Catboost_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HackerEarth Machine Learning Challenge - Adopt a Pet Buddy "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A leading pet adoption agency is planning to create a virtual tour experience for \n",
    "their customers showcasing all animals that are available in their shelter. \n",
    "To enable this tour experience, you are required to build a Machine Learning model that \n",
    "determines type and breed of the animal based on its physical attributes and other factors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1590,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1591,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas_profiling import ProfileReport\n",
    "from imblearn.over_sampling import SMOTE,SMOTENC,SVMSMOTE\n",
    "from imblearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import accuracy_score,make_scorer\n",
    "from sklearn.metrics import precision_score, recall_score, confusion_matrix,classification_report\n",
    "from sklearn.metrics import f1_score, roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from datetime import date\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1592,
   "metadata": {
    "scrolled": true
   },
   "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>2016-07-10 00:00:00</td>\n",
       "      <td>2016-09-21 16:25:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Brown Tabby</td>\n",
       "      <td>0.80</td>\n",
       "      <td>7.78</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANSL_66892</td>\n",
       "      <td>2013-11-21 00:00:00</td>\n",
       "      <td>2018-12-27 17:47:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>White</td>\n",
       "      <td>0.72</td>\n",
       "      <td>14.19</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANSL_69750</td>\n",
       "      <td>2014-09-28 00:00:00</td>\n",
       "      <td>2016-10-19 08:24:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown</td>\n",
       "      <td>0.15</td>\n",
       "      <td>40.90</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANSL_71623</td>\n",
       "      <td>2016-12-31 00:00:00</td>\n",
       "      <td>2019-01-25 18:30:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>White</td>\n",
       "      <td>0.62</td>\n",
       "      <td>17.82</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANSL_57969</td>\n",
       "      <td>2017-09-28 00:00:00</td>\n",
       "      <td>2017-11-19 09:38:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.06</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pet_id           issue_date         listing_date  condition  \\\n",
       "0  ANSL_69903  2016-07-10 00:00:00  2016-09-21 16:25:00        2.0   \n",
       "1  ANSL_66892  2013-11-21 00:00:00  2018-12-27 17:47:00        1.0   \n",
       "2  ANSL_69750  2014-09-28 00:00:00  2016-10-19 08:24:00        NaN   \n",
       "3  ANSL_71623  2016-12-31 00:00:00  2019-01-25 18:30:00        1.0   \n",
       "4  ANSL_57969  2017-09-28 00:00:00  2017-11-19 09:38:00        2.0   \n",
       "\n",
       "    color_type  length(m)  height(cm)  X1  X2  breed_category  pet_category  \n",
       "0  Brown Tabby       0.80        7.78  13   9             0.0             1  \n",
       "1        White       0.72       14.19  13   9             0.0             2  \n",
       "2        Brown       0.15       40.90  15   4             2.0             4  \n",
       "3        White       0.62       17.82   0   1             0.0             2  \n",
       "4        Black       0.50       11.06  18   4             0.0             1  "
      ]
     },
     "execution_count": 1592,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('train.csv')\n",
    "df_test = pd.read_csv('test.csv')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1593,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75751ad89b7f4f83829cb528678acbaf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Summarize dataset', max=25.0, style=ProgressStyle(descrip…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "model_id": "ce3aad923e8b43b0ac8fc6722371afb5",
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       "HBox(children=(FloatProgress(value=0.0, description='Generate report structure', max=1.0, style=ProgressStyle(…"
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
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       "model_id": "",
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       "HBox(children=(FloatProgress(value=0.0, description='Render widgets', max=1.0, style=ProgressStyle(description…"
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       "VBox(children=(Tab(children=(Tab(children=(GridBox(children=(VBox(children=(GridspecLayout(children=(HTML(valu…"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profile = ProfileReport(df_train,title='Detailed Customer Report',explorative=True)\n",
    "profile.to_widgets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1594,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate():\n",
    "    # f1 score\n",
    "    s1 = f1_score(y_test,y_pred_breed,average='weighted')\n",
    "    s2 = f1_score(y_test,y_pred_pet,average='weighted')\n",
    "    score = 100*((s1+s2)/2)\n",
    "    return score\n",
    "\n",
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "\n",
    "def generate_model_report(y_actual, y_predicted):\n",
    "    print(\"Accuracy = \" , accuracy_score(y_actual, y_predicted))\n",
    "    print(\"Precision = \" ,precision_score(y_actual, y_predicted,average='weighted'))\n",
    "    print(\"Recall = \" ,recall_score(y_actual, y_predicted,average='weighted'))\n",
    "    print(\"F1 Score = \" ,f1_score(y_actual, y_predicted,average='weighted'))\n",
    "    pass\n",
    "\n",
    "def generate_auc_roc_curve(y_test, y_score,n_classes):\n",
    "   # Compute ROC curve and ROC area for each class\n",
    "    fpr = dict()\n",
    "    tpr = dict()\n",
    "    roc_auc = dict()\n",
    "    for i in range(n_classes):\n",
    "        fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n",
    "        roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "\n",
    "    # Plot of a ROC curve for a specific class\n",
    "    for i in range(n_classes):\n",
    "        plt.figure()\n",
    "        plt.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f)' % roc_auc[i])\n",
    "        plt.plot([0, 1], [0, 1], 'k--')\n",
    "        plt.xlim([0.0, 1.0])\n",
    "        plt.ylim([0.0, 1.05])\n",
    "        plt.xlabel('False Positive Rate')\n",
    "        plt.ylabel('True Positive Rate')\n",
    "        plt.title('Receiver operating characteristic example')\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1595,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1) To clean the data and see which are the redundant or unnecessary cols\n",
    "def del_col(col,data):\n",
    "    clean_data = data.drop(col, axis=1)\n",
    "    return clean_data\n",
    "\n",
    "# 2) Dropping the duplicates from the dataset.\n",
    "def del_duplicates(data):\n",
    "    clean_data = data.drop_duplicates(keep='first')\n",
    "    return clean_data\n",
    "\n",
    "# 3) Imputing missing data\n",
    "def impute_col(data,filler):\n",
    "    data.fillna(filler,inplace=True)\n",
    "    return data\n",
    "\n",
    "# 4) Typecasting Variables\n",
    "def typecast_col(col,data,types):\n",
    "    clean_data = data.col.astype(types)\n",
    "    return clean_data\n",
    "  \n",
    "# 5) To Replace the spaces between the strings with '_' and also converting all strings to LowerCase\n",
    "def convert_case(col,data,chars):\n",
    "    data = data.str.replace(' ',chars) \n",
    "    data = data.str.lower() \n",
    "    return data\n",
    "\n",
    "# 6) Encoding using Label Encoder or OHE which converts categorical features to numerical features\n",
    "def label_encoder(data):\n",
    "    le = LabelEncoder()\n",
    "    data = le.fit_transform(data)\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1674,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing Unnecessary Columns\n",
    "X = del_col('pet_id',df_train)\n",
    "\n",
    "# Generating new feature \n",
    "X[['issue_date','listing_date']] = X[['issue_date','listing_date']].apply(pd.to_datetime) #if conversion required\n",
    "X['diff_days'] = (X['listing_date'] - X['issue_date']).dt.days\n",
    "X = del_col('issue_date',X)\n",
    "X = del_col('listing_date',X)\n",
    "\n",
    "# Imputing missing values with new category \n",
    "X['condition'] = impute_col(X['condition'],3.0)\n",
    "X['condition'] = X['condition'].astype('int')\n",
    "\n",
    "# Standardization - converting cm to mts\n",
    "# X['height(cm)'] = X['height(cm)']*0.01\n",
    "X['length(cm)'] = X['length(m)'].apply(lambda x: x*100)\n",
    "X = del_col('length(m)',X)\n",
    "# replace all 0 length with mean of lengths\n",
    "val = X['length(cm)'].mean()\n",
    "X['length(cm)'] = X['length(cm)'].replace(to_replace=0, value=val)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantile Based Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1645,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00      1.000000\n",
       "0.25     26.000000\n",
       "0.50     50.263566\n",
       "0.75     76.000000\n",
       "1.00    100.000000\n",
       "Name: length(cm), dtype: float64"
      ]
     },
     "execution_count": 1645,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantile_list = [0, .25, .5, .75, 1.]\n",
    "quantiles = X['length(cm)'].quantile(quantile_list)\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1646,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>breed_category</th>\n",
       "      <th>pet_category</th>\n",
       "      <th>diff_days</th>\n",
       "      <th>length(cm)</th>\n",
       "      <th>length_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Brown Tabby</td>\n",
       "      <td>7.78</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>80.0</td>\n",
       "      <td>75-100Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>White</td>\n",
       "      <td>14.19</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1862</td>\n",
       "      <td>72.0</td>\n",
       "      <td>50-75Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Brown</td>\n",
       "      <td>40.90</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>752</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0-25Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>White</td>\n",
       "      <td>17.82</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>755</td>\n",
       "      <td>62.0</td>\n",
       "      <td>50-75Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Black</td>\n",
       "      <td>11.06</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>50.0</td>\n",
       "      <td>25-50Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   condition   color_type  height(cm)  X1  X2  breed_category  pet_category  \\\n",
       "0          2  Brown Tabby        7.78  13   9             0.0             1   \n",
       "1          1        White       14.19  13   9             0.0             2   \n",
       "2          3        Brown       40.90  15   4             2.0             4   \n",
       "3          1        White       17.82   0   1             0.0             2   \n",
       "4          2        Black       11.06  18   4             0.0             1   \n",
       "\n",
       "   diff_days  length(cm) length_label  \n",
       "0         73        80.0      75-100Q  \n",
       "1       1862        72.0       50-75Q  \n",
       "2        752        15.0        0-25Q  \n",
       "3        755        62.0       50-75Q  \n",
       "4         52        50.0       25-50Q  "
      ]
     },
     "execution_count": 1646,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantile_labels = ['0-25Q', '25-50Q', '50-75Q', '75-100Q']\n",
    "X['length_label'] = pd.qcut(X['length(cm)'],q=quantile_list, labels=quantile_labels)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Based Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1647,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['diff_days'] = abs(X['diff_days'])\n",
    "X['diff_days'] =np.array(np.array(X['diff_days']) / 365.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1648,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding category using One Hot Encoding\n",
    "X = pd.concat([X,pd.get_dummies(X['condition'], prefix='condition')],axis=1)\n",
    "X = pd.concat([X,pd.get_dummies(X['X2'], prefix='X2')],axis=1)\n",
    "X = pd.concat([X,pd.get_dummies(X['X1'], prefix='X1')],axis=1)\n",
    "X = pd.concat([X,pd.get_dummies(X['color_type'], prefix='color_type')],axis=1)\n",
    "X = pd.concat([X,pd.get_dummies(X['length_label'], prefix='length_label')],axis=1)\n",
    "\n",
    "X = del_col('condition',X)\n",
    "X = del_col('color_type',X)\n",
    "X = del_col('X2',X)\n",
    "X = del_col('X1',X)\n",
    "X = del_col('length(cm)',X)\n",
    "X = del_col('length_label',X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1650,
   "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>height(cm)</th>\n",
       "      <th>breed_category</th>\n",
       "      <th>pet_category</th>\n",
       "      <th>diff_days</th>\n",
       "      <th>condition_0</th>\n",
       "      <th>condition_1</th>\n",
       "      <th>condition_2</th>\n",
       "      <th>condition_3</th>\n",
       "      <th>X2_0</th>\n",
       "      <th>X2_1</th>\n",
       "      <th>...</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>length_label_0-25Q</th>\n",
       "      <th>length_label_25-50Q</th>\n",
       "      <th>length_label_50-75Q</th>\n",
       "      <th>length_label_75-100Q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.78</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.19</td>\n",
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       "      <td>2</td>\n",
       "      <td>5.101370</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.90</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2.060274</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.82</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.068493</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.06</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.142466</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   height(cm)  breed_category  pet_category  diff_days  condition_0  \\\n",
       "0        7.78               0             1   0.200000            0   \n",
       "1       14.19               0             2   5.101370            0   \n",
       "2       40.90               2             4   2.060274            0   \n",
       "3       17.82               0             2   2.068493            0   \n",
       "4       11.06               0             1   0.142466            0   \n",
       "\n",
       "   condition_1  condition_2  condition_3  X2_0  X2_1  ...  color_type_Tortie  \\\n",
       "0            0            1            0     0     0  ...                  0   \n",
       "1            1            0            0     0     0  ...                  0   \n",
       "2            0            0            1     0     0  ...                  0   \n",
       "3            1            0            0     0     1  ...                  0   \n",
       "4            0            1            0     0     0  ...                  0   \n",
       "\n",
       "   color_type_Tortie Point  color_type_Tricolor  color_type_White  \\\n",
       "0                        0                    0                 0   \n",
       "1                        0                    0                 1   \n",
       "2                        0                    0                 0   \n",
       "3                        0                    0                 1   \n",
       "4                        0                    0                 0   \n",
       "\n",
       "   color_type_Yellow  color_type_Yellow Brindle  length_label_0-25Q  \\\n",
       "0                  0                          0                   0   \n",
       "1                  0                          0                   0   \n",
       "2                  0                          0                   1   \n",
       "3                  0                          0                   0   \n",
       "4                  0                          0                   0   \n",
       "\n",
       "   length_label_25-50Q  length_label_50-75Q  length_label_75-100Q  \n",
       "0                    0                    0                     1  \n",
       "1                    0                    1                     0  \n",
       "2                    0                    0                     0  \n",
       "3                    0                    1                     0  \n",
       "4                    1                    0                     0  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
     "execution_count": 1650,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['breed_category'] = X['breed_category'].astype('int')\n",
    "X['pet_category'] = X['pet_category'].astype('int')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1651,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y1 = X['breed_category']\n",
    "Y2 = X['pet_category']\n",
    "\n",
    "#Splitting up for MultiLabel Classification\n",
    "X1 = X.drop(['pet_category','breed_category'], axis=1)\n",
    "X2 = X.drop(['pet_category','breed_category'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1652,
   "metadata": {},
   "outputs": [
    {
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       "      <th>color_type_Yellow</th>\n",
       "      <th>color_type_Yellow Brindle</th>\n",
       "      <th>length_label_0-25Q</th>\n",
       "      <th>length_label_25-50Q</th>\n",
       "      <th>length_label_50-75Q</th>\n",
       "      <th>length_label_75-100Q</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.82</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   height(cm)  diff_days  condition_0  condition_1  condition_2  condition_3  \\\n",
       "0        7.78   0.200000            0            0            1            0   \n",
       "1       14.19   5.101370            0            1            0            0   \n",
       "2       40.90   2.060274            0            0            0            1   \n",
       "3       17.82   2.068493            0            1            0            0   \n",
       "4       11.06   0.142466            0            0            1            0   \n",
       "\n",
       "   X2_0  X2_1  X2_2  X2_3  ...  color_type_Tortie  color_type_Tortie Point  \\\n",
       "0     0     0     0     0  ...                  0                        0   \n",
       "1     0     0     0     0  ...                  0                        0   \n",
       "2     0     0     0     0  ...                  0                        0   \n",
       "3     0     1     0     0  ...                  0                        0   \n",
       "4     0     0     0     0  ...                  0                        0   \n",
       "\n",
       "   color_type_Tricolor  color_type_White  color_type_Yellow  \\\n",
       "0                    0                 0                  0   \n",
       "1                    0                 1                  0   \n",
       "2                    0                 0                  0   \n",
       "3                    0                 1                  0   \n",
       "4                    0                 0                  0   \n",
       "\n",
       "   color_type_Yellow Brindle  length_label_0-25Q  length_label_25-50Q  \\\n",
       "0                          0                   0                    0   \n",
       "1                          0                   0                    0   \n",
       "2                          0                   1                    0   \n",
       "3                          0                   0                    0   \n",
       "4                          0                   0                    1   \n",
       "\n",
       "   length_label_50-75Q  length_label_75-100Q  \n",
       "0                    0                     1  \n",
       "1                    1                     0  \n",
       "2                    0                     0  \n",
       "3                    1                     0  \n",
       "4                    0                     0  \n",
       "\n",
       "[5 rows x 96 columns]"
      ]
     },
     "execution_count": 1652,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1653,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.90</td>\n",
       "      <td>2.060274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.82</td>\n",
       "      <td>2.068493</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.06</td>\n",
       "      <td>0.142466</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   height(cm)  diff_days  condition_0  condition_1  condition_2  condition_3  \\\n",
       "0        7.78   0.200000            0            0            1            0   \n",
       "1       14.19   5.101370            0            1            0            0   \n",
       "2       40.90   2.060274            0            0            0            1   \n",
       "3       17.82   2.068493            0            1            0            0   \n",
       "4       11.06   0.142466            0            0            1            0   \n",
       "\n",
       "   X2_0  X2_1  X2_2  X2_3  ...  color_type_Tortie  color_type_Tortie Point  \\\n",
       "0     0     0     0     0  ...                  0                        0   \n",
       "1     0     0     0     0  ...                  0                        0   \n",
       "2     0     0     0     0  ...                  0                        0   \n",
       "3     0     1     0     0  ...                  0                        0   \n",
       "4     0     0     0     0  ...                  0                        0   \n",
       "\n",
       "   color_type_Tricolor  color_type_White  color_type_Yellow  \\\n",
       "0                    0                 0                  0   \n",
       "1                    0                 1                  0   \n",
       "2                    0                 0                  0   \n",
       "3                    0                 1                  0   \n",
       "4                    0                 0                  0   \n",
       "\n",
       "   color_type_Yellow Brindle  length_label_0-25Q  length_label_25-50Q  \\\n",
       "0                          0                   0                    0   \n",
       "1                          0                   0                    0   \n",
       "2                          0                   1                    0   \n",
       "3                          0                   0                    0   \n",
       "4                          0                   0                    1   \n",
       "\n",
       "   length_label_50-75Q  length_label_75-100Q  \n",
       "0                    0                     1  \n",
       "1                    1                     0  \n",
       "2                    0                     0  \n",
       "3                    1                     0  \n",
       "4                    0                     0  \n",
       "\n",
       "[5 rows x 96 columns]"
      ]
     },
     "execution_count": 1653,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SMOTE Analysis for highly imbalaced class Balancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1654,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE('auto',random_state=42)\n",
    "X_train1_smote,y_train1_smote = smote.fit_resample(X1,Y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1655,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote1 = SMOTE('auto',random_state=42)\n",
    "X_train2_smote,y_train2_smote = smote1.fit_resample(X2,Y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1656,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before SMOTE : Counter({0: 9000, 1: 8357, 2: 1477})\n",
      "Before SMOTE : Counter({0: 9000, 2: 9000, 1: 9000})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "print(\"Before SMOTE :\", Counter(Y1))\n",
    "print(\"Before SMOTE :\", Counter(y_train1_smote))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1657,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before SMOTE : Counter({2: 10621, 1: 7184, 4: 941, 0: 88})\n",
      "Before SMOTE : Counter({1: 10621, 2: 10621, 4: 10621, 0: 10621})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "print(\"Before SMOTE :\", Counter(Y2))\n",
    "print(\"Before SMOTE :\", Counter(y_train2_smote))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoost Classifier used with different set of parameters separately "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model to predict Breed category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1658,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 1.0052118\ttotal: 7.72ms\tremaining: 7.71s\n",
      "100:\tlearn: 0.1290996\ttotal: 771ms\tremaining: 6.87s\n",
      "200:\tlearn: 0.1173195\ttotal: 1.67s\tremaining: 6.65s\n",
      "300:\tlearn: 0.1151242\ttotal: 2.35s\tremaining: 5.47s\n",
      "400:\tlearn: 0.1141922\ttotal: 2.98s\tremaining: 4.45s\n",
      "500:\tlearn: 0.1130310\ttotal: 3.6s\tremaining: 3.58s\n",
      "600:\tlearn: 0.1120369\ttotal: 4.21s\tremaining: 2.79s\n",
      "700:\tlearn: 0.1114967\ttotal: 4.83s\tremaining: 2.06s\n",
      "800:\tlearn: 0.1110384\ttotal: 5.47s\tremaining: 1.36s\n",
      "900:\tlearn: 0.1106872\ttotal: 6.09s\tremaining: 669ms\n",
      "999:\tlearn: 0.1102725\ttotal: 6.71s\tremaining: 0us\n",
      "fold 0 f1 0.9281846282965027\n",
      "[[1167  158    0]\n",
      " [ 119 1128    0]\n",
      " [   0    0 1286]]\n",
      "0:\tlearn: 1.0056277\ttotal: 6.59ms\tremaining: 6.59s\n",
      "100:\tlearn: 0.1272219\ttotal: 628ms\tremaining: 5.59s\n",
      "200:\tlearn: 0.1184157\ttotal: 1.25s\tremaining: 4.97s\n",
      "300:\tlearn: 0.1158300\ttotal: 1.86s\tremaining: 4.31s\n",
      "400:\tlearn: 0.1147107\ttotal: 2.48s\tremaining: 3.71s\n",
      "500:\tlearn: 0.1137572\ttotal: 3.1s\tremaining: 3.09s\n",
      "600:\tlearn: 0.1131962\ttotal: 3.72s\tremaining: 2.47s\n",
      "700:\tlearn: 0.1126118\ttotal: 4.33s\tremaining: 1.85s\n",
      "800:\tlearn: 0.1122453\ttotal: 4.96s\tremaining: 1.23s\n",
      "900:\tlearn: 0.1118239\ttotal: 5.58s\tremaining: 614ms\n",
      "999:\tlearn: 0.1113637\ttotal: 6.19s\tremaining: 0us\n",
      "fold 1 f1 0.9351809998332556\n",
      "[[1154  118    0]\n",
      " [ 132 1167    0]\n",
      " [   0    0 1286]]\n",
      "0:\tlearn: 1.0055224\ttotal: 7.14ms\tremaining: 7.13s\n",
      "100:\tlearn: 0.1294245\ttotal: 1.1s\tremaining: 9.77s\n",
      "200:\tlearn: 0.1184063\ttotal: 1.78s\tremaining: 7.08s\n",
      "300:\tlearn: 0.1164783\ttotal: 2.54s\tremaining: 5.9s\n",
      "400:\tlearn: 0.1151273\ttotal: 3.35s\tremaining: 5.01s\n",
      "500:\tlearn: 0.1142005\ttotal: 4.01s\tremaining: 4s\n",
      "600:\tlearn: 0.1133646\ttotal: 4.63s\tremaining: 3.07s\n",
      "700:\tlearn: 0.1126956\ttotal: 5.33s\tremaining: 2.27s\n",
      "800:\tlearn: 0.1123132\ttotal: 6.17s\tremaining: 1.53s\n",
      "900:\tlearn: 0.1117053\ttotal: 6.83s\tremaining: 751ms\n",
      "999:\tlearn: 0.1113581\ttotal: 7.47s\tremaining: 0us\n",
      "fold 2 f1 0.9315440104852389\n",
      "[[1139  117    0]\n",
      " [ 147 1168    0]\n",
      " [   0    0 1286]]\n",
      "0:\tlearn: 1.0050757\ttotal: 6.71ms\tremaining: 6.71s\n",
      "100:\tlearn: 0.1281538\ttotal: 622ms\tremaining: 5.54s\n",
      "200:\tlearn: 0.1174808\ttotal: 1.25s\tremaining: 4.97s\n",
      "300:\tlearn: 0.1156284\ttotal: 1.87s\tremaining: 4.34s\n",
      "400:\tlearn: 0.1146465\ttotal: 2.48s\tremaining: 3.71s\n",
      "500:\tlearn: 0.1140270\ttotal: 3.1s\tremaining: 3.09s\n",
      "600:\tlearn: 0.1133669\ttotal: 4s\tremaining: 2.66s\n",
      "700:\tlearn: 0.1124856\ttotal: 4.64s\tremaining: 1.98s\n",
      "800:\tlearn: 0.1120360\ttotal: 5.41s\tremaining: 1.34s\n",
      "900:\tlearn: 0.1116115\ttotal: 6.48s\tremaining: 712ms\n",
      "999:\tlearn: 0.1112298\ttotal: 7.24s\tremaining: 0us\n",
      "fold 3 f1 0.932849273432297\n",
      "[[1155  128    0]\n",
      " [ 131 1158    0]\n",
      " [   0    0 1285]]\n",
      "0:\tlearn: 1.0052267\ttotal: 6.67ms\tremaining: 6.66s\n",
      "100:\tlearn: 0.1284044\ttotal: 970ms\tremaining: 8.64s\n",
      "200:\tlearn: 0.1185586\ttotal: 1.75s\tremaining: 6.98s\n",
      "300:\tlearn: 0.1167497\ttotal: 2.52s\tremaining: 5.85s\n",
      "400:\tlearn: 0.1154015\ttotal: 3.2s\tremaining: 4.78s\n",
      "500:\tlearn: 0.1144058\ttotal: 4.04s\tremaining: 4.03s\n",
      "600:\tlearn: 0.1138960\ttotal: 4.72s\tremaining: 3.14s\n",
      "700:\tlearn: 0.1132293\ttotal: 5.37s\tremaining: 2.29s\n",
      "800:\tlearn: 0.1128385\ttotal: 6.57s\tremaining: 1.63s\n",
      "900:\tlearn: 0.1124387\ttotal: 7.3s\tremaining: 802ms\n",
      "999:\tlearn: 0.1120901\ttotal: 7.97s\tremaining: 0us\n",
      "fold 4 f1 0.9310344410572506\n",
      "[[1154  134    0]\n",
      " [ 132 1152    0]\n",
      " [   0    0 1285]]\n",
      "0:\tlearn: 1.0051604\ttotal: 6.76ms\tremaining: 6.76s\n",
      "100:\tlearn: 0.1280854\ttotal: 625ms\tremaining: 5.57s\n",
      "200:\tlearn: 0.1170181\ttotal: 1.32s\tremaining: 5.24s\n",
      "300:\tlearn: 0.1150343\ttotal: 1.98s\tremaining: 4.6s\n",
      "400:\tlearn: 0.1139941\ttotal: 2.62s\tremaining: 3.91s\n",
      "500:\tlearn: 0.1131459\ttotal: 3.29s\tremaining: 3.27s\n",
      "600:\tlearn: 0.1123390\ttotal: 3.93s\tremaining: 2.61s\n",
      "700:\tlearn: 0.1118583\ttotal: 4.57s\tremaining: 1.95s\n",
      "800:\tlearn: 0.1112678\ttotal: 5.22s\tremaining: 1.3s\n",
      "900:\tlearn: 0.1106728\ttotal: 5.85s\tremaining: 643ms\n",
      "999:\tlearn: 0.1102480\ttotal: 6.53s\tremaining: 0us\n",
      "fold 5 f1 0.9278859228247563\n",
      "[[1117  110    0]\n",
      " [ 168 1176    0]\n",
      " [   0    0 1286]]\n",
      "0:\tlearn: 1.0055250\ttotal: 6.67ms\tremaining: 6.67s\n",
      "100:\tlearn: 0.1281449\ttotal: 639ms\tremaining: 5.69s\n",
      "200:\tlearn: 0.1176828\ttotal: 1.27s\tremaining: 5.07s\n",
      "300:\tlearn: 0.1156810\ttotal: 1.89s\tremaining: 4.4s\n",
      "400:\tlearn: 0.1146246\ttotal: 2.51s\tremaining: 3.75s\n",
      "500:\tlearn: 0.1138119\ttotal: 3.13s\tremaining: 3.12s\n",
      "600:\tlearn: 0.1131006\ttotal: 3.79s\tremaining: 2.51s\n",
      "700:\tlearn: 0.1124117\ttotal: 4.44s\tremaining: 1.9s\n",
      "800:\tlearn: 0.1118365\ttotal: 5.08s\tremaining: 1.26s\n",
      "900:\tlearn: 0.1113066\ttotal: 5.75s\tremaining: 632ms\n",
      "999:\tlearn: 0.1108444\ttotal: 6.42s\tremaining: 0us\n",
      "fold 6 f1 0.9325823853103589\n",
      "[[1169  144    0]\n",
      " [ 116 1142    0]\n",
      " [   0    0 1286]]\n",
      "Mean f1 score 0.9313230944628085\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "categorical_features_indices=[0]\n",
    "rf1_2 = CatBoostClassifier(learning_rate=0.055, \n",
    "                          n_estimators=1000, \n",
    "                          subsample=0.075, \n",
    "                          max_depth=3, \n",
    "                          verbose=100,\n",
    "                          l2_leaf_reg = 7,\n",
    "                          bootstrap_type=\"Bernoulli\",\n",
    "                          class_weights=[1, 1, 1],\n",
    "                          loss_function='MultiClass')\n",
    "#                           eval_metric='F1')\n",
    "\n",
    "kf = StratifiedKFold(n_splits=7,shuffle=True,random_state=99)\n",
    "f1 = []\n",
    "\n",
    "for fold,(t_id,v_id) in enumerate(kf.split(X_train1_smote,y_train1_smote)):\n",
    "    tx = X_train1_smote.iloc[t_id]; ty = y_train1_smote.iloc[t_id]\n",
    "    vx = X_train1_smote.iloc[v_id]; vy = y_train1_smote.iloc[v_id]\n",
    "    rf1_2.fit(tx,\n",
    "              ty,\n",
    "#               cat_features = categorical_features_indices\n",
    "          )\n",
    "           \n",
    "    val_y = rf1_2.predict(vx)\n",
    "    F1_score = f1_score(vy, val_y,average='weighted')\n",
    "    f1.append(F1_score)\n",
    "    print(f\"fold {fold} f1 {F1_score}\")\n",
    "    print(confusion_matrix(val_y, vy))\n",
    "\n",
    "print(f\"Mean f1 score {np.mean(f1)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model to predict Pet category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1659,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 1.3517286\ttotal: 13.4ms\tremaining: 13.4s\n",
      "100:\tlearn: 0.6032447\ttotal: 1.37s\tremaining: 12.2s\n",
      "200:\tlearn: 0.4918655\ttotal: 2.69s\tremaining: 10.7s\n",
      "300:\tlearn: 0.4353542\ttotal: 3.93s\tremaining: 9.12s\n",
      "400:\tlearn: 0.3999216\ttotal: 5.15s\tremaining: 7.69s\n",
      "500:\tlearn: 0.3738688\ttotal: 6.38s\tremaining: 6.36s\n",
      "600:\tlearn: 0.3516089\ttotal: 7.63s\tremaining: 5.07s\n",
      "700:\tlearn: 0.3346503\ttotal: 8.88s\tremaining: 3.79s\n",
      "800:\tlearn: 0.3216373\ttotal: 10.1s\tremaining: 2.51s\n",
      "900:\tlearn: 0.3087230\ttotal: 11.2s\tremaining: 1.24s\n",
      "999:\tlearn: 0.2980919\ttotal: 12.4s\tremaining: 0us\n",
      "fold 0 f1 0.9058339402791528\n",
      "[[1485   21   11  139]\n",
      " [  11 1298   84   41]\n",
      " [   2  178 1414   33]\n",
      " [  20   21    8 1304]]\n",
      "0:\tlearn: 1.3533088\ttotal: 12.7ms\tremaining: 12.7s\n",
      "100:\tlearn: 0.6003055\ttotal: 1.14s\tremaining: 10.2s\n",
      "200:\tlearn: 0.4879639\ttotal: 2.28s\tremaining: 9.05s\n",
      "300:\tlearn: 0.4323917\ttotal: 3.7s\tremaining: 8.6s\n",
      "400:\tlearn: 0.3987107\ttotal: 4.87s\tremaining: 7.28s\n",
      "500:\tlearn: 0.3724793\ttotal: 6.04s\tremaining: 6.01s\n",
      "600:\tlearn: 0.3512564\ttotal: 7.26s\tremaining: 4.82s\n",
      "700:\tlearn: 0.3357248\ttotal: 8.44s\tremaining: 3.6s\n",
      "800:\tlearn: 0.3218819\ttotal: 9.63s\tremaining: 2.39s\n",
      "900:\tlearn: 0.3105887\ttotal: 10.8s\tremaining: 1.19s\n",
      "999:\tlearn: 0.3002073\ttotal: 12s\tremaining: 0us\n",
      "fold 1 f1 0.9049640365380719\n",
      "[[1464   23    8  138]\n",
      " [   6 1317  112   33]\n",
      " [   1  154 1390   23]\n",
      " [  46   24    7 1323]]\n",
      "0:\tlearn: 1.3531222\ttotal: 12.5ms\tremaining: 12.5s\n",
      "100:\tlearn: 0.6009829\ttotal: 1.14s\tremaining: 10.1s\n",
      "200:\tlearn: 0.4889074\ttotal: 2.26s\tremaining: 9s\n",
      "300:\tlearn: 0.4336868\ttotal: 3.39s\tremaining: 7.87s\n",
      "400:\tlearn: 0.3988830\ttotal: 4.51s\tremaining: 6.73s\n",
      "500:\tlearn: 0.3731151\ttotal: 5.84s\tremaining: 5.82s\n",
      "600:\tlearn: 0.3503441\ttotal: 7.22s\tremaining: 4.79s\n",
      "700:\tlearn: 0.3341715\ttotal: 8.63s\tremaining: 3.68s\n",
      "800:\tlearn: 0.3199649\ttotal: 10.2s\tremaining: 2.54s\n",
      "900:\tlearn: 0.3088297\ttotal: 11.6s\tremaining: 1.28s\n",
      "999:\tlearn: 0.2986857\ttotal: 12.8s\tremaining: 0us\n",
      "fold 2 f1 0.9024616421500459\n",
      "[[1467   11    4  146]\n",
      " [  17 1311  103   34]\n",
      " [   1  173 1406   42]\n",
      " [  32   22    5 1295]]\n",
      "0:\tlearn: 1.3524184\ttotal: 14ms\tremaining: 14s\n",
      "100:\tlearn: 0.5982668\ttotal: 1.3s\tremaining: 11.6s\n",
      "200:\tlearn: 0.4914634\ttotal: 2.58s\tremaining: 10.3s\n",
      "300:\tlearn: 0.4326163\ttotal: 3.84s\tremaining: 8.92s\n",
      "400:\tlearn: 0.3965416\ttotal: 5.31s\tremaining: 7.93s\n",
      "500:\tlearn: 0.3706040\ttotal: 6.56s\tremaining: 6.53s\n",
      "600:\tlearn: 0.3484609\ttotal: 8.3s\tremaining: 5.51s\n",
      "700:\tlearn: 0.3316853\ttotal: 9.74s\tremaining: 4.15s\n",
      "800:\tlearn: 0.3177710\ttotal: 11.1s\tremaining: 2.75s\n",
      "900:\tlearn: 0.3062700\ttotal: 12.3s\tremaining: 1.35s\n",
      "999:\tlearn: 0.2962243\ttotal: 13.7s\tremaining: 0us\n",
      "fold 3 f1 0.9015768801705897\n",
      "[[1455   13    9  146]\n",
      " [  10 1316  118   24]\n",
      " [   3  166 1386   31]\n",
      " [  49   22    5 1316]]\n",
      "0:\tlearn: 1.3550297\ttotal: 12ms\tremaining: 12s\n",
      "100:\tlearn: 0.5993462\ttotal: 1.14s\tremaining: 10.2s\n",
      "200:\tlearn: 0.4891105\ttotal: 2.57s\tremaining: 10.2s\n",
      "300:\tlearn: 0.4332650\ttotal: 3.85s\tremaining: 8.94s\n",
      "400:\tlearn: 0.3964394\ttotal: 5.08s\tremaining: 7.59s\n",
      "500:\tlearn: 0.3708518\ttotal: 6.48s\tremaining: 6.45s\n",
      "600:\tlearn: 0.3494568\ttotal: 7.82s\tremaining: 5.19s\n",
      "700:\tlearn: 0.3316514\ttotal: 9.1s\tremaining: 3.88s\n",
      "800:\tlearn: 0.3168793\ttotal: 10.5s\tremaining: 2.6s\n",
      "900:\tlearn: 0.3051640\ttotal: 11.8s\tremaining: 1.3s\n",
      "999:\tlearn: 0.2959368\ttotal: 13.2s\tremaining: 0us\n",
      "fold 4 f1 0.8930958548705966\n",
      "[[1468   18    5  135]\n",
      " [  16 1269  135   21]\n",
      " [   1  202 1363   40]\n",
      " [  32   28   14 1322]]\n",
      "0:\tlearn: 1.3538919\ttotal: 14.6ms\tremaining: 14.6s\n",
      "100:\tlearn: 0.6018557\ttotal: 1.35s\tremaining: 12s\n",
      "200:\tlearn: 0.4933624\ttotal: 2.67s\tremaining: 10.6s\n",
      "300:\tlearn: 0.4346985\ttotal: 4.25s\tremaining: 9.86s\n",
      "400:\tlearn: 0.3996792\ttotal: 5.56s\tremaining: 8.3s\n",
      "500:\tlearn: 0.3725685\ttotal: 6.86s\tremaining: 6.83s\n",
      "600:\tlearn: 0.3499253\ttotal: 8.15s\tremaining: 5.41s\n",
      "700:\tlearn: 0.3338465\ttotal: 9.49s\tremaining: 4.05s\n",
      "800:\tlearn: 0.3206943\ttotal: 10.8s\tremaining: 2.68s\n",
      "900:\tlearn: 0.3084724\ttotal: 12.1s\tremaining: 1.33s\n",
      "999:\tlearn: 0.2988160\ttotal: 13.4s\tremaining: 0us\n",
      "fold 5 f1 0.90317518942538\n",
      "[[1471   16    5  146]\n",
      " [   8 1305   90   37]\n",
      " [   3  171 1408   35]\n",
      " [  35   25   14 1300]]\n",
      "0:\tlearn: 1.3521539\ttotal: 13.5ms\tremaining: 13.5s\n",
      "100:\tlearn: 0.5989160\ttotal: 1.32s\tremaining: 11.8s\n",
      "200:\tlearn: 0.4911569\ttotal: 2.64s\tremaining: 10.5s\n",
      "300:\tlearn: 0.4325282\ttotal: 3.96s\tremaining: 9.19s\n",
      "400:\tlearn: 0.3991115\ttotal: 5.27s\tremaining: 7.88s\n",
      "500:\tlearn: 0.3718759\ttotal: 6.55s\tremaining: 6.52s\n",
      "600:\tlearn: 0.3508948\ttotal: 7.94s\tremaining: 5.27s\n",
      "700:\tlearn: 0.3344943\ttotal: 8.93s\tremaining: 3.81s\n",
      "800:\tlearn: 0.3199595\ttotal: 9.91s\tremaining: 2.46s\n",
      "900:\tlearn: 0.3090949\ttotal: 10.9s\tremaining: 1.2s\n",
      "999:\tlearn: 0.2995727\ttotal: 11.8s\tremaining: 0us\n",
      "fold 6 f1 0.8973543924414661\n",
      "[[1468   20   13  142]\n",
      " [   9 1298  121   36]\n",
      " [   2  175 1372   29]\n",
      " [  39   24   11 1310]]\n",
      "Mean f1 score 0.9012088479821861\n"
     ]
    }
   ],
   "source": [
    "categorical_features_indices=[0]\n",
    "from catboost import CatBoostClassifier\n",
    "rf2_2 = CatBoostClassifier(learning_rate=0.035, \n",
    "                          n_estimators=1000, \n",
    "                          subsample=0.075, \n",
    "                          max_depth=4,\n",
    "                          l2_leaf_reg = 40,\n",
    "                          verbose=100,\n",
    "                          bootstrap_type=\"Bernoulli\",\n",
    "                          class_weights=[1, 1, 1, 1],\n",
    "                          loss_function='MultiClass')\n",
    "\n",
    "kf = StratifiedKFold(n_splits=7,shuffle=True,random_state=99)\n",
    "f1 = []\n",
    "\n",
    "for fold,(t_id,v_id) in enumerate(kf.split(X_train2_smote,y_train2_smote)):\n",
    "    tx = X_train2_smote.iloc[t_id]; ty = y_train2_smote.iloc[t_id]\n",
    "    vx = X_train2_smote.iloc[v_id]; vy = y_train2_smote.iloc[v_id]\n",
    "    rf2_2.fit(tx,ty,\n",
    "#     cat_features = categorical_features_indices,\n",
    "          )\n",
    "           \n",
    "    val_y = rf2_2.predict(vx)\n",
    "    F1_score = f1_score(vy, val_y,average='weighted')\n",
    "    f1.append(F1_score)\n",
    "    print(f\"fold {fold} f1 {F1_score}\")\n",
    "    print(confusion_matrix(val_y, vy))\n",
    "\n",
    "print(f\"Mean f1 score {np.mean(f1)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Interpretation and Comparative analysis of Model Feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1677,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skater.core.explanations import Interpretation\n",
    "from skater.model import InMemoryModel\n",
    "\n",
    "def feature_imp(X_train,X_test,feature_names,model):\n",
    "    \n",
    "    # leveraging skater for feature importances\n",
    "    interpreter = Interpretation(X_test, feature_names=feature_names)\n",
    "    im_model = InMemoryModel(model.predict_proba, examples=X_train, target_names=model.classes_)\n",
    "    \n",
    "    # retrieving feature importances from the scikit-learn estimator\n",
    "    feature_importances = model.feature_importances_\n",
    "    feature_names, feature_scores = zip(*sorted(zip(feature_names, feature_importances), \n",
    "                                                          key=lambda x: x[1]))\n",
    "    # plot the feature importance plots\n",
    "    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(40, 96))\n",
    "    t = f.suptitle('Feature Importances for Random Forest', fontsize=32)\n",
    "    f.subplots_adjust(top=0.85, wspace=0.6)\n",
    "    y_position = list(range(len(feature_names)))\n",
    "    ax1.barh(y_position, feature_scores, height=0.6, align='center', tick_label=feature_names)\n",
    "    ax1.set_title(\"Scikit-Learn\")\n",
    "    ax1.set_xlabel('Relative Importance Score')\n",
    "    ax1.set_ylabel('Feature')\n",
    "    plots = interpreter.feature_importance.plot_feature_importance(im_model, ascending=False, ax=ax2)\n",
    "    ax2.set_title(\"Skater\")\n",
    "    ax2.set_xlabel('Relative Importance Score')\n",
    "    ax2.set_ylabel('Feature')\n",
    "    pass "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1678,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[96/96] features ████████████████████ Time elapsed: 7 seconds"
     ]
    },
    {
     "data": {
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sOOWUU+L777+PiIgmTZpEx44dY6eddoqZM2fG888/H7Nnz44bb7yx1M++pY/jXXfdFTNmzIhatWrFiSeeGI0aNYr58+fH888/HzNmzIhly5bFddddFw0bNox33nknGjRoECeccELUq1cvZs6cGU899VQsWLAgvv7667j11lujd+/epW7/D+GVV16JSy+9NBt+rv/e7bTTTrFgwYIYNmxYfPzxx5GbmxvnnXde3HHHHXHEEUeUuL6ePXvGypUro2PHjnHggQfGwoULY+TIkYVG2bvmmmti4MCBERFRvXr1OP7446Np06ZRsWLF+OKLL2LQoEHx9ddfx5tvvhlnnHFGPPvss4UCy9WrV8f555+fDa4OPvjgaNeuXeywww6Rl5cXn3zySQwZMiRWr14djz/+eDRo0CD++Mc/RsSP+1sPAADAJsgAAADAL8wLL7yQycnJyeTk5GSuuOKKzVrXl19+mWnatGkmJycn07Jly8zHH39c7HKPPfZY9j1PPPHEIvMvv/zy7Py77rqr2HWsXLkyc9ppp2WX69mzZ6mf7YUXXigyf9asWdn5p512Wqmf7a677ipxXaNHj87O29h+HDNmTKZx48aZnJyczGGHHZaZNm1akWXWrFmTuemmm7Lru+iii0rdttIU3K6NzW/WrFlmzJgxhebn5+dnzjzzzELLtW/fPvPNN98UWm7lypWZTp06ZZf54IMPCs3fcB81b968yHtlMpnMM888k13mwAMPzCxevLjQ/Mcffzw7/9BDD81MnTq1yDq+//77TLdu3bLLnX/++UWWKXju/O53vyv2OKxX8NiPHj26yPzp06dnj2mbNm2K7Jv1Ro4cmWnUqFEmJycn06RJk8x3331XaH7B83X9OZmXl1dkPf/7v/+bXaZjx45F5k+YMCHTpEmTTE5OTmbffffNfPjhh0WWmTNnTuaggw7KrmfUqFHZeVviHJ00aVJ23llnnZXJz88vso5JkyZlmjVrlj33Vq1aVex+K03B86q4a8Xy5cszBx98cHaZ2267rdhtGTZsWGbvvffOHptPP/20xPfZEtfKgusr7dqzZs2aTG5ubmbSpEmZRx99NNOmTZvs69q2bZv59ttvi7zm9ttvzy5z5ZVXZtauXVtkmbVr12Yuu+yy7HIXXHBBkWUKfkdycnIyjz32WJFlcnNzMx06dMgu8/jjjxdZ5txzz83Ov+666zJr1qwpNH/58uWFlinuWG6t43jkkUcW+b7m5eVlWrZsWWi5888/P7Ny5cpCy82ePTt7/u69995F5pdFwd+gdu3aJb++oPnz52f22WefTE5OTqZRo0aZJ554otjlnnjiiex1qFmzZpm5c+cWmr/hdaik3+BMJpP5z3/+k12uU6dOxV77VqxYkbnooosKHbuCXn311ey8a665ptj3eeedd7LXpPbt2xeZv7HfegAAAH4aih+rHQAAAH4hXnzxxWjUqFGZ/tO3b98ir3/44YdjxYoVERFx0003RfPmzYt9nzPPPDM7qsqECRPivffey85btWpVvPbaaxERsfPOO5d4y6lKlSrFOeeck308ffr0TfrMW8OFF15Y4rx777031q5dG+XKlYu777672NsrVqhQIa688srYa6+9IiJi2LBhP8jnO/vss6NFixaFnitfvnx2VJH1brjhhiK3k6tUqVJ07tw5+7i425oVdP311xd5r4iIrl27RqdOnSJi3W3+Xnjhhey8NWvWxAMPPBAREeXKlYu77rorfvvb3xZZR40aNeKee+7JbuNrr70WkyZNKnFbjjrqqBJvc1kWw4YNy44sc95555V428CDDz44mjZtGhHrRuyaOXNmieusWLFi9OrVq9BtvNa74IILYptttomIdbf62vD2f48++mj2Nl9XX3117L///kXWUa9evbj66quzjwveYm1LnKMFp9u2bVvsLRAbN24cxx13XOTk5ESrVq1i4cKFxe+MzfD8889nR3xq27Zt/P3vfy92W4444oi4+OKLI2Ldsfn3v/9d6npL+46n+vDDD0u8zu65557RsmXLOP744+OWW26J+fPnR8S60aKeeOKJIqNKRUR2VLbKlSvHFVdcUeyt2MqVK1fo2rqx68shhxxS7KhRNWvWjLPOOiv7ePz48YXmT5s2LXuLzD322COuv/76qFChQqFlKleuHL169Sr1dptb6zgWdy3bbrvt4phjjsk+rlq1atx8881Fbn/5q1/9Kg4++OCIWPe7NWPGjFLfa2PmzJlT5t/f888/v8jrH3nkkVi+fHlERJxyyilx+umnF/s+p59+epxyyikREbFs2bJ45JFHStymKlWqxNlnn13svLVr12ZH66tatWrcd999xR7DbbfdNnr27Bm77LJLREQ888wzkZeXl51f8Nw77LDDin2vNm3axKGHHhpNmjSJnJyc7H/PAAAA4OdFdAUAAAAlWLt2bbzyyisREVGnTp34/e9/X+ryBUOe9beKi1gX77z22mvx3HPPxd13313iLeoiInbdddfs9E/lj7D16tWL+vXrFztv0aJFMWrUqIhYd8uv9bdDLE758uWzt43LZDLZcGFrOv7444t9vuDtH7fbbrvsrZs2tP72ZRFR6I/qxS1X0ntFRPzpT3/KTr/xxhvZ6U8++SQb5hx44IHZgKk4NWrUiNNOOy37eNiwYSUuW1yUlOLss8+OYcOGxUMPPRTHHntsqcsWPGfXBxLF2XfffUuMUKpUqRK/+c1vImLduVFwX69atSpGjhwZERG1a9cudT8ffvjhccEFF8Stt96aDWq21Dla8HaOzz//fIlB1T//+c8YMmRI3H///VG3bt0S32tTFby2nHvuuaUue+qpp0bNmjUjImLkyJElHp/SvuNbU82aNaNdu3bRp0+fGDhwYKFzqaDnn38+XnjhhXjooYdKvS1pyvXzyCOPLHFe48aNs9O5ubmF5hX8/v7xj38s8XpevXr1InFnQVvjONatWzdatmxZ7LyC+6Z169bFxo8Rha9562+f+GNZv4/Kly8f3bt3L3XZc889Nxu/DR8+vMTl9t5776hatWqx8yZOnJgNzQ499NBSo7mqVatmb7+5bNmy7C1rI6JQOPjkk0/GkiVLil3H/fffH4MGDYq77747KleuXOJ7AQAA8NNV8r/yAgAAwC9Aq1atolu3bmVaNicnp9DjKVOmZP9YWr169UJ/JC9OwT9Qbzg6yg477BA77LBDsa/Lz8+Pr776KiZOnBhvv/12oed/Cho0aFDivI8//jg7vc0222RH9CrJ4sWLs9Mb7qMtbdttty0x4qhRo0Z2+te//nWxI+dErIuB1ls/8lNx2rRpU+I6IiIaNWoUtWrVitzc3Pjkk0+yzxecbt26dYmvX++ggw6K3r17F3nthjZnlKuIdaHg7rvvHrvvvnux8/Py8mLy5MnxySefxLhx47LPl7aP9thjj1Lfs+AxWbNmTXb6iy++yH4PmzVrVmq0WKlSpfjb3/5W6LktdY62aNEidt5555g3b15Mnjw5/vCHP8TBBx8cBx98cBx00EFRr169Ute7paw/7lWrVo1mzZqVumzlypVj3333jTfffDNWr14dEydOLHY0ttK+45tijz32yI7OFLEuYFu6dGl88MEHMXjw4FizZk1UrVo1LrroojjllFNK/e5ErIuz1kdHG8pkMjF79uz47LPPYsyYMdnnN3b9LO18LBgkrV69utC8gud7cfuyoFatWpU4b2scx9K+9wU/02677VbicgWveZv7G1S7du248cYby7TsjjvuWOjxokWLsiPn7b777tlRpUqy8847R8OGDWPKlCkxb968mDt3brHRY1l/z/Lz8zd6rSh4nRo/fnx06NAhItaNblWlSpVYvnx5jBo1Kg477LBo165dtGnTJlq3bh116tQpdb0AAAD8fIiuAAAA+EWrV69e9rZ/qebOnZudnjFjRlxwwQVlfu2iRYuKff7777+PN954I/773//GV199FTNnzow5c+YU+cN+xLqY4KegpNghIuKbb77JTn/00Ufx0UcflXm9Je2jLaW07S6opFFPImKjMch6ZYlW6tWrF7m5ubFixYpYvHhx1KhRo9BoSaWFEOsVHKHr22+/LXG5kkax2RTjx4+P999/P6ZNmxazZs2KmTNnlnjsSjtnC0ZVxSkYUxWMtwruo4Kj8JTVljpHt9lmm7jjjjuie/fusXjx4li2bFkMHz48O6rOb37zmzjkkEOiffv20aJFizKfOymWLFmSHeWofv36xd6ObkMFz5mSRucq63elrLbffvtir7sdO3aMLl26xLnnnhu5ubnRo0eP+Oijj6JXr15l+izLly+Pt956KyZMmBAzZsyImTNnxuzZs4sd1Wpj18/SviMFbxe44XoK7sONjWS2fvS2DW2t41jaKGAFbYlrXllUqVJlk39/FyxYkJ0u+NlL8+tf/zp7G9iFCxcWe3zK+ns2YsSIGDFiRFk3t9C1onbt2nHbbbfFZZddFqtWrYq8vLwYNGhQDBo0KMqVKxeNGjWKQw89NNq3bx977713md8DAACAnx7RFQAAAJSg4Ig3qTa8nVAmk4n77rsvHnzwwRJvDdWwYcNo3rx5DBgwYJPfd2uoVKlSifO25D7a0rbZZputuv6CqlWrttFlCt4+asmSJVGjRo1C+6DgCDMlKbjMsmXLSlyutGNWVlOnTo1rrrmmxBHJqlWrFi1btoz58+fHxIkTN7q+0kaoKk3B27uVZR9taEueo/vuu2+8/PLL8dBDD8XQoUMLhW8zZsyIGTNmxBNPPBG//e1vo0ePHrHffvtt8nsXZ+nSpdnp0sKZgspyzmyJ86WsmjVrFnfeeWecddZZkZ+fHy+//HLUrl07rr322lJf179//+jTp0+R2/2tt+uuu8YhhxwSTz/9dJm2o2BYlaLgiIYbOx+rV69e7PNb6zj+kNe8ra3gd++HOte35LWiffv2MXjw4HjwwQfj1Vdfza47k8nE559/Hp9//nk88MAD0bx58/jXv/4Vv/3tbzf5vQEAAPjxiK4AAACgBAX/gNu1a9e44YYbNnld//M//xP9+/ePiHUjiTRt2jSaNm0aDRs2jAYNGkTjxo2jZs2aMXPmzB8sulq5cuVmr6PgPrr88svjnHPO2ex1/hytWrVqo8sUjABq164dEYVjgpJivIIKxhqbEiCV1fTp0+OPf/xjNiTYfvvto1WrVtGoUaPYfffdIycnJ3bfffcoX758/P3vfy9TdLWpCn7Osuyj0l6/Jc7RnXfeOa699tq4+uqrY/z48fHee+/F6NGj45NPPsmOWDdt2rQ4++yzo1+/ftG4cePNer+CCp4vpUV3Bf1Q50yKAw44IM4///y4++67IyLiySefjJYtW0b79u2LXf6BBx6IPn36ZB83atQomjdvHr/97W+jQYMG0ahRo6hTp07k5+eXObraVAVHSlq2bFmpEU9J14VfynHcmgqGrD/UPioYxt5+++1x1FFHJa+joN133z1uvvnm6NGjR4wdOzZGjRoVo0ePjokTJ2Zv3Thu3Ljo1q1bDBo0KHbaaafNej8AAAB+eKIrAAAAKEGdOnWy09OnT9/k9YwfPz4bXNWsWTMeeuih2GeffYpdNi8vb5PfJyIK3aZqzZo1pS5bcMSWTbWl9tHPXcFbURYnk8nE7NmzI2LdObDttttGRMSOO+6YXearr77a6PsUXGaXXXbZlE0tk549e2aDq86dO8cNN9yQ3eYNbYnzqDQ77LBDdnpj+zli3fetUqVKseuuu0b16tW32jlavnz5aN68eTRv3jwuvPDCWLp0abz11lvRp0+fmD17dixfvjweeuih6N279xZ7zxo1akSVKlVi+fLlMXv27Fi7du1Gb033Q50zqc4777x44403ssHeddddF/vtt1+h4x0RMW/evLjrrrsiYt1ITnfddVccdthhxa5zc6+fZVEwjJk1a1apt/QreLu6gn5Jx3FrKXhtnDlzZples7n7qOB7bslrxTbbbBOtW7eO1q1bR8S60ftGjBgRffr0ie+++y6+/fbbeOqpp+LSSy/dYu8JAADAD6P0/zUPAAAA/4ftueee2VFMxo0bV+JtrdabMmVKXHjhhXHzzTfHkCFDss+//vrr2enTTjutxOAqIuKzzz7LTmcymeRtLjjqysZu3zdlypTk9W+o4Gd55513siP9lGTUqFFx0UUXxW233RZvvfXWZr//T8XYsWNLnT9x4sTs8TjggAOyzzdr1iw7/f7772/0fd57773sdJMmTRK3smxWrlyZfZ/KlSvH9ddfX2JwFRExadKk7PSmnLMb06RJk+xt0z755JPsCDElueSSS6Jjx45xyCGHRH5+/hY7R8eMGROPPfZYXH/99YVG1FmvWrVqcfTRR8e///3v7HMFv89bStOmTSNi3eg/n3zySanLLl++PMaNGxcR627vuMcee2zx7dlUFSpUiJ49e2ZvO5mbmxu33XZbkeXeeuutbEB69NFHlxhcRWz+9bMsWrRokZ3e2Hd2zJgxJc77pRzHrWWHHXaI+vXrR0TEl19+udHgcu7cufHll19GxLoYeFNGjSp4rXjzzTc3uvzgwYPjsssuiz59+sRHH32Uff7tt9+OBx98MG688cZiX1erVq04+eSTo2fPntnntsa1AgAAgK1PdAUAAAAl2HbbbePQQw+NiHW3ibr//vtLXf6ee+6JV199Nfr27VvoD6gFY62Ct0za0KpVq+Kpp57KPi5upKqCo6GsXbu2yPxatWpFhQoVImLd6CAlhVczZ86M//73vyV/mDLaddddY88994yIiAULFkS/fv1KXDaTycTtt98ew4YNi0ceeSQ78tMvwbhx4+LTTz8tcf6jjz6anS54C7V99tknO7rKqFGjYsKECSWu4/vvv49nnnkm+/j3v//9Jm9vuXLlstMbnkd5eXnZ5ypUqFDollsbGjx4cMybNy/7eGOjq22KypUrx4EHHhgREQsXLozhw4eXuOzYsWNjzpw5ERHRunXrqFChwhY7RwcOHBi33HJL9O/fP95+++0S17H99ttnp0v7vm+qgufPAw88UOqyTz/9dCxevDgi1sV+1atX3+LbszkaN24c3bp1yz4eNGhQkVCprNfPTCZT6Hu2Nc7FiIgjjjgi+/3p169fibe+W716dTz77LMlrueXdBy3lvX7aO3atfHQQw+VuuyDDz6YvW5t6rVx3333zV6P//vf/8Ybb7xR4rIrV66M3r17x0svvRQPPPBAoRH/Hn744ejdu3c89dRTpV7TS7tWbOy3HgAAgJ8G0RUAAACUonv37tk/fvbt2zeefvrpYpd7+OGHszHItttuG2eccUZ23vrROiLWRQUrVqwo8vrc3Nz461//Wmj0qZUrVxZZrkqVKtnp4m7rVrFixdhrr70iImLFihVx7733Fllm4cKFcemll250xJ+yOu+887LTt912W4wYMaLIMplMJv71r39l/wBdp06d6Ny58xZ5/5+Kyy67rNjRWPr27Rsvv/xyRETk5OREhw4dsvMqVaoUf/7znyNi3T7629/+Fl988UWRdSxZsiT+9re/xfz58yMi4tBDD43mzZtv8rZWrVo1O73heVS7du1sALB06dL4z3/+U+w6hg8fHjfccEOh54o7Z7eE9fsoIqJHjx7Fjgozb968uO6667KPTz/99Oz0ljhHjzrqqOz0LbfcUuKt4wqOdNWqVatSP9emOOGEE7JhyFtvvRW9evUqNsoYMWJE3HnnnRGxLp678MILt/i2bAkXXnhh7LzzztnHN9xwQ6Fr06677pqdHjFiRHz77bdF1rFixYq45pprCo0Et7XOxbp168aJJ54YERFz5syJSy+9tMg1PT8/P2644YZSRxP8pR3HreGMM87IXqueeeaZQlFyQU8//XQ2cKtWrVqcc845m/R+lSpVirPPPjv7+Morryx2FMPVq1fHJZdckr0G5OTkZAPtiHUjsq137bXXZoO5gvLz8wvFdhteKzb2Ww8AAMBPQ8UfewMAAADgp6xp06ZxySWXRO/evSOTyUSPHj1iyJAh0aFDh9hpp51i/vz5MXz48Pj444+zr7n22mtjl112yT4+9thj4957740VK1bElClT4phjjomTTjop6tevH0uWLImJEyfG0KFDi/xhtrhRqurWrZudfvTRR6NmzZpRuXLlaN68edSrVy8iIrp06ZINRx599NGYOnVq/OEPf4jKlSvHxIkTY9CgQZGXlxfNmjXb6G2tyqJ9+/bRpUuX6N+/f6xatSr++te/Rps2beKwww6L7bffPmbPnh1DhgzJBggVKlSIm2++uVD483NXrly5+Oqrr+LYY4+NLl26xF577RXff/99DB06ND744IOIWDdi00033ZS9ndp63bp1i1GjRsXbb78dc+fOjY4dO8Zxxx0XLVq0iEqVKsWUKVPihRdeiAULFkRExC677BK33HLLZm1vwfPzzjvvjBUrVkT58uXjkEMOiZo1a0anTp2ygcPVV18d7733XrRo0SKqVasWX3/9dbz22msxfvz4IustLi7YElq2bBl/+tOf4rHHHovvvvsuTj755Dj22GOjZcuWERHx+eefx3PPPZcddahLly6FbuO4Jc7Rgw8+ONq0aRPvvvtuzJs3L4488sjo1KlT5OTkRPXq1eObb76JoUOHxsSJEyNiXbT1pz/9aYvviypVqsTtt98ef/rTn2L16tXx0EMPxdtvvx3HHXdc/OpXv4q8vLx46623Ct0a8eKLL96sSG9rqlatWlx11VVx8cUXR0TEtGnT4rHHHovu3btHxLrAcMcdd4wFCxbEggUL4rjjjosuXbrEb37zm1i1alVMnTo1XnrppVi4cGGh9S5fvjzWrFlT5Pu2JVxxxRUxevTomDVrVrz55ptx9NFHx8knnxy77rprzJs3LwYOHBhTpkyJHXbYodhILOKXdxy3hl122SVuvPHGuPzyyyOTycSNN94YL7/8cvb3d8GCBTFs2LDsrf3KlSsX//znPwuFeqnOOOOMeP/99+Ptt9+OvLy8OP300+MPf/hDtGnTJqpWrRpfffVVvPDCC9kR9apUqRL/+7//W2hkqs6dO8fTTz8dU6ZMicmTJ8fhhx8enTt3jgYNGsS2224bc+bMiUGDBsWMGTMiIqJBgwZxwgknFNqOsvzWAwAA8OMTXQEAAMBGdO/ePapVqxa33XZbrFixIsaNGxfjxo0rslyVKlXi6quvjpNPPrnQ83Xr1o1bbrkl/vGPf8SqVati1qxZ0adPnyKvr1q1alx55ZXx4osvxrhx4+Kbb76J7777rtAtiPbcc89o2LBhfPHFF7FgwYK4+uqrIyLi+uuvj1NPPTUi1o2gMmHChOjfv39ERLzzzjvxzjvvFHqv448/Pk455ZTo0qXL5u2c/++f//xn1K5dOx566KFYs2ZNvPvuu/Huu+8WWa5WrVrRs2fPOOSQQ7bI+/5UHHHEEfH999/HqFGj4uGHHy4yf8cdd4z77rsvfve73xWZV758+bjnnnuiR48e8fzzz8eqVatiwIABMWDAgCLLHnDAAdGrV6+oXbv2Zm3vQQcdlA1Cvvjii/jHP/4REetuc9a2bdu47LLL4vPPP4+xY8dGfn5+DB48OAYPHlxkPa1atYrjjjsurrnmmoiImDRp0mZtV2muuOKKqFy5cjz44IOxevXqGDhwYAwcOLDIcl27di004tV6W+Ic7dOnT1xwwQUxZsyYWLZsWYkj3+22225x1113RZ06dTbhk27c/vvvH3379o1LLrkk5s+fH1OmTIlevXoVWa5KlSpx/fXX/+RHlTvyyCNjwIAB2ePx73//O44++uj41a9+FdWqVYs77rgj/vKXv8TixYtj4cKFxY7gV7FixbjgggtiypQp8corr8TatWtjypQp2VtLbkk1atSIfv36xXnnnRcTJkyI2bNnF7mm165dO26//fZCt0/c0C/tOG4NxxxzTFSqVCmuueaa+P777+Pjjz8uFDmvt/3228ett95aaMSpTVHwejxgwIBYu3ZtDB8+vNjbmta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      "text/plain": [
       "<Figure size 2880x6912 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = X1.columns\n",
    "X1_Feature_imp = feature_imp(tx,vx,feature_names,rf1_2)\n",
    "X1_Feature_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7/7] features ████████████████████ Time elapsed: 1 seconds"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = X2.columns\n",
    "X2_Feature_imp = feature_imp(tx1,vx1,feature_names,rf2_2)\n",
    "X2_Feature_imp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction on Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1679,
   "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": 1679,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1680,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8072 entries, 0 to 8071\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   pet_id        8072 non-null   object \n",
      " 1   issue_date    8072 non-null   object \n",
      " 2   listing_date  8072 non-null   object \n",
      " 3   condition     7453 non-null   float64\n",
      " 4   color_type    8072 non-null   object \n",
      " 5   length(m)     8072 non-null   float64\n",
      " 6   height(cm)    8072 non-null   float64\n",
      " 7   X1            8072 non-null   int64  \n",
      " 8   X2            8072 non-null   int64  \n",
      "dtypes: float64(3), int64(2), object(4)\n",
      "memory usage: 567.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1681,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "896de1ade94d4618a77fec38606169f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Summarize dataset', max=23.0, style=ProgressStyle(descrip…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "47cfb8f04a23454ba50f701901f53574",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Generate report structure', max=1.0, style=ProgressStyle(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Render widgets', max=1.0, style=ProgressStyle(description…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9bb1966b34734e248a3d90057b3068ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Tab(children=(Tab(children=(GridBox(children=(VBox(children=(GridspecLayout(children=(HTML(valu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_profile = ProfileReport(df_test,title='Detailed Customer Report',explorative=True)\n",
    "test_profile.to_widgets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1660,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing Unnecessary Columns\n",
    "Z = del_col('pet_id',df_test)\n",
    "\n",
    "\n",
    "# Imputation\n",
    "Z['condition'] = impute_col(Z['condition'],3.0)\n",
    "Z['condition'] = Z['condition'].astype('int')\n",
    "\n",
    "# Standardization - converting cm to mts\n",
    "Z['length(cm)'] = Z['length(m)'].apply(lambda x: x*100)\n",
    "Z = del_col('length(m)',Z)\n",
    "val = Z['length(cm)'].mean()\n",
    "Z['length(cm)'] = Z['length(cm)'].replace(to_replace=0, value=val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1661,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z[['issue_date','listing_date']] = Z[['issue_date','listing_date']].apply(pd.to_datetime) #if conversion required\n",
    "Z['diff_days'] = (Z['listing_date'] - Z['issue_date']).dt.days\n",
    "Z = del_col('issue_date',Z)\n",
    "Z = del_col('listing_date',Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1662,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Diff days Standardization\n",
    "Z['diff_days'] = abs(Z['diff_days'])\n",
    "Z['diff_days'] =np.array(np.array(Z['diff_days']) / 365.)\n",
    "\n",
    "# Quantile Based Binning\n",
    "quantile_list = [0, .25, .5, .75, 1.]\n",
    "quantiles = Z['length(cm)'].quantile(quantile_list)\n",
    "quantile_labels = ['0-25Q', '25-50Q', '50-75Q', '75-100Q']\n",
    "Z['length_label'] = pd.qcut(Z['length(cm)'],q=quantile_list, labels=quantile_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1663,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = pd.concat([Z,pd.get_dummies(Z['condition'], prefix='condition')],axis=1)\n",
    "Z = pd.concat([Z,pd.get_dummies(Z['X2'], prefix='X2')],axis=1)\n",
    "Z = pd.concat([Z,pd.get_dummies(Z['X1'], prefix='X1')],axis=1)\n",
    "Z = pd.concat([Z,pd.get_dummies(Z['color_type'], prefix='color_type')],axis=1)\n",
    "Z = pd.concat([Z,pd.get_dummies(Z['length_label'], prefix='length_label')],axis=1)\n",
    "\n",
    "Z = del_col('condition',Z)\n",
    "Z = del_col('color_type',Z)\n",
    "Z = del_col('X2',Z)\n",
    "Z = del_col('X1',Z)\n",
    "Z = del_col('length(cm)',Z)\n",
    "Z = del_col('length_label',Z)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1664,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>height(cm)</th>\n",
       "      <th>diff_days</th>\n",
       "      <th>condition_0</th>\n",
       "      <th>condition_1</th>\n",
       "      <th>condition_2</th>\n",
       "      <th>condition_3</th>\n",
       "      <th>X2_0</th>\n",
       "      <th>X2_1</th>\n",
       "      <th>X2_2</th>\n",
       "      <th>X2_3</th>\n",
       "      <th>...</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>length_label_0-25Q</th>\n",
       "      <th>length_label_25-50Q</th>\n",
       "      <th>length_label_50-75Q</th>\n",
       "      <th>length_label_75-100Q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.73</td>\n",
       "      <td>12.065753</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</th>\n",
       "      <td>6.71</td>\n",
       "      <td>0.476712</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.21</td>\n",
       "      <td>5.476712</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.46</td>\n",
       "      <td>3.145205</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.92</td>\n",
       "      <td>1.268493</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   height(cm)  diff_days  condition_0  condition_1  condition_2  condition_3  \\\n",
       "0       42.73  12.065753            1            0            0            0   \n",
       "1        6.71   0.476712            0            1            0            0   \n",
       "2       41.21   5.476712            0            1            0            0   \n",
       "3        8.46   3.145205            0            1            0            0   \n",
       "4       30.92   1.268493            0            1            0            0   \n",
       "\n",
       "   X2_0  X2_1  X2_2  X2_3  ...  color_type_Tortie  color_type_Tortie Point  \\\n",
       "0     0     0     0     0  ...                  0                        0   \n",
       "1     0     1     0     0  ...                  0                        0   \n",
       "2     0     0     0     0  ...                  0                        0   \n",
       "3     0     1     0     0  ...                  0                        0   \n",
       "4     0     0     0     0  ...                  0                        0   \n",
       "\n",
       "   color_type_Tricolor  color_type_White  color_type_Yellow  \\\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_Yellow Brindle  length_label_0-25Q  length_label_25-50Q  \\\n",
       "0                          0                   0                    0   \n",
       "1                          0                   1                    0   \n",
       "2                          0                   1                    0   \n",
       "3                          0                   0                    1   \n",
       "4                          0                   0                    0   \n",
       "\n",
       "   length_label_50-75Q  length_label_75-100Q  \n",
       "0                    0                     1  \n",
       "1                    0                     0  \n",
       "2                    0                     0  \n",
       "3                    0                     0  \n",
       "4                    1                     0  \n",
       "\n",
       "[5 rows x 92 columns]"
      ]
     },
     "execution_count": 1664,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1682,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding Missing Columns from training Set\n",
    "Z['color_type_Black Tiger'] = 0\n",
    "Z['color_type_Brown Tiger'] = 0\n",
    "Z['X1_3'] = 0\n",
    "Z['X1_19'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1667,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = Z[X1.columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1668,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [0],\n",
       "       [0],\n",
       "       ...,\n",
       "       [1],\n",
       "       [2],\n",
       "       [1]])"
      ]
     },
     "execution_count": 1668,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breed_category = rf1_2.predict(Z)\n",
    "breed_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1669,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2],\n",
       "       [1],\n",
       "       [2],\n",
       "       ...,\n",
       "       [2],\n",
       "       [4],\n",
       "       [2]])"
      ]
     },
     "execution_count": 1669,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pet_category = rf2_2.predict(Z)\n",
    "pet_category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1670,
   "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>pet_id</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_75005</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANSL_76663</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANSL_58259</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANSL_67171</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANSL_72871</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ANSL_64886</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ANSL_73798</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ANSL_53131</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ANSL_66674</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ANSL_55165</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pet_id  breed_category  pet_category\n",
       "0  ANSL_75005               1             2\n",
       "1  ANSL_76663               0             1\n",
       "2  ANSL_58259               0             2\n",
       "3  ANSL_67171               0             2\n",
       "4  ANSL_72871               0             2\n",
       "5  ANSL_64886               1             1\n",
       "6  ANSL_73798               1             2\n",
       "7  ANSL_53131               1             2\n",
       "8  ANSL_66674               0             1\n",
       "9  ANSL_55165               1             2"
      ]
     },
     "execution_count": 1670,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.DataFrame(df_test['pet_id'],columns=['pet_id',])\n",
    "submission['breed_category'] = breed_category\n",
    "submission['pet_category'] = pet_category\n",
    "submission.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1671,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3869\n",
       "1    3584\n",
       "2     619\n",
       "Name: breed_category, dtype: int64"
      ]
     },
     "execution_count": 1671,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission['breed_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1672,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    4704\n",
       "1    2922\n",
       "4     386\n",
       "0      60\n",
       "Name: pet_category, dtype: int64"
      ]
     },
     "execution_count": 1672,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission['pet_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1673,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('adopt_pet_v88.csv',index=False)"
   ]
  }
 ],
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