Credit Risk Modelling in Python.ipynb

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   "source": [
    "# Credit Risk Modelling in Python\n",
    "\n",
    "Credit risk is the possibility that a borrower will not be able to make timely payments and will default on their debt. It refers to the possibility that a lender may not get the interest or principle due to them on time.\n",
    "\n",
    "Financial organisations are concerned about reducing the risk of default. As a result, commercial and investment banks, venture capital funds, asset management organisations, and insurance corporations, to mention a few, are increasingly depending on technology to anticipate which customers are most likely to default on their obligations.\n",
    "\n",
    "#### Machine Learning models have helped these companies in improving the accuracy of their credit risk analysis by giving a scientific technique for predicting potential debtors.\n",
    "\n",
    "##### The objective of this article is to build a model to predict probability of person defaulting a loan. The following steps will be followed in building the model.\n",
    "\n",
    "- Data Preprocessing and data prepration\n",
    "- Features Engineering and Features selection\n",
    "- Model Developement and Model Evaluations\n",
    "\n",
    "Data links:\n",
    "https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "584a6a54",
   "metadata": {},
   "source": [
    "## Importing the libraries and reading the data"
   ]
  },
  {
   "cell_type": "code",
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   "id": "e5ae8eed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
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   "id": "47f37d65",
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       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
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       "\n",
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Reading the data \n",
    "credit_risk= pd.read_csv(\"UCI_credit_card.csv\")\n",
    "credit_risk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f188348",
   "metadata": {},
   "outputs": [
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       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0   1    20000.0    2          2         1   24      2      2     -1     -1   \n",
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       "2   3    90000.0    2          2         2   34      0      0      0      0   \n",
       "3   4    50000.0    2          2         1   37      0      0      0      0   \n",
       "4   5    50000.0    1          2         1   57     -1      0     -1      0   \n",
       "\n",
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       "4  ...    20940.0    19146.0    19131.0    2000.0   36681.0   10000.0   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  default.payment.next.month  \n",
       "0       0.0       0.0       0.0                           1  \n",
       "1    1000.0       0.0    2000.0                           1  \n",
       "2    1000.0    1000.0    5000.0                           0  \n",
       "3    1100.0    1069.0    1000.0                           0  \n",
       "4    9000.0     689.0     679.0                           0  \n",
       "\n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Here I am doing to copy the original data in data frame called df.\n",
    "df= credit_risk.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2883eb4d",
   "metadata": {},
   "source": [
    "## Data preparation and Pre-processing\n",
    "\n",
    "The data set was taken from kaggle for credit risk modelling. The data initially revealed a total of 25 features. It is very important to clean the data in a suitable format before developing any machine learning model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1cecae26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30000 entries, 0 to 29999\n",
      "Data columns (total 25 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   ID                          30000 non-null  int64  \n",
      " 1   LIMIT_BAL                   30000 non-null  float64\n",
      " 2   SEX                         30000 non-null  int64  \n",
      " 3   EDUCATION                   30000 non-null  int64  \n",
      " 4   MARRIAGE                    30000 non-null  int64  \n",
      " 5   AGE                         30000 non-null  int64  \n",
      " 6   PAY_0                       30000 non-null  int64  \n",
      " 7   PAY_2                       30000 non-null  int64  \n",
      " 8   PAY_3                       30000 non-null  int64  \n",
      " 9   PAY_4                       30000 non-null  int64  \n",
      " 10  PAY_5                       30000 non-null  int64  \n",
      " 11  PAY_6                       30000 non-null  int64  \n",
      " 12  BILL_AMT1                   30000 non-null  float64\n",
      " 13  BILL_AMT2                   30000 non-null  float64\n",
      " 14  BILL_AMT3                   30000 non-null  float64\n",
      " 15  BILL_AMT4                   30000 non-null  float64\n",
      " 16  BILL_AMT5                   30000 non-null  float64\n",
      " 17  BILL_AMT6                   30000 non-null  float64\n",
      " 18  PAY_AMT1                    30000 non-null  float64\n",
      " 19  PAY_AMT2                    30000 non-null  float64\n",
      " 20  PAY_AMT3                    30000 non-null  float64\n",
      " 21  PAY_AMT4                    30000 non-null  float64\n",
      " 22  PAY_AMT5                    30000 non-null  float64\n",
      " 23  PAY_AMT6                    30000 non-null  float64\n",
      " 24  default.payment.next.month  30000 non-null  int64  \n",
      "dtypes: float64(13), int64(12)\n",
      "memory usage: 5.7 MB\n"
     ]
    }
   ],
   "source": [
    "# Lets see the information of data\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c96271",
   "metadata": {},
   "source": [
    "We are working with a data set containing 25 features for 30,000 clients. \"default.payment.next.month\" is a feature and is the target variable we are trying to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "89aa3710",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we seen Column ID has no meaning here so, we will remove it\n",
    "df.drop([\"ID\"], axis=1, inplace= True) #axis=1 -- column removal and inplcae= True --means change in the original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ddf5cab4",
   "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>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default.payment.next.month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.00000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>167484.322667</td>\n",
       "      <td>1.603733</td>\n",
       "      <td>1.853133</td>\n",
       "      <td>1.551867</td>\n",
       "      <td>35.485500</td>\n",
       "      <td>-0.016700</td>\n",
       "      <td>-0.133767</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>-0.220667</td>\n",
       "      <td>-0.266200</td>\n",
       "      <td>...</td>\n",
       "      <td>43262.948967</td>\n",
       "      <td>40311.400967</td>\n",
       "      <td>38871.760400</td>\n",
       "      <td>5663.580500</td>\n",
       "      <td>5.921163e+03</td>\n",
       "      <td>5225.68150</td>\n",
       "      <td>4826.076867</td>\n",
       "      <td>4799.387633</td>\n",
       "      <td>5215.502567</td>\n",
       "      <td>0.221200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>129747.661567</td>\n",
       "      <td>0.489129</td>\n",
       "      <td>0.790349</td>\n",
       "      <td>0.521970</td>\n",
       "      <td>9.217904</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>1.197186</td>\n",
       "      <td>1.196868</td>\n",
       "      <td>1.169139</td>\n",
       "      <td>1.133187</td>\n",
       "      <td>...</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>59554.107537</td>\n",
       "      <td>16563.280354</td>\n",
       "      <td>2.304087e+04</td>\n",
       "      <td>17606.96147</td>\n",
       "      <td>15666.159744</td>\n",
       "      <td>15278.305679</td>\n",
       "      <td>17777.465775</td>\n",
       "      <td>0.415062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2326.750000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.330000e+02</td>\n",
       "      <td>390.00000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>252.500000</td>\n",
       "      <td>117.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>140000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19052.000000</td>\n",
       "      <td>18104.500000</td>\n",
       "      <td>17071.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>1800.00000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>240000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>54506.000000</td>\n",
       "      <td>50190.500000</td>\n",
       "      <td>49198.250000</td>\n",
       "      <td>5006.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4505.00000</td>\n",
       "      <td>4013.250000</td>\n",
       "      <td>4031.500000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>891586.000000</td>\n",
       "      <td>927171.000000</td>\n",
       "      <td>961664.000000</td>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.684259e+06</td>\n",
       "      <td>896040.00000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            LIMIT_BAL           SEX     EDUCATION      MARRIAGE           AGE  \\\n",
       "count    30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean    167484.322667      1.603733      1.853133      1.551867     35.485500   \n",
       "std     129747.661567      0.489129      0.790349      0.521970      9.217904   \n",
       "min      10000.000000      1.000000      0.000000      0.000000     21.000000   \n",
       "25%      50000.000000      1.000000      1.000000      1.000000     28.000000   \n",
       "50%     140000.000000      2.000000      2.000000      2.000000     34.000000   \n",
       "75%     240000.000000      2.000000      2.000000      2.000000     41.000000   \n",
       "max    1000000.000000      2.000000      6.000000      3.000000     79.000000   \n",
       "\n",
       "              PAY_0         PAY_2         PAY_3         PAY_4         PAY_5  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean      -0.016700     -0.133767     -0.166200     -0.220667     -0.266200   \n",
       "std        1.123802      1.197186      1.196868      1.169139      1.133187   \n",
       "min       -2.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       -1.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        8.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "       ...      BILL_AMT4      BILL_AMT5      BILL_AMT6       PAY_AMT1  \\\n",
       "count  ...   30000.000000   30000.000000   30000.000000   30000.000000   \n",
       "mean   ...   43262.948967   40311.400967   38871.760400    5663.580500   \n",
       "std    ...   64332.856134   60797.155770   59554.107537   16563.280354   \n",
       "min    ... -170000.000000  -81334.000000 -339603.000000       0.000000   \n",
       "25%    ...    2326.750000    1763.000000    1256.000000    1000.000000   \n",
       "50%    ...   19052.000000   18104.500000   17071.000000    2100.000000   \n",
       "75%    ...   54506.000000   50190.500000   49198.250000    5006.000000   \n",
       "max    ...  891586.000000  927171.000000  961664.000000  873552.000000   \n",
       "\n",
       "           PAY_AMT2      PAY_AMT3       PAY_AMT4       PAY_AMT5  \\\n",
       "count  3.000000e+04   30000.00000   30000.000000   30000.000000   \n",
       "mean   5.921163e+03    5225.68150    4826.076867    4799.387633   \n",
       "std    2.304087e+04   17606.96147   15666.159744   15278.305679   \n",
       "min    0.000000e+00       0.00000       0.000000       0.000000   \n",
       "25%    8.330000e+02     390.00000     296.000000     252.500000   \n",
       "50%    2.009000e+03    1800.00000    1500.000000    1500.000000   \n",
       "75%    5.000000e+03    4505.00000    4013.250000    4031.500000   \n",
       "max    1.684259e+06  896040.00000  621000.000000  426529.000000   \n",
       "\n",
       "            PAY_AMT6  default.payment.next.month  \n",
       "count   30000.000000                30000.000000  \n",
       "mean     5215.502567                    0.221200  \n",
       "std     17777.465775                    0.415062  \n",
       "min         0.000000                    0.000000  \n",
       "25%       117.750000                    0.000000  \n",
       "50%      1500.000000                    0.000000  \n",
       "75%      4000.000000                    0.000000  \n",
       "max    528666.000000                    1.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets check the statistics of data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "01aee923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIMIT_BAL                     0\n",
       "SEX                           0\n",
       "EDUCATION                     0\n",
       "MARRIAGE                      0\n",
       "AGE                           0\n",
       "PAY_0                         0\n",
       "PAY_2                         0\n",
       "PAY_3                         0\n",
       "PAY_4                         0\n",
       "PAY_5                         0\n",
       "PAY_6                         0\n",
       "BILL_AMT1                     0\n",
       "BILL_AMT2                     0\n",
       "BILL_AMT3                     0\n",
       "BILL_AMT4                     0\n",
       "BILL_AMT5                     0\n",
       "BILL_AMT6                     0\n",
       "PAY_AMT1                      0\n",
       "PAY_AMT2                      0\n",
       "PAY_AMT3                      0\n",
       "PAY_AMT4                      0\n",
       "PAY_AMT5                      0\n",
       "PAY_AMT6                      0\n",
       "default.payment.next.month    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7de755",
   "metadata": {},
   "source": [
    " We do not find any missing values in our data set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "273a4228",
   "metadata": {},
   "source": [
    "### Analysis of column \"EDUCATION\"\n",
    "\n",
    "EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "780252c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14030\n",
       "1    10585\n",
       "3     4917\n",
       "5      280\n",
       "4      123\n",
       "6       51\n",
       "0       14\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets see the value counts in EDUCATION columns:\n",
    "df.EDUCATION.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0857428b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14030\n",
       "1    10930\n",
       "3     4917\n",
       "4      123\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From the Data Description given, we know that in df.EDUCATION, 5 and 6 represents \"unknown\" \n",
    "#Changing 0,5 and 6 to keep it under 1 category.\n",
    "\n",
    "df['EDUCATION'].replace({0:1,1:1,2:2,3:3,4:4,5:1,6:1}, inplace=True)\n",
    "df.EDUCATION.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4efab17",
   "metadata": {},
   "source": [
    "### Aalysis of column \"MARRIAGE\"\n",
    "\n",
    "Marital status (1=married, 2=single, 3=others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e403e333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    15964\n",
       "1    13659\n",
       "3      323\n",
       "0       54\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets see the values count in column marriage\n",
    "df['MARRIAGE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d29771fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    15964\n",
       "1    13713\n",
       "3      323\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here I am going to map 0 with 1\n",
    "df['MARRIAGE'].replace({0:1,1:1,2:2,3:3}, inplace=True)\n",
    "df['MARRIAGE'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de5db1b6",
   "metadata": {},
   "source": [
    "### Analysis of column \"PAY_0 to PAY_6\"\n",
    "\n",
    "PAY_0: Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, … 8=payment delay for eight months, 9=payment delay for nine months and above)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e3e29db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    14737\n",
       "-1     5686\n",
       " 1     3688\n",
       "-2     2759\n",
       " 2     2667\n",
       " 3      322\n",
       " 4       76\n",
       " 5       26\n",
       " 8       19\n",
       " 6       11\n",
       " 7        9\n",
       "Name: PAY_0, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets see the value counts in column 'PAY_0'\n",
    "df['PAY_0'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7013e4",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9fd0c9e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Target Distribution')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets visualize the target column \"default.payment.next.month\"\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.countplot(df['default.payment.next.month'])\n",
    "plt.xticks([0,1], labels=[\"Not Deafaulted\", \"Defaulted\"])\n",
    "plt.title(\"Target Distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f7c9b4fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23364\n",
       "1     6636\n",
       "Name: default.payment.next.month, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets count it\n",
    "df['default.payment.next.month'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c839e8",
   "metadata": {},
   "source": [
    "So, By looking at the target varibale we could say that, our data is pretty much imbalance. We would like to make it balanced before going to trained the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b397264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age distribution')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# with age column\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.displot(df['AGE'], kde=True)\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylabel('Count')\n",
    "plt.title(\"Age distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0566946c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gender Distribution')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# With sex columns \n",
    "#Gender (1=male, 2=female)\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.countplot('SEX',hue='default.payment.next.month',data=df)\n",
    "plt.xticks([0,1], labels=[\"Male\", \"Female\"])\n",
    "#plt.xlabel({1:'Male',2:'Feamle'})\n",
    "plt.title(\"Gender Distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fe3748e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Education Distribution')"
      ]
     },
     "execution_count": 17,
     "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": [
    "# With EDUCATION columns \n",
    "# (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot('EDUCATION', hue='default.payment.next.month',data=df)\n",
    "plt.xticks([0,1,2,3],labels=[\"graduate school\", \"university\",'high school','others'])\n",
    "plt.title(\"Education Distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ec6a750e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'MARRIAGE Distribution')"
      ]
     },
     "execution_count": 18,
     "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": [
    "# With MARRIAGE columns \n",
    "#\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot('MARRIAGE',hue='default.payment.next.month',data=df)\n",
    "plt.xticks([0,1,2],labels=[\"Married\", \"single\",'others'])\n",
    "plt.title(\"MARRIAGE Distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9cdfe56e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x15f04c16970>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# With column 'limit_bal'\n",
    "sns.displot(df.LIMIT_BAL, kde=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b39fd737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(20,10))\n",
    "plt.subplot(231)\n",
    "plt.scatter(x=df.PAY_AMT1, y=df.BILL_AMT1, c='r', s=1)\n",
    "plt.xlabel('PAY_AMT1')\n",
    "plt.ylabel('BILL_AMT1')\n",
    "\n",
    "plt.subplot(232)\n",
    "plt.scatter(x=df.PAY_AMT2, y=df.BILL_AMT2, c='g', s=1)\n",
    "plt.xlabel('PAY_AMT2')\n",
    "plt.ylabel('BILL_AMT2')\n",
    "plt.title('Payment structure vs Bill amount in the last 6 months', fontsize=15)\n",
    "\n",
    "plt.subplot(233)\n",
    "plt.scatter(x=df.PAY_AMT3, y=df.BILL_AMT3, c='b', s=1)\n",
    "plt.xlabel('PAY_AMT3')\n",
    "plt.ylabel('BILL_AMT3')\n",
    "\n",
    "plt.subplot(234)\n",
    "plt.scatter(x=df.PAY_AMT4, y=df.BILL_AMT4, c='y', s=1)\n",
    "plt.xlabel('PAY_AMT4')\n",
    "plt.ylabel('BILL_AMT4')\n",
    "\n",
    "plt.subplot(235)\n",
    "plt.scatter(x=df.PAY_AMT5, y=df.BILL_AMT5, c='black', s=1)\n",
    "plt.xlabel('PAY_AMT5')\n",
    "plt.ylabel('BILL_AMT5')\n",
    "\n",
    "plt.subplot(236)\n",
    "plt.scatter(x=df.PAY_AMT6, y=df.BILL_AMT6, c='m', s=1)\n",
    "plt.xlabel('PAY_AMT6')\n",
    "plt.ylabel('BILL_AMT6')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d15c7cb8",
   "metadata": {},
   "source": [
    "## Creating Independent features and depened features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "340897b3",
   "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>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2682.0</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13559.0</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>49291.0</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>35835.0</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0    20000.0    2          2         1   24      2      2     -1     -1   \n",
       "1   120000.0    2          2         2   26     -1      2      0      0   \n",
       "2    90000.0    2          2         2   34      0      0      0      0   \n",
       "3    50000.0    2          2         1   37      0      0      0      0   \n",
       "4    50000.0    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "   PAY_5  ...  BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "0     -2  ...      689.0        0.0        0.0        0.0       0.0     689.0   \n",
       "1      0  ...     2682.0     3272.0     3455.0     3261.0       0.0    1000.0   \n",
       "2      0  ...    13559.0    14331.0    14948.0    15549.0    1518.0    1500.0   \n",
       "3      0  ...    49291.0    28314.0    28959.0    29547.0    2000.0    2019.0   \n",
       "4      0  ...    35835.0    20940.0    19146.0    19131.0    2000.0   36681.0   \n",
       "\n",
       "   PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \n",
       "0       0.0       0.0       0.0       0.0  \n",
       "1    1000.0    1000.0       0.0    2000.0  \n",
       "2    1000.0    1000.0    1000.0    5000.0  \n",
       "3    1200.0    1100.0    1069.0    1000.0  \n",
       "4   10000.0    9000.0     689.0     679.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Independnet features\n",
    "X = df.drop(['default.payment.next.month'], axis=1)\n",
    "# Dependent feature\n",
    "y = df['default.payment.next.month']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e745a68d",
   "metadata": {},
   "source": [
    "## Scalling the features\n",
    "- So, scalling the independent features are very important so that our model is not biased toward the higher range of values. To make all features in same range\n",
    "- Here we are using StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9bb00aa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler= StandardScaler()\n",
    "X= scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "519a1d3e",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "73c6f8ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test= train_test_split(X,y,test_size=0.20,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cd8e06e",
   "metadata": {},
   "source": [
    "## Balancing the data\n",
    "- since our dataset was imbalace, so used SMOTE technique to make it balanced.\n",
    "- For over-sampling techniques, SMOTE (Synthetic Minority Oversampling Technique) is considered as one of the most popular and influential data sampling algorithms in ML and data mining. With SMOTE, the minority class is over-sampled by creating “synthetic” examples rather than by over-sampling with replacement [2]. These introduced synthetic examples are based along the line segments joining a defined number of k minority class nearest neighbours, which is in the imblearn package is set at five by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7e6adc78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before oversampling:  Counter({0: 18677, 1: 18677})\n",
      "After oversampling:  Counter({0: 18677, 1: 18677})\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from collections import Counter\n",
    "\n",
    "# summarize class distribution\n",
    "print(\"Before oversampling: \",Counter(y_train))\n",
    "\n",
    "# define oversampling strategy\n",
    "SMOTE= SMOTE()\n",
    "\n",
    "# fit and apply the transform \n",
    "X_train,y_train= SMOTE.fit_resample(X_train,y_train)\n",
    "\n",
    "# summarize class distribution\n",
    "print(\"After oversampling: \",Counter(y_train))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c569694",
   "metadata": {},
   "source": [
    "## Building Model\n",
    "- Logistic Regression\n",
    "- Random Forest Classifier\n",
    "- XGBoost Classifier\n",
    "- Support vector machine classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecfbbfd3",
   "metadata": {},
   "source": [
    "### Logistic regression Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8dfd0dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logit= LogisticRegression()\n",
    "logit.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c240f261",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the model\n",
    "pred_logit= logit.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "876ef38f",
   "metadata": {},
   "source": [
    "#### Evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f3564327",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of logit model is: 0.6868333333333333\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.70      0.78      4687\n",
      "           1       0.38      0.66      0.48      1313\n",
      "\n",
      "    accuracy                           0.69      6000\n",
      "   macro avg       0.63      0.68      0.63      6000\n",
      "weighted avg       0.77      0.69      0.71      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, roc_auc_score, plot_confusion_matrix, plot_precision_recall_curve\n",
    "\n",
    "print(\"The accuracy of logit model is:\", accuracy_score(y_test, pred_logit))\n",
    "print(classification_report(y_test, pred_logit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "91b20378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x15f04dae3d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion metrics\n",
    "plot_confusion_matrix(logit, X_test, y_test, cmap=\"Blues_r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1cb7d658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x15f09c06dc0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot roc_auc curve\n",
    "plot_precision_recall_curve(logit,X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7cb130",
   "metadata": {},
   "source": [
    "### Random Forest Classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f7a7f3f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf= RandomForestClassifier()\n",
    "# Fitting the model\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "44985d1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the model\n",
    "pred_rf= rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb3a017",
   "metadata": {},
   "source": [
    "#### Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cb87d1f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of logit model is: 0.795\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.89      0.87      4687\n",
      "           1       0.54      0.47      0.50      1313\n",
      "\n",
      "    accuracy                           0.80      6000\n",
      "   macro avg       0.70      0.68      0.69      6000\n",
      "weighted avg       0.79      0.80      0.79      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"The accuracy of logit model is:\", accuracy_score(y_test, pred_rf))\n",
    "print(classification_report(y_test,pred_rf ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4c933c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x15f09c06070>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion metrics\n",
    "plot_confusion_matrix(rf, X_test, y_test, cmap=\"Blues_r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c1b740ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x15f03dc9100>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot pprcision_recall curve\n",
    "plot_precision_recall_curve(rf,X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c3df912",
   "metadata": {},
   "source": [
    "### XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2bfe3874",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:32:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=8,\n",
       "              num_parallel_tree=1, predictor='auto', 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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "xgb_clf= xgb.XGBClassifier()\n",
    "#fitting the model\n",
    "xgb_clf.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f5da2d4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Predicting the model\n",
    "xgb_predict= xgb_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71266ac0",
   "metadata": {},
   "source": [
    "### Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6cb792e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of logit model is: 0.8091666666666667\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.92      0.88      4687\n",
      "           1       0.60      0.40      0.48      1313\n",
      "\n",
      "    accuracy                           0.81      6000\n",
      "   macro avg       0.72      0.66      0.68      6000\n",
      "weighted avg       0.79      0.81      0.79      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"The accuracy of logit model is:\", accuracy_score(y_test, xgb_predict))\n",
    "print(classification_report(y_test,xgb_predict ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f706130a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x15f0a22a7c0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YmLb7AdKxEUB/cktcjkuLHwGMB0aRW9GtXzpeUKF3e/dIu49LugiYRC7ofRW4r8mvZWYVKYDNzfDQLy05uSLtr5e0gFzHamOGAZMiYhOwRNJiYIikV4DOEfE0gKRbgeE0sYJboQ6PmWy9Gvq5+eUGrix0YTOrXM09zk/SvuRWcnsWOBq4QNJIcnMMfDci3iAXGJ/JO21ZSvsg7W+bXlChd3v7Ziy/mVWByPb2RldJ+ZOkTIiICfkZJO0K3AF8KyLeljSeXOWqvpJ1NXA2H1bEtipOgfSCinrDQ9IA4GCg45YrR9xazLlmVnky1PzWRMTgxg5Kakcu8P0uIu7MXTtW5h2/Abg3fVwG9M47vRewPKX3aiC9oGKGulwK/Hfajgf+C/hSU+eZWeWqK3IrJPXI3gQsiIhr8tJ75mU7FXgx7U8BRkjqIKkvuY6N6enZ4XpJR6ZrjgTubuo7FFPz+wpwKPBCRHxDUg/gxiLOM7MK1Iyvtx0NnAnMlTQrpV0CnC5pYLrVK6T+hoiYJ2kyMJ9cT/HoiKifZ+B84BagE7mOjoKdHVBc8NsYEXWSNkvqDKwCPMjZrIo1R+yLiKdo+Hnd/QXOGQOMaSB9BjAgy/2LCX4zJO0O3ECuB3gDMD3LTcyscuTG8LX+ob7FvNv772n315IeJDeeZk5pi2VmLVklvNtbaJDz4YWORcTzpSmSmbV0FR38yI2taUwAJzRzWXjhpVV0Gf6r5r6sldIuHy93CSyLNjVN52lCbt3e1h/9Cg1yPn5nFsTMWo/aSg5+ZmYNqqJp7M3Mtgha/uJExXDwM7PMKiD2FfV6myR9XdJP0ud9JA0pfdHMrKWKiKK2lqyYyUzHAZ8BTk+f1wPukjWrYs00mWlZFdPs/YeIOFzSCwAR8UZawtLMqlBE9fT2fpCmig4ASd1oesIGM6tgLb1JW4xigt9Y4C6gu6Qx5GZ5+XFJS2VmLValLF1ZzLu9v5M0EziR3AwMwyNiQclLZmYtVlXU/CTtA7wL3JOfFhFLS1kwM2u5KiD2FdXsvY8P58nvCPQFFpJbPs7MqkxENNdkpmVVTLP30/mf02wv5zaS3cyqQFU0e7cVEc9LOqIUhTGz1qH1h77invl9J+9jG+BwYHXJSmRmLV4lvNtbzBseu+VtHcg9AxxWykKZWctV7NsdTcVHSb0lPS5pgaR5kr6Z0veQ9IikRelnl7xzLpa0WNJCSSflpQ+SNDcdG5tWcSuoYM0vDW7eNSK+39SFzKx6NNMzv83Ad9OjtN2AmZIeAf4FmBoRV0m6CLgI+KGkg4ER5Dpb9wIelXRAWsFtPDAKeIbcAkhDaWIFt0ZrfpLapos2Op29mVWf+qUri9kKXidiRf1yGBGxHlgA7E2uZTkxZZsIDE/7w4BJEbEpIpYAi4EhaZ3fzhHxdOSi8q155zSqUM1vOrnAN0vSFOB24J28gt/Z1MXNrDJlqPh1lTQj7/OEiJiwbSZJ+wKHAc8CPdJC5ETECkndU7a9ydXs6i1LaR+k/W3TCyqmt3cPYC25NTvqx/sF4OBnVo2yTVe1JiIGF8ogaVfgDuBbEfF2gcd1DR2IAukFFQp+3VNP74sN3KD1d/WY2XZrrjHOktqRC3y/y2tNrpTUM9X6egKrUvoyoHfe6b2A5Sm9VwPpBRXq7a0Bdk3bbnn79ZuZVaEgN5V9MX8KST2yNwELIuKavENTgLPS/lnA3XnpIyR1kNQX6AdMT03k9ZKOTNccmXdOowrV/FZExBVNXcDMqk8zDfM7GjgTmCtpVkq7BLgKmCzpHGApcFrunjFP0mRgPrme4tGpUxbgfOAWoBO5Xt6CPb1QOPg1OU7GzKpTc7zbGxFP0XicObGRc8YAYxpInwEMyHL/QsGvwZubWXXLDWBu/Y/9Cy1avm5nFsTMWo8KiH1eutLMsquEd3sd/MwsswqIfQ5+ZpZNRFBXDZOZmpltq6I7PMzMGlMBsc/Bz8yyc83PzKpO4OBnZtWoiFmaWwMHPzPLzL29ZlaFMs3n12I5+JlZJrlnfuUuxY5z8DOzbCp9YgMzs8ZUQOxz8DOz7NzhYWZVx+P8zKw6Vcg4v0ILGJmZNSjS8pVNbU2RdLOkVZJezEu7TNJrkmal7ZS8YxdLWixpoaST8tIHSZqbjo1VgfUv6zn4mVlmEcVtRbgFGNpA+rURMTBt9wNIOhgYAfRP54yTVJPyjwdGkVvRrV8j19yKg5+ZZVRcra+Yml9ETAOKXTJjGDApIjZFxBJgMTAkre3bOSKejtxNbwWGN3UxBz8zyyQi19tbzAZ0lTQjbxtV5G0ukDQnNYu7pLS9gVfz8ixLaXun/W3TC3KHh5lllqG3d01EDM54+fHAleQ6lq8ErgbOpuFlLqNAekEOfmaWWSl7eyNiZf2+pBuAe9PHZUDvvKy9gOUpvVcD6QW52WtmmQSZmr2ZpWd49U4F6nuCpwAjJHWQ1Jdcx8b0iFgBrJd0ZOrlHQnc3dR9XPPbQfvvtTs3f39Ljzt9enTmP297lqdeXM415x1Lx/Zt2Vxbx/eu/zPPL1rFacccwH+cetiW/P377Mmx353Mi0vWlKP4VWv2pO+xYeMmamuDzbV1nHDuOK44bygnHfUpPthcy5Ll6xh91R28veE9APp/sgfXfG84u+3SgYjghHPHs+n9zWX+FmXSjOP8JN0GHEfu2eAy4FLgOEkDc3fiFeBcgIiYJ2kyMB/YDIyOiNp0qfPJ9Rx3Ah5IW+F7l2qktqSbgS8CqyJiQDHntNl9n+jw2R+WpDw7Q5s2Yv5N/8I//uCP/HL0cYyfMptHn1/KPw7qw4WnHsY///hPW+U/uM8e/O7iUzjsvN+Wp8DN4Y0mWxct0uxJ3+P4c8ex7q13t6QdP3h/pr3wMrW1dVx2bu4/tMuuf4iamjb8+YbRnDfmdl586XW6dO7EWxvea5WveG2afTN1G1Y0OQaukI6fOCD2GTm2qLyLfn7yzO145rdTlLLZewtFjLWpJMce0otXXn+LV1evJwJ269QegM67tOf1de98JP//+dwB3PHkop1dTGvE4zMWU1tbB8Bz819lr26dAThh8P7Me+l1XnzpdQDeeHtjqwx8zae4Jm9L/zsqWbM3IqZJ2rdU12+JvvzZfluC2SU3PcUdl/4zV37jKCQx9KI7P5L/1M/uzxk/u39nF9OAILjzF98gIrjlnueYeM9zWx3/+imDuOuxOQDs17srQfDHn/8LXXf/GHc+Noextz1ZjmK3CJ7Pr5mkcT+5sT+duhTO3IK1a9uGk4fsyxW/eRqAs4cO4JKbn+Kep19m+NH7M/aC4zn10ilb8g/q14ONmzazYGmx4zutOQ0dPYHX166n6+4f466rv8Giv6/mr3NeAeC7Xz+OzbV1TH5kNgBta9pw5Kf7cMK549n43gf86dqzmbXwNaY9/3IZv0EZVch8fmXv7Y2ICRExOCIGq/2u5S7Odvv84X2Y/fJqVr+1EYDTjz+Qe57O/XL86S+LObxfj63yf/lz+7vJW0avr10PwJo33+HeJ+dz+EG5kRIjTjqMLxx1IKOunLwl7/LVb/GXWa+w7q132bjpAx555n859IC9ylLulqK53vAop7IHv0rxlc/1445pHwazFeve4egBuV+QYw7pxcsr3txyTIJhRzn4lcsuHduxa3oeu0vHdpxwxP4sWLKSE4f045tfO4avXfwbNm76YEv+qdMX0X+/T9CpQztqatpw9KH7svCV1eUqfssQRW4tWNmbvZWgU/u2HHdob749/oktad8a9wT/+a+fpW2bNrz3QS3fGvfhsaP678XytRv4+8q3d35hjW5dduW3Pz0DgJqaNtzx6BymTl/EzN99hw7ta7jr6rMBmDH/Vb5zzd28teE9xk1+iqnXnw8Bjzy7kIefWVjOr1B2dXV15S7CDivlUJct43eAlcClEXFToXNa+1CXqtRKh7pUq+YY6tK++/7xia9eU1TeV68b1mKHupSyt/f0Ul3bzMqrpT/PK4abvWaWXeuPfQ5+Zpada35mVpUc/Mys+gREC391rRgOfmaWSdDyBzAXw8HPzDJz8DOzquTgZ2bVp0ImNnDwM7PsWn/sc/Azs2yCqIh3ez2ri5ll1lxTWqV1eVdJejEvbQ9Jj0halH52yTt2saTFkhZKOikvfZCkuenY2LSQUUEOfmaWTbHTWRXXNL6Fjy53cREwNSL6AVPTZyQdDIwA+qdzxkmqSeeMJzcpcr+0NbmEhoOfmWXWXDW/iJgGbDud+TBgYtqfCAzPS58UEZsiYgmwGBiSlrrsHBFPR+6mt+ad0yg/8zOzzErc29sjrcVLRKyQ1D2l7w08k5dvWUr7IO1vm16Qg5+ZZZKxw6OrpBl5nydExITtvHVDz/GiQHpBDn5mlk22KerXbMdkpisl9Uy1vp7AqpS+DOidl68XsDyl92ogvSA/8zOzzEq8gNEU4Ky0fxZwd176CEkdJPUl17ExPTWR10s6MvXyjsw7p1Gu+ZlZZs31zC9/uQtJy4BLgauAyZLOAZYCp6V7zpM0GZgPbAZGR0RtutT55HqOOwEPpK0gBz8zy6y5gl+B5S5ObCT/GGBMA+kzgAFZ7u3gZ2YZeUorM6tC4clMzaxaueZnZtXJwc/Mqk6u3VvuUuwwBz8zy841PzOrSq75mVn1CairbTpbC+fgZ2bZBK75mVmV8jM/M6s+7u01s2rlmp+ZVZ1wh4eZVSs3e82sKrnZa2bVxx0eZlatXPMzs6rjQc5mVp0Calt/b69XbzOz7KKuuK0Jkl6RNFfSrPr1fSXtIekRSYvSzy55+S+WtFjSQkkn7chXcPAzs2yCNKdfEVtxjo+IgXnr+14ETI2IfsDU9BlJBwMjgP7AUGCcpJrt/RoOfmaWUTRbza8Rw4CJaX8iMDwvfVJEbIqIJcBiYMj23sTBz8yyK77m11XSjLxt1LZXAh6WNDPvWI+0EDnpZ/eUvjfwat65y1LadnGHh5llV3ytbk1ec7YhR0fEckndgUck/a1AXjVUkmILsi0HPzPLphnf7Y2I5ennKkl3kWvGrpTUMyJWSOoJrErZlwG9807vBSzf3nu72Wtm2TVDh4ekj0narX4f+ALwIjAFOCtlOwu4O+1PAUZI6iCpL9APmL69X8E1PzPLqNleb+sB3CUJcrHo9xHxoKTngMmSzgGWAqcBRMQ8SZOB+cBmYHREbHcV1MHPzLJrhtfbIuJl4NAG0tcCJzZyzhhgzA7fHAc/M9sefr3NzKpOhUxmqmhBszNIWg38vdzlKIGuwJpyF8IyqdR/sz4R0W1HLiDpQXJ/P8VYExFDd+R+pdKigl+lkjSjibFO1sL436zyeaiLmVUlBz8zq0oOfjvHhHIXwDLzv1mF8zM/M6tKrvmZWVVy8DOzquTgV0KShqbpthdLuqjc5bGmSbpZ0ipJL5a7LFZaDn4lkqbX/hVwMnAwcHqahttatlvITZFuFc7Br3SGAIsj4uWIeB+YRG4abmvBImIasK7c5bDSc/ArnWadctvMmpeDX+k065TbZta8HPxKp1mn3Daz5uXgVzrPAf0k9ZXUntx6o1PKXCYzSxz8SiQiNgMXAA8BC4DJETGvvKWypki6DXgaOFDSsjSVulUgv95mZlXJNT8zq0oOfmZWlRz8zKwqOfiZWVVy8DOzquTg14pIqpU0S9KLkm6XtMsOXOsWSV9J+zcWmnRB0nGSjtqOe7wi6SOrfDWWvk2eDRnvdZmk72Uto1UvB7/WZWNEDIyIAcD7wHn5B9NMMplFxL9GxPwCWY4DMgc/s5bMwa/1ehLYP9XKHpf0e2CupBpJP5f0nKQ5ks4FUM51kuZLug/oXn8hSU9IGpz2h0p6XtJsSVMl7UsuyH471To/J6mbpDvSPZ6TdHQ6d09JD0t6QdL1NPx+81Yk/UnSTEnzJI3a5tjVqSxTJXVLaftJejCd86SkTzXL36ZVnbblLoBlJ6ktuXkCH0xJQ4ABEbEkBZC3IuIISR2Av0h6GDgMOBD4NNADmA/cvM11uwE3AMeka+0REesk/RrYEBG/SPl+D1wbEU9J2ofcWywHAZcCT0XEFZL+CdgqmDXi7HSPTsBzku6IiLXAx4DnI+K7kn6Srn0BuYWFzouIRZL+ARgHnLAdf41W5Rz8WpdOkmal/SeBm8g1R6dHxJKU/gXgkPrnecDHgX7AMcBtEVELLJf0WAPXPxKYVn+tiGhsXrvPAwdLWyp2nSXtlu7x5XTufZLeKOI7XSjp1LTfO5V1LVAH/CGl/xa4U9Ku6fvennfvDkXcw+wjHPxal40RMTA/IQWBd/KTgP+IiIe2yXcKTU+ppSLyQO5xyWciYmMDZSn6fUlJx5ELpJ+JiHclPQF0bCR7pPu+ue3fgdn28DO/yvMQcL6kdgCSDpD0MWAaMCI9E+wJHN/AuU8Dx0rqm87dI6WvB3bLy/cwuSYoKd/AtDsNOCOlnQx0aaKsHwfeSIHvU+RqnvXaAPW116+Ra06/DSyRdFq6hyQd2sQ9zBrk4Fd5biT3PO/5tAjP9eRq+HcBi4C5wHjgz9ueGBGryT2nu1PSbD5sdt4DnFrf4QFcCAxOHSrz+bDX+XLgGEnPk2t+L22irA8CbSXNAa4Ensk79g7QX9JMcs/0rkjpZwDnpPLNw0sD2HbyrC5mVpVc8zOzquTgZ2ZVycHPzKqSg5+ZVSUHPzOrSg5+ZlaVHPzMrCr9f77Oh2mWXF9/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion metrics\n",
    "plot_confusion_matrix(xgb_clf, X_test, y_test, cmap=\"Blues_r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e7815e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x15f09b4b790>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot pprcision_recall curve\n",
    "plot_precision_recall_curve(xgb_clf,X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04b1c707",
   "metadata": {},
   "source": [
    "## Hyperparameter tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "afec5e87",
   "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": 54,
   "id": "b7218fd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hyperparameter optimization using RandomizedSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e35f1fec",
   "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": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: 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": [
      "[18:40:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
       "                                           colsample_bylevel=1,\n",
       "                                           colsample_bynode=1,\n",
       "                                           colsample_bytree=1,\n",
       "                                           enable_categorical=False, gamma=0,\n",
       "                                           gpu_id=-1, importance_type=None,\n",
       "                                           interaction_constraints='',\n",
       "                                           learning_rate=0.300000012,\n",
       "                                           max_delta_step=0, max_depth=6,\n",
       "                                           min_child_weight=1, missing=nan,\n",
       "                                           monotone_constraints='()',\n",
       "                                           n_estimat...\n",
       "                                           reg_alpha=0, reg_lambda=1,\n",
       "                                           scale_pos_weight=1, subsample=1,\n",
       "                                           tree_method='exact',\n",
       "                                           validate_parameters=1,\n",
       "                                           verbosity=None),\n",
       "                   n_iter=5, n_jobs=-1,\n",
       "                   param_distributions={'colsample_bytree': [0.3, 0.4, 0.5,\n",
       "                                                             0.7],\n",
       "                                        'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],\n",
       "                                        'learning_rate': [0.05, 0.1, 0.15, 0.2,\n",
       "                                                          0.25, 0.3],\n",
       "                                        'max_depth': [3, 4, 5, 6, 8, 10, 12,\n",
       "                                                      15],\n",
       "                                        'min_child_weight': [1, 3, 5, 7]},\n",
       "                   scoring='roc_auc', verbose=3)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search=RandomizedSearchCV(xgb_clf,param_distributions=params,n_iter=5,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)\n",
    "\n",
    "# fitting the RandomizedSearchCV\n",
    "random_search.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "53e07fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.5,\n",
       "              enable_categorical=False, gamma=0.2, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.15, max_delta_step=0, max_depth=15,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=8, num_parallel_tree=1, predictor='auto',\n",
       "              random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "              subsample=1, tree_method='exact', validate_parameters=1,\n",
       "              verbosity=None)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the best estimators\n",
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a7c0ab50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_child_weight': 1,\n",
       " 'max_depth': 15,\n",
       " 'learning_rate': 0.15,\n",
       " 'gamma': 0.2,\n",
       " 'colsample_bytree': 0.5}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the best param\n",
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e8e3c93c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:50:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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=0.5,\n",
       "              enable_categorical=False, gamma=0.2, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.15, max_delta_step=0, max_depth=15,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=8, num_parallel_tree=1, predictor='auto',\n",
       "              random_state=0, reg_alpha=0, reg_lambda=10, scale_pos_weight=1,\n",
       "              subsample=0.9, tree_method='exact', use_label_encoder=False,\n",
       "              validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier=xgboost.XGBClassifier(objective='binary:logistic',\n",
    "                                  gamma=0.2,\n",
    "                                  learning_rate=0.15,\n",
    "                                  max_depth=15,\n",
    "                                  reg_lambda=10,\n",
    "                                  min_child_weight=1,\n",
    "                                  subsample=0.9, \n",
    "                                  colsample_bytree=0.50,\n",
    "                                  use_label_encoder=False)\n",
    "\n",
    "# Fitting the model\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "af56cc71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting model\n",
    "y_pred= classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43ddecb4",
   "metadata": {},
   "source": [
    "#### Evaluating the model after hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6a118fb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model is: 0.8083333333333333\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.92      0.88      4687\n",
      "           1       0.59      0.40      0.48      1313\n",
      "\n",
      "    accuracy                           0.81      6000\n",
      "   macro avg       0.72      0.66      0.68      6000\n",
      "weighted avg       0.79      0.81      0.79      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"The accuracy of the model is:\", accuracy_score(y_test, y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a81158",
   "metadata": {},
   "source": [
    "### Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "25b2973f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:52:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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",
      "[18:52:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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",
      "[18:52:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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",
      "[18:53:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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",
      "[18:53:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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",
      "[18:53:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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",
      "[18:53:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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",
      "[18:53:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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",
      "[18:53:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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",
      "[18:53:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: 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"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(classifier,X,y,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b6f058ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.802     , 0.80666667, 0.81066667, 0.808     , 0.81433333,\n",
       "       0.82433333, 0.83933333, 0.824     , 0.82333333, 0.82133333])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4a382acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8173999999999999"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12464f81",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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