       24.51   0   1        64.0\n",
       "\n",
       "[26906 rows x 6 columns]"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "color_type\n",
      "Agouti               0.000223\n",
      "Apricot              0.000595\n",
      "Black                0.244369\n",
      "Black Brindle        0.003419\n",
      "Black Smoke          0.001561\n",
      "Black Tabby          0.003196\n",
      "Black Tiger          0.000037\n",
      "Blue                 0.045901\n",
      "Blue Cream           0.000557\n",
      "Blue Merle           0.004980\n",
      "Blue Point           0.000855\n",
      "Blue Smoke           0.000446\n",
      "Blue Tabby           0.020702\n",
      "Blue Tick            0.001524\n",
      "Blue Tiger           0.000409\n",
      "Brown                0.096335\n",
      "Brown Brindle        0.026685\n",
      "Brown Merle          0.002267\n",
      "Brown Tabby          0.089162\n",
      "Brown Tiger          0.000149\n",
      "Buff                 0.006801\n",
      "Calico               0.018397\n",
      "Calico Point         0.000632\n",
      "Chocolate            0.014569\n",
      "Chocolate Point      0.000632\n",
      "Cream                0.008920\n",
      "Cream Tabby          0.009663\n",
      "Fawn                 0.007991\n",
      "Flame Point          0.002750\n",
      "Gold                 0.001487\n",
      "Gray                 0.016205\n",
      "Gray Tabby           0.003568\n",
      "Green                0.000483\n",
      "Lilac Point          0.000818\n",
      "Liver                0.000149\n",
      "Liver Tick           0.000074\n",
      "Lynx Point           0.006430\n",
      "Orange               0.002081\n",
      "Orange Tabby         0.041738\n",
      "Pink                 0.000409\n",
      "Red                  0.027577\n",
      "Red Merle            0.000929\n",
      "Red Tick             0.001264\n",
      "Sable                0.008623\n",
      "Seal Point           0.004274\n",
      "Silver               0.001264\n",
      "Silver Lynx Point    0.000260\n",
      "Silver Tabby         0.000966\n",
      "Tan                  0.071880\n",
      "Torbie               0.012674\n",
      "Tortie               0.019029\n",
      "Tortie Point         0.001264\n",
      "Tricolor             0.024679\n",
      "White                0.129562\n",
      "Yellow               0.007731\n",
      "Yellow Brindle       0.000855\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#frequency Encoding\n",
    "feq_encode = all_data.groupby('color_type').size()/len(all_data)\n",
    "print(feq_encode)\n",
    "\n",
    "all_data.loc[:,'color_type'] = all_data['color_type'].map(feq_encode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "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>condition</th>\n",
       "      <th>color_type</th>\n",
       "      <th>height(cm)</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>length(cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.089162</td>\n",
       "      <td>7.78</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.129562</td>\n",
       "      <td>14.19</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.096335</td>\n",
       "      <td>40.90</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.129562</td>\n",
       "      <td>17.82</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.244369</td>\n",
       "      <td>11.06</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   condition  color_type  height(cm)  X1  X2  length(cm)\n",
       "0        2.0    0.089162        7.78  13   9        80.0\n",
       "1        1.0    0.129562       14.19  13   9        72.0\n",
       "2        1.0    0.096335       40.90  15   4        15.0\n",
       "3        1.0    0.129562       17.82   0   1        62.0\n",
       "4        2.0    0.244369       11.06  18   4        50.0"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# getting dummies and splitting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26906, 6)\n"
     ]
    }
   ],
   "source": [
    "all_data = pd.get_dummies(all_data)\n",
    "print(all_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = all_data[:ntrain]\n",
    "test = all_data[ntrain:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "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>condition</th>\n",
       "      <th>color_type</th>\n",
       "      <th>height(cm)</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>length(cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18834</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.244369</td>\n",
       "      <td>42.73</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18835</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.041738</td>\n",
       "      <td>6.71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18836</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.244369</td>\n",
       "      <td>41.21</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18837</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.244369</td>\n",
       "      <td>8.46</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18838</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.096335</td>\n",
       "      <td>30.92</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>71.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26901</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.096335</td>\n",
       "      <td>36.08</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26902</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.071880</td>\n",
       "      <td>27.54</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26903</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.244369</td>\n",
       "      <td>37.19</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26904</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.244369</td>\n",
       "      <td>23.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26905</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.244369</td>\n",
       "      <td>24.51</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8072 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       condition  color_type  height(cm)  X1  X2  length(cm)\n",
       "18834        0.0    0.244369       42.73   0   7        87.0\n",
       "18835        1.0    0.041738        6.71   0   1         6.0\n",
       "18836        1.0    0.244369       41.21   0   7        24.0\n",
       "18837        1.0    0.244369        8.46   7   1        29.0\n",
       "18838        1.0    0.096335       30.92   0   7        71.0\n",
       "...          ...         ...         ...  ..  ..         ...\n",
       "26901        2.0    0.096335       36.08  13   9        82.0\n",
       "26902        0.0    0.071880       27.54  13   9        49.0\n",
       "26903        0.0    0.244369       37.19   0   7        98.0\n",
       "26904        1.0    0.244369       23.83   0   2        79.0\n",
       "26905        0.0    0.244369       24.51   0   1        64.0\n",
       "\n",
       "[8072 rows x 6 columns]"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# training the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hyper Parameters\n",
    "\n",
    "params={\n",
    " \"learning_rate\"    : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30,0.50 ] ,\n",
    " \"max_depth\"        : [ 3, 4, 5, 6, 8, 10, 12, 15],\n",
    " \"min_child_weight\" : [ 1, 3, 5, 7 ],\n",
    " \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],\n",
    " \"colsample_bytree\" : [ 0.3, 0.4, 0.5 , 0.7 ]\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "breed_precidition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "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=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  19 out of  25 | elapsed:   28.2s remaining:    8.8s\n",
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:   31.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None, gamma=None,\n",
       "                                           gpu_id=None, importance_type='gain',\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None,\n",
       "                                           max_delta_step=None, max_depth=None,\n",
       "                                           min_child_weight=None, missing=nan,\n",
       "                                           monotone_constraints=None,\n",
       "                                           n...\n",
       "                                           verbosity=None),\n",
       "                   iid='deprecated', n_iter=5, n_jobs=-1,\n",
       "                   param_distributions={'colsample_bytree': [0.3, 0.4, 0.5,\n",
       "                                                             0.7],\n",
       "                                        'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],\n",
       "                                        'learning_rate': [0.05, 0.1, 0.15, 0.2,\n",
       "                                                          0.25, 0.3, 0.5],\n",
       "                                        'max_depth': [3, 4, 5, 6, 8, 10, 12,\n",
       "                                                      15],\n",
       "                                        'min_child_weight': [1, 3, 5, 7]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=3)"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "xgb_model = xgb.XGBClassifier()\n",
    "random_search=RandomizedSearchCV(xgb_model,param_distributions=params,n_iter=5,n_jobs=-1,cv=5,verbose=3)\n",
    "random_search.fit(train,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_child_weight': 7,\n",
       " 'max_depth': 3,\n",
       " 'learning_rate': 0.2,\n",
       " 'gamma': 0.2,\n",
       " 'colsample_bytree': 0.5}"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.5, gamma=0.2, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.2, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=7, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
       "              objective='multi:softprob', random_state=0, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_1 = xgb.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "              colsample_bynode=1, colsample_bytree=0.5, gamma=0.0, gpu_id=-1,\n",
    "              importance_type='gain', interaction_constraints='',\n",
    "              learning_rate=0.3, max_delta_step=0, max_depth=3,\n",
    "              min_child_weight=7, monotone_constraints='()',\n",
    "              n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
    "              objective='multi:softprob', random_state=0, reg_alpha=0,\n",
    "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
    "              tree_method='exact', validate_parameters=1, verbosity=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.5, gamma=0.0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.3, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=7, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
       "              objective='multi:softprob', random_state=0, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_1.fit(train,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_1_pred = train_1.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 1, 2, 1], dtype=int64)"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_1_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pet_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "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=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  19 out of  25 | elapsed:   21.6s remaining:    6.8s\n",
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:   26.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None, gamma=None,\n",
       "                                           gpu_id=None, importance_type='gain',\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None,\n",
       "                                           max_delta_step=None, max_depth=None,\n",
       "                                           min_child_weight=None, missing=nan,\n",
       "                                           monotone_constraints=None,\n",
       "                                           n...\n",
       "                                           verbosity=None),\n",
       "                   iid='deprecated', n_iter=5, n_jobs=-1,\n",
       "                   param_distributions={'colsample_bytree': [0.3, 0.4, 0.5,\n",
       "                                                             0.7],\n",
       "                                        'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],\n",
       "                                        'learning_rate': [0.05, 0.1, 0.15, 0.2,\n",
       "                                                          0.25, 0.3, 0.5],\n",
       "                                        'max_depth': [3, 4, 5, 6, 8, 10, 12,\n",
       "                                                      15],\n",
       "                                        'min_child_weight': [1, 3, 5, 7]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=3)"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model = xgb.XGBClassifier()\n",
    "\n",
    "random_search=RandomizedSearchCV(xgb_model,param_distributions=params,n_iter=5,n_jobs=-1,cv=5,verbose=3)\n",
    "random_search.fit(train,y_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.4, gamma=0.0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.05, max_delta_step=0, max_depth=15,\n",
       "              min_child_weight=3, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
       "              objective='multi:softprob', random_state=0, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_child_weight': 3,\n",
       " 'max_depth': 15,\n",
       " 'learning_rate': 0.05,\n",
       " 'gamma': 0.0,\n",
       " 'colsample_bytree': 0.4}"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_2 = xgb.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "              colsample_bynode=1, colsample_bytree=0.7, gamma=0.4, gpu_id=-1,\n",
    "              importance_type='gain', interaction_constraints='',\n",
    "              learning_rate=0.15, max_delta_step=0, max_depth=6,\n",
    "              min_child_weight=3, monotone_constraints='()',\n",
    "              n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
    "              objective='multi:softprob', random_state=0, reg_alpha=0,\n",
    "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
    "              tree_method='exact', validate_parameters=1, verbosity=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.7, gamma=0.4, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.15, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=3, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
       "              objective='multi:softprob', random_state=0, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_2.fit(train,y_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_2_pred = train_2.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 2, ..., 2, 4, 2], dtype=int64)"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_2_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame()\n",
    "sub['pet_id'] = test_ID\n",
    "sub['breed_category'] = y_1_pred\n",
    "sub['pet_category']  = y_2_pred\n",
    "sub.to_csv('output_1.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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