DSG8B09 - V.SaiKrishna - Credit_Card.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Om Sri Sai Ram. Welcome to the final leg of your training program.\n",
    "\n",
    "This is a competition in which you need to predict whether the customer would buy a credit card or not. Relevant data has been provided to you.\n",
    "\n",
    "The competition will end at 2 PM sharp. Please start submitting when you have 30 mins in hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler, StandardScaler, MinMaxScaler, OneHotEncoder\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, cross_val_predict\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import xgboost as xgb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\saikr\\anaconda3\\lib\\site-packages (1.3.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from xgboost) (1.19.3)\n",
      "Requirement already satisfied: scipy in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from xgboost) (1.5.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\saikr'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
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   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Income</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Securities Account</th>\n",
       "      <th>CD Account</th>\n",
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       "      <th>CreditCard</th>\n",
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       "      <th>4</th>\n",
       "      <td>1682</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
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       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "     ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "0   385   51          25      21      9307       4   0.60          3   \n",
       "1   653   34           9      92     90005       1   2.80          1   \n",
       "2  1470   59          35      59     90005       4   1.20          2   \n",
       "3  1620   45          21      29     90005       1   0.30          3   \n",
       "4  1682   32           8     141     90005       2   4.33          1   \n",
       "\n",
       "   Mortgage  Personal Loan  Securities Account  CD Account  Online  CreditCard  \n",
       "0         0              0                   0           0       1           1  \n",
       "1         0              0                   0           0       1           0  \n",
       "2         0              0                   0           0       1           0  \n",
       "3         0              0                   0           0       1           0  \n",
       "4         0              0                   0           0       0           0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = pd.read_csv('C:\\\\Users\\\\saikr\\\\Desktop\\\\Data Science\\\\SSSO-Medchal-Malkajgiri\\\\sssso-mdcl-competition\\\\train.csv')\n",
    "df = X_train.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "0   295   35           9      55     94536       1    2.0          1   \n",
       "1  3359   59          35      40     94536       4    0.4          1   \n",
       "2  3379   25           0      44     94536       4    0.6          2   \n",
       "3  3457   46          22     125     94536       2    4.7          3   \n",
       "4   767   37          12      81     94538       1    2.8          1   \n",
       "\n",
       "   Mortgage  Personal Loan  Securities Account  CD Account  Online  \n",
       "0         0              0                   0           0       1  \n",
       "1         0              0                   0           0       0  \n",
       "2         0              0                   0           0       0  \n",
       "3         0              1                   0           0       1  \n",
       "4         0              0                   0           0       0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = pd.read_csv('C:\\\\Users\\\\saikr\\\\Desktop\\\\Data Science\\\\SSSO-Medchal-Malkajgiri\\\\sssso-mdcl-competition\\\\test1.csv')\n",
    "df_test = X_test.copy()\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000 14\n"
     ]
    }
   ],
   "source": [
    "num_rows = df.shape[0]\n",
    "num_cols = df.shape[1]\n",
    "print(num_rows, num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500 13\n"
     ]
    }
   ],
   "source": [
    "num_test_rows = df_test.shape[0]\n",
    "num_test_cols = df_test.shape[1]\n",
    "print(num_test_rows, num_test_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# Data Cleaning\n",
    "print(df.isnull().values.any())\n",
    "print(df_test.isnull().values.any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 14 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   ID                  5000 non-null   int64  \n",
      " 1   Age                 5000 non-null   int64  \n",
      " 2   Experience          5000 non-null   int64  \n",
      " 3   Income              5000 non-null   int64  \n",
      " 4   ZIP Code            5000 non-null   int64  \n",
      " 5   Family              5000 non-null   int64  \n",
      " 6   CCAvg               5000 non-null   float64\n",
      " 7   Education           5000 non-null   int64  \n",
      " 8   Mortgage            5000 non-null   int64  \n",
      " 9   Personal Loan       5000 non-null   int64  \n",
      " 10  Securities Account  5000 non-null   int64  \n",
      " 11  CD Account          5000 non-null   int64  \n",
      " 12  Online              5000 non-null   int64  \n",
      " 13  CreditCard          5000 non-null   int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 547.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1500 entries, 0 to 1499\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   ID                  1500 non-null   int64  \n",
      " 1   Age                 1500 non-null   int64  \n",
      " 2   Experience          1500 non-null   int64  \n",
      " 3   Income              1500 non-null   int64  \n",
      " 4   ZIP Code            1500 non-null   int64  \n",
      " 5   Family              1500 non-null   int64  \n",
      " 6   CCAvg               1500 non-null   float64\n",
      " 7   Education           1500 non-null   int64  \n",
      " 8   Mortgage            1500 non-null   int64  \n",
      " 9   Personal Loan       1500 non-null   int64  \n",
      " 10  Securities Account  1500 non-null   int64  \n",
      " 11  CD Account          1500 non-null   int64  \n",
      " 12  Online              1500 non-null   int64  \n",
      "dtypes: float64(1), int64(12)\n",
      "memory usage: 152.5 KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    3530\n",
      "1    1470\n",
      "Name: CreditCard, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['CreditCard'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies_df = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "      <td>3</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>1682</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>141</td>\n",
       "      <td>90005</td>\n",
       "      <td>2</td>\n",
       "      <td>4.33</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>673</td>\n",
       "      <td>51</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>96651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.20</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>4996</th>\n",
       "      <td>1427</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>60</td>\n",
       "      <td>96651</td>\n",
       "      <td>3</td>\n",
       "      <td>0.50</td>\n",
       "      <td>3</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>4997</th>\n",
       "      <td>1654</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>96651</td>\n",
       "      <td>2</td>\n",
       "      <td>0.90</td>\n",
       "      <td>3</td>\n",
       "      <td>123</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>4998</th>\n",
       "      <td>2732</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>96651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3</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>4999</th>\n",
       "      <td>3526</td>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "      <td>13</td>\n",
       "      <td>96651</td>\n",
       "      <td>4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "0      385   51          25      21      9307       4   0.60          3   \n",
       "1      653   34           9      92     90005       1   2.80          1   \n",
       "2     1470   59          35      59     90005       4   1.20          2   \n",
       "3     1620   45          21      29     90005       1   0.30          3   \n",
       "4     1682   32           8     141     90005       2   4.33          1   \n",
       "...    ...  ...         ...     ...       ...     ...    ...        ...   \n",
       "4995   673   51          27      23     96651       1   0.20          1   \n",
       "4996  1427   37          11      60     96651       3   0.50          3   \n",
       "4997  1654   26           1      24     96651       2   0.90          3   \n",
       "4998  2732   29           5      28     96651       1   0.20          3   \n",
       "4999  3526   59          34      13     96651       4   0.90          2   \n",
       "\n",
       "      Mortgage  Personal Loan  Securities Account  CD Account  Online  \\\n",
       "0            0              0                   0           0       1   \n",
       "1            0              0                   0           0       1   \n",
       "2            0              0                   0           0       1   \n",
       "3            0              0                   0           0       1   \n",
       "4            0              0                   0           0       0   \n",
       "...        ...            ...                 ...         ...     ...   \n",
       "4995         0              0                   0           0       1   \n",
       "4996         0              0                   0           0       1   \n",
       "4997       123              0                   0           0       0   \n",
       "4998         0              0                   0           0       1   \n",
       "4999         0              0                   0           0       0   \n",
       "\n",
       "      CreditCard  \n",
       "0              1  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  \n",
       "...          ...  \n",
       "4995           0  \n",
       "4996           0  \n",
       "4997           1  \n",
       "4998           0  \n",
       "4999           0  \n",
       "\n",
       "[5000 rows x 14 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case1  : Splitting the data into train and test data from the train.csv file provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import train_test_split\n",
    "X_train = dummies_df.drop('CreditCard',axis=1)\n",
    "y_train = dummies_df['CreditCard']\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Securities Account</th>\n",
       "      <th>CD Account</th>\n",
       "      <th>Online</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>385</td>\n",
       "      <td>51</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>9307</td>\n",
       "      <td>4</td>\n",
       "      <td>0.60</td>\n",
       "      <td>3</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>653</td>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "      <td>92</td>\n",
       "      <td>90005</td>\n",
       "      <td>1</td>\n",
       "      <td>2.80</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1470</td>\n",
       "      <td>59</td>\n",
       "      <td>35</td>\n",
       "      <td>59</td>\n",
       "      <td>90005</td>\n",
       "      <td>4</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2</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>3</th>\n",
       "      <td>1620</td>\n",
       "      <td>45</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>90005</td>\n",
       "      <td>1</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3</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>4</th>\n",
       "      <td>1682</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>141</td>\n",
       "      <td>90005</td>\n",
       "      <td>2</td>\n",
       "      <td>4.33</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "0   385   51          25      21      9307       4   0.60          3   \n",
       "1   653   34           9      92     90005       1   2.80          1   \n",
       "2  1470   59          35      59     90005       4   1.20          2   \n",
       "3  1620   45          21      29     90005       1   0.30          3   \n",
       "4  1682   32           8     141     90005       2   4.33          1   \n",
       "\n",
       "   Mortgage  Personal Loan  Securities Account  CD Account  Online  \n",
       "0         0              0                   0           0       1  \n",
       "1         0              0                   0           0       1  \n",
       "2         0              0                   0           0       1  \n",
       "3         0              0                   0           0       1  \n",
       "4         0              0                   0           0       0  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Model (Stochastic Gradient Descent Classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier()"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear model\n",
    "model = linear_model.SGDClassifier()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 ... 1 1 1]\n",
      "1500\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(df_test)\n",
    "print(y_pred)\n",
    "print(np.count_nonzero(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6973333333333334"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier(criterion='gini',max_depth=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.74      0.73      1054\n",
      "           1       0.37      0.37      0.37       446\n",
      "\n",
      "    accuracy                           0.63      1500\n",
      "   macro avg       0.55      0.55      0.55      1500\n",
      "weighted avg       0.63      0.63      0.63      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = dtree.predict(X_test)\n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[776 278]\n",
      " [282 164]]\n",
      "Accuracy of prediction: 0.627\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,predictions)\n",
    "print(cm)\n",
    "print (\"Accuracy of prediction:\",round((cm[0,0]+cm[1,1])/cm.sum(),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5786666666666667"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=600)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "cr = classification_report(y_test,rfc_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.94      0.80      1046\n",
      "           1       0.36      0.08      0.13       454\n",
      "\n",
      "    accuracy                           0.68      1500\n",
      "   macro avg       0.53      0.51      0.47      1500\n",
      "weighted avg       0.60      0.68      0.60      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[981  65]\n",
      " [417  37]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,rfc_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6786666666666666"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, rfc_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saikr\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:892: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:37:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=13,\n",
       "              min_child_weight=3, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=150, n_jobs=8, num_parallel_tree=1, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBClassifier(learning_rate = 0.1, n_estimators=150, min_child_weight=3,  max_depth=13)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rfc = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7133333333333334"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_rfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5800363878336603"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, y_pred_rfc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BAGGING classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7166666666666667"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn import tree\n",
    "model = BaggingClassifier(tree.DecisionTreeClassifier(random_state=1))\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaboost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7526666666666667"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "model = AdaBoostClassifier(random_state=1)\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.758"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "model= GradientBoostingClassifier(learning_rate=0.01,random_state=1)\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Light GBM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: lightgbm in c:\\users\\saikr\\anaconda3\\lib\\site-packages (3.1.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from lightgbm) (1.19.3)\n",
      "Requirement already satisfied: wheel in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from lightgbm) (0.34.2)\n",
      "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from lightgbm) (0.23.1)\n",
      "Requirement already satisfied: scipy in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from lightgbm) (1.5.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (0.16.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (2.1.0)\n",
      "Collecting catboost\n",
      "  Downloading catboost-0.24.4-cp38-none-win_amd64.whl (65.4 MB)\n",
      "Requirement already satisfied: pandas>=0.24.0 in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from catboost) (1.0.5)\n",
      "Requirement already satisfied: scipy in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from catboost) (1.5.0)\n",
      "Collecting plotly\n",
      "  Downloading plotly-4.14.3-py2.py3-none-any.whl (13.2 MB)\n",
      "Requirement already satisfied: six in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from catboost) (1.15.0)\n",
      "Requirement already satisfied: numpy>=1.16.0 in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from catboost) (1.19.3)\n",
      "Collecting graphviz\n",
      "  Downloading graphviz-0.16-py2.py3-none-any.whl (19 kB)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from catboost) (3.2.2)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from pandas>=0.24.0->catboost) (2020.1)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from pandas>=0.24.0->catboost) (2.8.1)\n",
      "Collecting retrying>=1.3.3\n",
      "  Downloading retrying-1.3.3.tar.gz (10 kB)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\saikr\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (1.2.0)\n",
      "Building wheels for collected packages: retrying\n",
      "  Building wheel for retrying (setup.py): started\n",
      "  Building wheel for retrying (setup.py): finished with status 'done'\n",
      "  Created wheel for retrying: filename=retrying-1.3.3-py3-none-any.whl size=11435 sha256=fe4f7e8f023baca8746b998b463728172bd89d004376ef8e4a104e9bfc01caaa\n",
      "  Stored in directory: c:\\users\\saikr\\appdata\\local\\pip\\cache\\wheels\\c4\\a7\\48\\0a434133f6d56e878ca511c0e6c38326907c0792f67b476e56\n",
      "Successfully built retrying\n",
      "Installing collected packages: retrying, plotly, graphviz, catboost\n",
      "Successfully installed catboost-0.24.4 graphviz-0.16 plotly-4.14.3 retrying-1.3.3\n"
     ]
    }
   ],
   "source": [
    "!pip install lightgbm\n",
    "!pip install catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000243 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1110\n",
      "[LightGBM] [Info] Number of data points in the train set: 3500, number of used features: 13\n",
      "[LightGBM] [Info] Start training from score 0.298571\n"
     ]
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "train_data=lgb.Dataset(X_train,label=y_train)\n",
    "#define parameters\n",
    "params = {'learning_rate':0.001}\n",
    "model= lgb.train(params, train_data, 100) \n",
    "y_pred=model.predict(X_test)\n",
    "for i in range(0,185):\n",
    "    if y_pred[i]>=0.5: \n",
    "        y_pred[i]=1\n",
    "    else: \n",
    "        y_pred[i]=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cat Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.043181\n",
      "0:\tlearn: 0.6818762\ttest: 0.6812742\tbest: 0.6812742 (0)\ttotal: 264ms\tremaining: 4m 24s\n",
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      "982:\tlearn: 0.3069767\ttest: 0.5481045\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 1.14s\n",
      "983:\tlearn: 0.3068168\ttest: 0.5480677\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 1.07s\n",
      "984:\tlearn: 0.3067280\ttest: 0.5478690\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 1.01s\n",
      "985:\tlearn: 0.3065596\ttest: 0.5477899\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 940ms\n",
      "986:\tlearn: 0.3064533\ttest: 0.5476984\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 873ms\n",
      "987:\tlearn: 0.3062654\ttest: 0.5477080\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 805ms\n",
      "988:\tlearn: 0.3061595\ttest: 0.5477768\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 738ms\n",
      "989:\tlearn: 0.3060354\ttest: 0.5478415\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 671ms\n",
      "990:\tlearn: 0.3058538\ttest: 0.5477580\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 604ms\n",
      "991:\tlearn: 0.3057001\ttest: 0.5478666\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 536ms\n",
      "992:\tlearn: 0.3055008\ttest: 0.5479746\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 469ms\n",
      "993:\tlearn: 0.3054946\ttest: 0.5479918\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 402ms\n",
      "994:\tlearn: 0.3052680\ttest: 0.5481197\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 335ms\n",
      "995:\tlearn: 0.3049605\ttest: 0.5483470\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 268ms\n",
      "996:\tlearn: 0.3048505\ttest: 0.5484528\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 201ms\n",
      "997:\tlearn: 0.3048039\ttest: 0.5484781\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 134ms\n",
      "998:\tlearn: 0.3046538\ttest: 0.5485436\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 66.9ms\n",
      "999:\tlearn: 0.3042291\ttest: 0.5485864\tbest: 0.5406518 (328)\ttotal: 1m 6s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.5406517757\n",
      "bestIteration = 328\n",
      "\n",
      "Shrink model to first 329 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.754"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "model=CatBoostClassifier()\n",
    "categorical_features_indices = np.where(df.dtypes != np.float)[0]\n",
    "model.fit(X_train,y_train,cat_features=([ 0,  1, 2, 3, 4, 10]),eval_set=(X_test, y_test))\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case2 : Using the test dataset provided for testing the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Securities Account</th>\n",
       "      <th>CD Account</th>\n",
       "      <th>Online</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>295</td>\n",
       "      <td>35</td>\n",
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       "      <td>40</td>\n",
       "      <td>94536</td>\n",
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       "      <td>3379</td>\n",
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       "      <td>0</td>\n",
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       "      <td>94536</td>\n",
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       "      <th>3</th>\n",
       "      <td>3457</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>125</td>\n",
       "      <td>94536</td>\n",
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       "      <td>81</td>\n",
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       "      <td>2.8</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "0   295   35           9      55     94536       1    2.0          1   \n",
       "1  3359   59          35      40     94536       4    0.4          1   \n",
       "2  3379   25           0      44     94536       4    0.6          2   \n",
       "3  3457   46          22     125     94536       2    4.7          3   \n",
       "4   767   37          12      81     94538       1    2.8          1   \n",
       "\n",
       "   Mortgage  Personal Loan  Securities Account  CD Account  Online  \n",
       "0         0              0                   0           0       1  \n",
       "1         0              0                   0           0       0  \n",
       "2         0              0                   0           0       0  \n",
       "3         0              1                   0           0       1  \n",
       "4         0              0                   0           0       0  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = pd.read_csv('C:\\\\Users\\\\saikr\\\\Desktop\\\\Data Science\\\\SSSO-Medchal-Malkajgiri\\\\sssso-mdcl-competition\\\\test.csv')\n",
    "df_test = X_test.copy()\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500 13\n"
     ]
    }
   ],
   "source": [
    "num_test_rows = df_test.shape[0]\n",
    "num_test_cols = df_test.shape[1]\n",
    "print(num_test_rows, num_test_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Searching the data for null values\n",
    "df_test.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\saikr'"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear model - SGD Classifier\n",
    "sgd_model = linear_model.SGDClassifier()\n",
    "sgd_model.fit(X_train, y_train)\n",
    "sgd_predict = sgd_model.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc_model= GradientBoostingClassifier(learning_rate=0.01,random_state=1)\n",
    "gbc_model.fit(X_train, y_train)\n",
    "gbc_predict = gbc_model.predict(df_test)\n",
    "\n",
    "df_test['CreditCard'] = gbc_predict\n",
    "\n",
    "df_test.to_csv('C:\\\\Users\\\\saikr\\\\Desktop\\\\Data Science\\\\SSSO-Medchal-Malkajgiri\\\\sssso-mdcl-competition\\\\submission.csv', index = False)\n",
    "\n",
    "np.count_nonzero(sgd_predict)\n",
    "np.count_nonzero(gbc_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5786666666666667\n"
     ]
    }
   ],
   "source": [
    "predictions = dtree.predict(X_test)\n",
    "print(accuracy_score(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.043181\n",
      "0:\tlearn: 0.6818762\ttest: 0.6860550\tbest: 0.6860550 (0)\ttotal: 35.8ms\tremaining: 35.7s\n",
      "1:\tlearn: 0.6721312\ttest: 0.6794114\tbest: 0.6794114 (1)\ttotal: 76.5ms\tremaining: 38.2s\n",
      "2:\tlearn: 0.6634355\ttest: 0.6734763\tbest: 0.6734763 (2)\ttotal: 117ms\tremaining: 39s\n",
      "3:\tlearn: 0.6554617\ttest: 0.6681421\tbest: 0.6681421 (3)\ttotal: 157ms\tremaining: 39.1s\n",
      "4:\tlearn: 0.6469250\ttest: 0.6637063\tbest: 0.6637063 (4)\ttotal: 198ms\tremaining: 39.4s\n",
      "5:\tlearn: 0.6404548\ttest: 0.6595538\tbest: 0.6595538 (5)\ttotal: 215ms\tremaining: 35.7s\n",
      "6:\tlearn: 0.6317956\ttest: 0.6558876\tbest: 0.6558876 (6)\ttotal: 274ms\tremaining: 38.9s\n",
      "7:\tlearn: 0.6255424\ttest: 0.6527893\tbest: 0.6527893 (7)\ttotal: 311ms\tremaining: 38.6s\n",
      "8:\tlearn: 0.6203549\ttest: 0.6502128\tbest: 0.6502128 (8)\ttotal: 353ms\tremaining: 38.8s\n",
      "9:\tlearn: 0.6146605\ttest: 0.6485700\tbest: 0.6485700 (9)\ttotal: 399ms\tremaining: 39.5s\n",
      "10:\tlearn: 0.6103245\ttest: 0.6464661\tbest: 0.6464661 (10)\ttotal: 466ms\tremaining: 41.9s\n",
      "11:\tlearn: 0.6058457\ttest: 0.6448778\tbest: 0.6448778 (11)\ttotal: 503ms\tremaining: 41.4s\n",
      "12:\tlearn: 0.6013905\ttest: 0.6440380\tbest: 0.6440380 (12)\ttotal: 546ms\tremaining: 41.5s\n",
      "13:\tlearn: 0.5981608\ttest: 0.6425744\tbest: 0.6425744 (13)\ttotal: 594ms\tremaining: 41.8s\n",
      "14:\tlearn: 0.5944176\ttest: 0.6416887\tbest: 0.6416887 (14)\ttotal: 639ms\tremaining: 42s\n",
      "15:\tlearn: 0.5914759\ttest: 0.6412195\tbest: 0.6412195 (15)\ttotal: 679ms\tremaining: 41.8s\n",
      "16:\tlearn: 0.5892057\ttest: 0.6403254\tbest: 0.6403254 (16)\ttotal: 718ms\tremaining: 41.5s\n",
      "17:\tlearn: 0.5871799\ttest: 0.6385411\tbest: 0.6385411 (17)\ttotal: 775ms\tremaining: 42.3s\n",
      "18:\tlearn: 0.5843436\ttest: 0.6384900\tbest: 0.6384900 (18)\ttotal: 815ms\tremaining: 42.1s\n",
      "19:\tlearn: 0.5818187\ttest: 0.6387014\tbest: 0.6384900 (18)\ttotal: 855ms\tremaining: 41.9s\n",
      "20:\tlearn: 0.5799996\ttest: 0.6384077\tbest: 0.6384077 (20)\ttotal: 895ms\tremaining: 41.7s\n",
      "21:\tlearn: 0.5786538\ttest: 0.6370612\tbest: 0.6370612 (21)\ttotal: 948ms\tremaining: 42.1s\n",
      "22:\tlearn: 0.5769579\ttest: 0.6367726\tbest: 0.6367726 (22)\ttotal: 1s\tremaining: 42.7s\n",
      "23:\tlearn: 0.5758619\ttest: 0.6356003\tbest: 0.6356003 (23)\ttotal: 1.04s\tremaining: 42.5s\n",
      "24:\tlearn: 0.5742413\ttest: 0.6358360\tbest: 0.6356003 (23)\ttotal: 1.09s\tremaining: 42.6s\n",
      "25:\tlearn: 0.5729303\ttest: 0.6347711\tbest: 0.6347711 (25)\ttotal: 1.14s\tremaining: 42.6s\n",
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      "963:\tlearn: 0.3102336\ttest: 0.7620893\tbest: 0.6347711 (25)\ttotal: 51.8s\tremaining: 1.93s\n",
      "964:\tlearn: 0.3101443\ttest: 0.7621195\tbest: 0.6347711 (25)\ttotal: 51.8s\tremaining: 1.88s\n",
      "965:\tlearn: 0.3098964\ttest: 0.7621047\tbest: 0.6347711 (25)\ttotal: 51.9s\tremaining: 1.82s\n",
      "966:\tlearn: 0.3097228\ttest: 0.7622763\tbest: 0.6347711 (25)\ttotal: 51.9s\tremaining: 1.77s\n",
      "967:\tlearn: 0.3095125\ttest: 0.7622231\tbest: 0.6347711 (25)\ttotal: 52s\tremaining: 1.72s\n",
      "968:\tlearn: 0.3093550\ttest: 0.7626453\tbest: 0.6347711 (25)\ttotal: 52s\tremaining: 1.66s\n",
      "969:\tlearn: 0.3091478\ttest: 0.7624921\tbest: 0.6347711 (25)\ttotal: 52.1s\tremaining: 1.61s\n",
      "970:\tlearn: 0.3089083\ttest: 0.7626640\tbest: 0.6347711 (25)\ttotal: 52.1s\tremaining: 1.56s\n",
      "971:\tlearn: 0.3086067\ttest: 0.7624943\tbest: 0.6347711 (25)\ttotal: 52.2s\tremaining: 1.5s\n",
      "972:\tlearn: 0.3085265\ttest: 0.7623893\tbest: 0.6347711 (25)\ttotal: 52.2s\tremaining: 1.45s\n",
      "973:\tlearn: 0.3083895\ttest: 0.7621968\tbest: 0.6347711 (25)\ttotal: 52.3s\tremaining: 1.4s\n",
      "974:\tlearn: 0.3082321\ttest: 0.7625009\tbest: 0.6347711 (25)\ttotal: 52.4s\tremaining: 1.34s\n",
      "975:\tlearn: 0.3081045\ttest: 0.7625397\tbest: 0.6347711 (25)\ttotal: 52.4s\tremaining: 1.29s\n",
      "976:\tlearn: 0.3080118\ttest: 0.7625837\tbest: 0.6347711 (25)\ttotal: 52.5s\tremaining: 1.24s\n",
      "977:\tlearn: 0.3079721\ttest: 0.7629078\tbest: 0.6347711 (25)\ttotal: 52.5s\tremaining: 1.18s\n",
      "978:\tlearn: 0.3077400\ttest: 0.7640410\tbest: 0.6347711 (25)\ttotal: 52.6s\tremaining: 1.13s\n",
      "979:\tlearn: 0.3076462\ttest: 0.7640296\tbest: 0.6347711 (25)\ttotal: 52.6s\tremaining: 1.07s\n",
      "980:\tlearn: 0.3075029\ttest: 0.7641482\tbest: 0.6347711 (25)\ttotal: 52.7s\tremaining: 1.02s\n",
      "981:\tlearn: 0.3072351\ttest: 0.7640680\tbest: 0.6347711 (25)\ttotal: 52.7s\tremaining: 966ms\n",
      "982:\tlearn: 0.3069767\ttest: 0.7641249\tbest: 0.6347711 (25)\ttotal: 52.8s\tremaining: 913ms\n",
      "983:\tlearn: 0.3068168\ttest: 0.7641216\tbest: 0.6347711 (25)\ttotal: 52.8s\tremaining: 859ms\n",
      "984:\tlearn: 0.3067280\ttest: 0.7639062\tbest: 0.6347711 (25)\ttotal: 52.9s\tremaining: 805ms\n",
      "985:\tlearn: 0.3065596\ttest: 0.7631025\tbest: 0.6347711 (25)\ttotal: 52.9s\tremaining: 751ms\n",
      "986:\tlearn: 0.3064533\ttest: 0.7631181\tbest: 0.6347711 (25)\ttotal: 53s\tremaining: 698ms\n",
      "987:\tlearn: 0.3062654\ttest: 0.7631261\tbest: 0.6347711 (25)\ttotal: 53s\tremaining: 644ms\n",
      "988:\tlearn: 0.3061595\ttest: 0.7632118\tbest: 0.6347711 (25)\ttotal: 53.1s\tremaining: 591ms\n",
      "989:\tlearn: 0.3060354\ttest: 0.7632220\tbest: 0.6347711 (25)\ttotal: 53.2s\tremaining: 537ms\n",
      "990:\tlearn: 0.3058538\ttest: 0.7619987\tbest: 0.6347711 (25)\ttotal: 53.2s\tremaining: 483ms\n",
      "991:\tlearn: 0.3057001\ttest: 0.7619755\tbest: 0.6347711 (25)\ttotal: 53.2s\tremaining: 429ms\n",
      "992:\tlearn: 0.3055008\ttest: 0.7622250\tbest: 0.6347711 (25)\ttotal: 53.3s\tremaining: 376ms\n",
      "993:\tlearn: 0.3054946\ttest: 0.7622241\tbest: 0.6347711 (25)\ttotal: 53.3s\tremaining: 322ms\n",
      "994:\tlearn: 0.3052680\ttest: 0.7622716\tbest: 0.6347711 (25)\ttotal: 53.4s\tremaining: 268ms\n",
      "995:\tlearn: 0.3049605\ttest: 0.7622577\tbest: 0.6347711 (25)\ttotal: 53.5s\tremaining: 215ms\n",
      "996:\tlearn: 0.3048505\ttest: 0.7627939\tbest: 0.6347711 (25)\ttotal: 53.5s\tremaining: 161ms\n",
      "997:\tlearn: 0.3048039\ttest: 0.7626071\tbest: 0.6347711 (25)\ttotal: 53.6s\tremaining: 107ms\n",
      "998:\tlearn: 0.3046538\ttest: 0.7626050\tbest: 0.6347711 (25)\ttotal: 53.6s\tremaining: 53.7ms\n",
      "999:\tlearn: 0.3042291\ttest: 0.7623233\tbest: 0.6347711 (25)\ttotal: 53.7s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.6347711365\n",
      "bestIteration = 25\n",
      "\n",
      "Shrink model to first 26 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6833333333333333"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=CatBoostClassifier()\n",
    "categorical_features_indices = np.where(df.dtypes != np.float)[0]\n",
    "model.fit(X_train,y_train,cat_features=([ 0,  1, 2, 3, 4, 10]),eval_set=(X_test, y_test))\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running a loop with increasing number of trees in the random forest and checking accuracy of confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criterion1  : 'gini' \n",
    "# Criterion2 : 'entropy'\n",
    "\n",
    "nsimu = 21\n",
    "accuracy=[0]*nsimu\n",
    "ntree = [0]*nsimu\n",
    "for i in range(1,nsimu):\n",
    "    rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=10,max_depth=None,criterion='gini')\n",
    "    rfc.fit(X_train, y_train)\n",
    "    rfc_pred = rfc.predict(X_test)\n",
    "    cm = confusion_matrix(y_test,rfc_pred)\n",
    "    accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
    "    ntree[i]=i*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prediction accuracy from confusion matrix')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
    "plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (criterion: 'gini')\", fontsize=18)\n",
    "plt.xlabel(\"Number of trees\", fontsize=15)\n",
    "plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "nsimu = 21\n",
    "accuracy=[0]*nsimu\n",
    "ntree = [0]*nsimu\n",
    "for i in range(1,nsimu):\n",
    "    rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=10,max_depth=None,criterion='entropy')\n",
    "    rfc.fit(X_train, y_train)\n",
    "    rfc_pred = rfc.predict(X_test)\n",
    "    cm = confusion_matrix(y_test,rfc_pred)\n",
    "    accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
    "    ntree[i]=i*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prediction accuracy from confusion matrix')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
    "plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (criterion: 'entropy')\", fontsize=18)\n",
    "plt.xlabel(\"Number of trees\", fontsize=15)\n",
    "plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fixing max tree depth\n",
    "\n",
    "nsimu = 21\n",
    "accuracy=[0]*nsimu\n",
    "ntree = [0]*nsimu\n",
    "for i in range(1,nsimu):\n",
    "    rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=10,max_depth=None,criterion='gini')\n",
    "    rfc.fit(X_train, y_train)\n",
    "    rfc_pred = rfc.predict(X_test)\n",
    "    cm = confusion_matrix(y_test,rfc_pred)\n",
    "    accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
    "    ntree[i]=i*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prediction accuracy from confusion matrix')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
    "plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (max depth: None)\", fontsize=18)\n",
    "plt.xlabel(\"Number of trees\", fontsize=15)\n",
    "plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "nsimu = 21\n",
    "accuracy=[0]*nsimu\n",
    "ntree = [0]*nsimu\n",
    "for i in range(1,nsimu):\n",
    "    rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=10,max_depth=5,criterion='gini')\n",
    "    rfc.fit(X_train, y_train)\n",
    "    rfc_pred = rfc.predict(X_test)\n",
    "    cm = confusion_matrix(y_test,rfc_pred)\n",
    "    accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
    "    ntree[i]=i*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prediction accuracy from confusion matrix')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
    "plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (max depth: 5)\", fontsize=18)\n",
    "plt.xlabel(\"Number of trees\", fontsize=15)\n",
    "plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Minimum sample split criteria\n",
    "\n",
    "nsimu = 21\n",
    "accuracy=[0]*nsimu\n",
    "ntree = [0]*nsimu\n",
    "for i in range(1,nsimu):\n",
    "    rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=2,max_depth=None,criterion='gini')\n",
    "    rfc.fit(X_train, y_train)\n",
    "    rfc_pred = rfc.predict(X_test)\n",
    "    cm = confusion_matrix(y_test,rfc_pred)\n",
    "    accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
    "    ntree[i]=i*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prediction accuracy from confusion matrix')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
    "plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (minimum sample split: 2)\", fontsize=18)\n",
    "plt.xlabel(\"Number of trees\", fontsize=15)\n",
    "plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "nsimu = 21\n",
    "accuracy=[0]*nsimu\n",
    "ntree = [0]*nsimu\n",
    "for i in range(1,nsimu):\n",
    "    rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=20,max_depth=None,criterion='gini')\n",
    "    rfc.fit(X_train, y_train)\n",
    "    rfc_pred = rfc.predict(X_test)\n",
    "    cm = confusion_matrix(y_test,rfc_pred)\n",
    "    accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
    "    ntree[i]=i*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prediction accuracy from confusion matrix')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
    "plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (minimum sample split: 20)\", fontsize=18)\n",
    "plt.xlabel(\"Number of trees\", fontsize=15)\n",
    "plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}