Hyperparameter Optimization For Xgboost.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Optimization For Xgboost using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read the Dataset\n",
    "\n",
    "df=pd.read_csv('Churn_Modelling.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Correlation\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#get correlations of each features in dataset\n",
    "corrmat = df.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "#plot heat map\n",
    "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the Independent and Dependent Features\n",
    "X=df.iloc[:,3:13]\n",
    "Y=df.iloc[:,13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "geography=pd.get_dummies(X['Geography'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Germany</th>\n",
       "      <th>Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Germany  Spain\n",
       "0        0      0\n",
       "1        0      1\n",
       "2        0      0\n",
       "3        0      0\n",
       "4        0      1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geography.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender=pd.get_dummies(X['Gender'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Male\n",
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Drop Categorical Features\n",
    "X=X.drop(['Geography','Gender'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619   42       2       0.00              1          1   \n",
       "1          608   41       1   83807.86              1          0   \n",
       "2          502   42       8  159660.80              3          1   \n",
       "3          699   39       1       0.00              2          0   \n",
       "4          850   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  \n",
       "0               1        101348.88  \n",
       "1               1        112542.58  \n",
       "2               0        113931.57  \n",
       "3               0         93826.63  \n",
       "4               1         79084.10  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=pd.concat([X,geography,gender],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Spain</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619   42       2       0.00              1          1   \n",
       "1          608   41       1   83807.86              1          0   \n",
       "2          502   42       8  159660.80              3          1   \n",
       "3          699   39       1       0.00              2          0   \n",
       "4          850   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Germany  Spain  Male  \n",
       "0               1        101348.88        0      0     0  \n",
       "1               1        112542.58        0      1     0  \n",
       "2               0        113931.57        0      0     0  \n",
       "3               0         93826.63        0      0     0  \n",
       "4               1         79084.10        0      1     0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hyper Parameter Optimization\n",
    "\n",
    "params={\n",
    " \"learning_rate\"    : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ] ,\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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hyperparameter optimization using RandomizedSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "import xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def timer(start_time=None):\n",
    "    if not start_time:\n",
    "        start_time = datetime.now()\n",
    "        return start_time\n",
    "    elif start_time:\n",
    "        thour, temp_sec = divmod((datetime.now() - start_time).total_seconds(), 3600)\n",
    "        tmin, tsec = divmod(temp_sec, 60)\n",
    "        print('\\n Time taken: %i hours %i minutes and %s seconds.' % (thour, tmin, round(tsec, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=xgboost.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search=RandomizedSearchCV(classifier,param_distributions=params,n_iter=5,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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:    5.3s remaining:    1.6s\n",
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:    5.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Time taken: 0 hours 0 minutes and 6.18 seconds.\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "# Here we go\n",
    "start_time = timer(None) # timing starts from this point for \"start_time\" variable\n",
    "random_search.fit(X,Y)\n",
    "timer(start_time) # timing ends here for \"start_time\" variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Spain</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619   42       2       0.00              1          1   \n",
       "1          608   41       1   83807.86              1          0   \n",
       "2          502   42       8  159660.80              3          1   \n",
       "3          699   39       1       0.00              2          0   \n",
       "4          850   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Germany  Spain  Male  \n",
       "0               1        101348.88        0      0     0  \n",
       "1               1        112542.58        0      1     0  \n",
       "2               0        113931.57        0      0     0  \n",
       "3               0         93826.63        0      0     0  \n",
       "4               1         79084.10        0      1     0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.5, gamma=0.4, learning_rate=0.1,\n",
       "       max_delta_step=0, max_depth=6, min_child_weight=7, missing=None,\n",
       "       n_estimators=100, n_jobs=1, nthread=None,\n",
       "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=True,\n",
       "       subsample=1)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_child_weight': 7,\n",
       " 'max_depth': 6,\n",
       " 'learning_rate': 0.1,\n",
       " 'gamma': 0.4,\n",
       " 'colsample_bytree': 0.5}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=xgboost.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "       colsample_bytree=0.5, gamma=0.4, learning_rate=0.1,\n",
    "       max_delta_step=0, max_depth=6, min_child_weight=7, missing=None,\n",
    "       n_estimators=100, n_jobs=1, nthread=None,\n",
    "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
    "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=True,\n",
    "       subsample=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(classifier,X,Y,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87012987, 0.86613387, 0.87012987, 0.867     , 0.862     ,\n",
       "       0.852     , 0.864     , 0.87887888, 0.85885886, 0.85785786])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8646989201989201"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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