Final_solution_fr.ipynb

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    "id": "31PihYgNI4b5",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "# Load the Drive helper and mount\n",
    "from google.colab import drive\n",
    "\n",
    "# This will prompt for authorization.\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "R_37jUVvUQ-o"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "WORK_DIR=\"drive/My Drive\"\n",
    "os.chdir(WORK_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b4sfnPE6I3kA"
   },
   "source": [
    "I will take the data from Machine Learning Summer Training Hackathon.\n",
    "Steps:\n",
    "\n",
    "\n",
    "1.   Univariate analisys of features. Check for normal distribution and outliers\n",
    "2.   Bivariate analisys of features. Check for correlation between features and target variables\n",
    "3.   Check for Correlation matrix\n",
    "4.   Feature Imputing for 'education'\n",
    "5.   Feature Engineering\n",
    "     High correlation between:\n",
    "     * asset_cost and loan_amount -> 74%\n",
    "     * no_of_curr_loans and no_of_loans -> 77%\n",
    "      I will create two new features to avoid high  \n",
    "     correlation.\n",
    "\n",
    "     * create a column, which will store an information     \n",
    "       about having loans before and drop no_of_loans\n",
    "     * create column loan_amount-asset_cost, which tells  \n",
    "      about asset value without loan amount disbursed\n",
    "6.   Feature Scaling for numerical features\n",
    "7.   One hot encoding for 'proof_submitted'\n",
    "8.   Label Encoding for 'education'\n",
    "9.   Modelling.\n",
    "     * Logistic regression\n",
    "     * Decision Tree\n",
    "     * SVC\n",
    "     * KNN\n",
    "     * XGBOOST\n",
    "     * Random forest\n",
    "     I choose Random Forest Classifier\n",
    "     Final model on test set has macro f1_score=0.524564\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "mODfeGUUpyCW"
   },
   "outputs": [],
   "source": [
    "#Libraries i will use\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold, StratifiedKFold, train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.svm import LinearSVR\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "warnings.simplefilter(action='ignore', category=UserWarning)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "id": "1768WtJ9o79m"
   },
   "outputs": [],
   "source": [
    "#my train data\n",
    "train_df=pd.read_csv('train_LZV4RXX.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "id": "RKALTe6-o77b"
   },
   "outputs": [],
   "source": [
    "#my test data\n",
    "test_df=pd.read_csv('test_4zJg83n.csv')"
   ]
  },
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   "execution_count": 7,
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       "      <th></th>\n",
       "      <th>loan_id</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>proof_submitted</th>\n",
       "      <th>loan_amount</th>\n",
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       "      <th>no_of_curr_loans</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Aadhar</td>\n",
       "      <td>504264</td>\n",
       "      <td>820920</td>\n",
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       "      <td>831444</td>\n",
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       "      <td>VoterID</td>\n",
       "      <td>642936</td>\n",
       "      <td>826092</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Aadhar</td>\n",
       "      <td>746556</td>\n",
       "      <td>930924</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Aadhar</td>\n",
       "      <td>1139880</td>\n",
       "      <td>1902000</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   loan_id  age  education proof_submitted  loan_amount  asset_cost  \\\n",
       "0        1   27        1.0          Aadhar       504264      820920   \n",
       "1        2   48        1.0          Aadhar       728556      831444   \n",
       "2        3   30        2.0         VoterID       642936      826092   \n",
       "3        4   28        1.0          Aadhar       746556      930924   \n",
       "4        5   29        1.0          Aadhar      1139880     1902000   \n",
       "\n",
       "   no_of_loans  no_of_curr_loans  last_delinq_none  loan_default  \n",
       "0            2                 2                 0             0  \n",
       "1            6                 2                 0             0  \n",
       "2            0                 0                 0             1  \n",
       "3            0                 0                 0             0  \n",
       "4            0                 0                 0             0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#quick view \n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IeRx9ax5VA63"
   },
   "source": [
    " Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CAgdofeEVKNg",
    "outputId": "efc49404-3292-41aa-e43b-c19477b96340"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_id', 'age', 'education', 'proof_submitted', 'loan_amount',\n",
       "       'asset_cost', 'no_of_loans', 'no_of_curr_loans', 'last_delinq_none',\n",
       "       'loan_default'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check columns\n",
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "egduhKaGVQci",
    "outputId": "836aee99-0ecd-44bc-f92c-57dc090d5560"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_id', 'age', 'education', 'proof_submitted', 'loan_amount',\n",
       "       'asset_cost', 'no_of_loans', 'no_of_curr_loans', 'last_delinq_none'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check columns\n",
    "test_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "egN3iv3BVfrE",
    "outputId": "539d2a85-1de8-4264-8a94-72edbe929f4a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7000, 10), (3000, 9))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shapes\n",
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
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     "height": 300
    },
    "id": "17DcPlwxMqOA",
    "outputId": "6bb4537e-3858-4e86-ba2b-1842c7683141"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_id</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>asset_cost</th>\n",
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       "      <th>no_of_curr_loans</th>\n",
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       "      <th>loan_default</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>6755.000000</td>\n",
       "      <td>7.000000e+03</td>\n",
       "      <td>7.000000e+03</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3500.500000</td>\n",
       "      <td>36.096571</td>\n",
       "      <td>1.561954</td>\n",
       "      <td>6.633552e+05</td>\n",
       "      <td>9.162998e+05</td>\n",
       "      <td>2.853286</td>\n",
       "      <td>1.371143</td>\n",
       "      <td>0.013286</td>\n",
       "      <td>0.400000</td>\n",
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       "      <th>std</th>\n",
       "      <td>2020.870275</td>\n",
       "      <td>7.587700</td>\n",
       "      <td>0.496184</td>\n",
       "      <td>1.498128e+05</td>\n",
       "      <td>2.144922e+05</td>\n",
       "      <td>5.471932</td>\n",
       "      <td>2.189278</td>\n",
       "      <td>0.114504</td>\n",
       "      <td>0.489933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.678800e+05</td>\n",
       "      <td>4.733520e+05</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>1750.750000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.777880e+05</td>\n",
       "      <td>7.979010e+05</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>50%</th>\n",
       "      <td>3500.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.571080e+05</td>\n",
       "      <td>8.584260e+05</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>75%</th>\n",
       "      <td>5250.250000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.373640e+05</td>\n",
       "      <td>9.576750e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7000.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.781376e+06</td>\n",
       "      <td>2.419200e+06</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de7bee61-9704-4207-877e-4d8222cb631b')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-de7bee61-9704-4207-877e-4d8222cb631b button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-de7bee61-9704-4207-877e-4d8222cb631b');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           loan_id          age    education   loan_amount    asset_cost  \\\n",
       "count  7000.000000  7000.000000  6755.000000  7.000000e+03  7.000000e+03   \n",
       "mean   3500.500000    36.096571     1.561954  6.633552e+05  9.162998e+05   \n",
       "std    2020.870275     7.587700     0.496184  1.498128e+05  2.144922e+05   \n",
       "min       1.000000    21.000000     1.000000  1.678800e+05  4.733520e+05   \n",
       "25%    1750.750000    29.000000     1.000000  5.777880e+05  7.979010e+05   \n",
       "50%    3500.500000    36.000000     2.000000  6.571080e+05  8.584260e+05   \n",
       "75%    5250.250000    43.000000     2.000000  7.373640e+05  9.576750e+05   \n",
       "max    7000.000000    50.000000     2.000000  1.781376e+06  2.419200e+06   \n",
       "\n",
       "       no_of_loans  no_of_curr_loans  last_delinq_none  loan_default  \n",
       "count  7000.000000       7000.000000       7000.000000   7000.000000  \n",
       "mean      2.853286          1.371143          0.013286      0.400000  \n",
       "std       5.471932          2.189278          0.114504      0.489933  \n",
       "min       0.000000          0.000000          0.000000      0.000000  \n",
       "25%       0.000000          0.000000          0.000000      0.000000  \n",
       "50%       0.000000          0.000000          0.000000      0.000000  \n",
       "75%       3.000000          2.000000          0.000000      1.000000  \n",
       "max     109.000000         33.000000          1.000000      1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#statistics\n",
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jMMF3ynUWYHT",
    "outputId": "2904c49c-cf8a-45a9-c829-f3388326773a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.6\n",
       "1    0.4\n",
       "Name: loan_default, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#quick look at target value\n",
    "train_df['loan_default'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "aOB7PLIkWYDH",
    "outputId": "ab3587a0-f500-495c-d411-120677a7ff9e"
   },
   "outputs": [
    {
     "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": [
    "ax = sns.countplot(x=\"loan_default\", data=train_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pSh-kPAJcrBw",
    "outputId": "8dc1b83e-4f14-46c5-a9b3-86aba6fc52a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Aadhar      0.847286\n",
       "VoterID     0.101000\n",
       "PAN         0.033571\n",
       "Driving     0.016286\n",
       "Passport    0.001857\n",
       "Name: proof_submitted, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check percentage of proof_submitted types\n",
    "train_df['proof_submitted'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "q7HsGR3TWX9Z",
    "outputId": "e3ee6a95-9813-4279-ab20-10093ed29e7c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEHCAYAAABfkmooAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAaTUlEQVR4nO3de7RdZX3u8e8j4K3cJVIkYFAzalErpTmo1XpjFFCr0BYpaDVSetKeg7Z2WC3WDlHUHjgcRdDCKZXUYCmIKBItStMAXqgI4SJXKTkohRQkNRHFCxX4nT/mu81iZ+/MlbjX3jvJ9zPGGmvOd75zrnfNvdd61jvnXO9KVSFJ0oY8ZqYbIEma/QwLSVIvw0KS1MuwkCT1MiwkSb22nekGjMJuu+1W8+bNm+lmSNJm5ZprrvnPqpoz0bItMizmzZvHihUrZroZkrRZSXLnZMs8DCVJ6mVYSJJ6GRaSpF4jDYskOye5IMk3k9ya5AVJdk2yLMnt7X6XVjdJTkuyMskNSfYf2M7CVv/2JAtH2WZJ0vpG3bM4FfhiVT0TeC5wK3AcsLyq5gPL2zzAK4D57bYIOAMgya7A8cDzgAOA48cCRpI0PUYWFkl2Al4MnAVQVf9VVd8DDgWWtGpLgMPa9KHA2dW5Etg5yR7AwcCyqlpTVWuBZcAho2q3JGl9o+xZ7AOsBv4+yXVJPpbkF4Ddq+qeVudeYPc2vSdw18D6d7eyycofJcmiJCuSrFi9evUUPxVJ2rqNMiy2BfYHzqiqXwV+yLpDTgBUNz76lIyRXlVnVtWCqlowZ86E3ymRJG2iUYbF3cDdVfX1Nn8BXXh8px1eot3f15avAvYaWH9uK5usXJI0TUb2De6qujfJXUl+qapuAw4Ebmm3hcCJ7f6itspS4M1JzqM7mX1/Vd2T5BLgrwdOah8EvHNT2/Vrbz97U1ed1a45+Y0z3QRJW7BRD/fxFuCcJI8F7gCOpuvNnJ/kGOBO4IhW92LglcBK4EetLlW1Jsn7gKtbvROqas2I2y1JGjDSsKiq64EFEyw6cIK6BRw7yXYWA4untnWSpGH5DW5JUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUa6RhkeTbSW5Mcn2SFa1s1yTLktze7ndp5UlyWpKVSW5Isv/Adha2+rcnWTjKNkuS1jcdPYuXVdV+VbWgzR8HLK+q+cDyNg/wCmB+uy0CzoAuXIDjgecBBwDHjwWMJGl6zMRhqEOBJW16CXDYQPnZ1bkS2DnJHsDBwLKqWlNVa4FlwCHT3WhJ2pqNOiwK+Ock1yRZ1Mp2r6p72vS9wO5tek/groF1725lk5U/SpJFSVYkWbF69eqpfA6StNXbdsTbf1FVrUryZGBZkm8OLqyqSlJT8UBVdSZwJsCCBQumZJuSpM5IexZVtard3wdcSHfO4Tvt8BLt/r5WfRWw18Dqc1vZZOWSpGkysrBI8gtJdhibBg4CbgKWAmNXNC0ELmrTS4E3tquing/c3w5XXQIclGSXdmL7oFYmSZomozwMtTtwYZKxx/nHqvpikquB85McA9wJHNHqXwy8ElgJ/Ag4GqCq1iR5H3B1q3dCVa0ZYbslSeOMLCyq6g7guROUfxc4cILyAo6dZFuLgcVT3UZJ0nD8BrckqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqNfKwSLJNkuuSfL7N75Pk60lWJvlkkse28se1+ZVt+byBbbyzld+W5OBRt1mS9GjT0bP4U+DWgfmTgFOq6hnAWuCYVn4MsLaVn9LqkWRf4EjgWcAhwOlJtpmGdkuSmpGGRZK5wKuAj7X5AC8HLmhVlgCHtelD2zxt+YGt/qHAeVX1YFV9C1gJHDDKdkuSHm3UPYsPA+8AHmnzTwK+V1UPtfm7gT3b9J7AXQBt+f2t/s/KJ1jnZ5IsSrIiyYrVq1dP9fOQpK3ayMIiyW8B91XVNaN6jEFVdWZVLaiqBXPmzJmOh5Skrca2I9z2C4HXJHkl8HhgR+BUYOck27bew1xgVau/CtgLuDvJtsBOwHcHyscMriNJmgYj61lU1Turam5VzaM7QX1pVb0euAw4vFVbCFzUppe2edryS6uqWvmR7WqpfYD5wFWjarckaX2j7FlM5i+A85K8H7gOOKuVnwV8IslKYA1dwFBVNyc5H7gFeAg4tqoenv5mS9LWa1rCoqouBy5v03cwwdVMVfUT4LWTrP8B4AOja6EkaUP8BrckqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqNVRYJFk+TJkkacu0wd+zSPJ44InAbkl2AdIW7QjsOeK2SZJmib4fP/oj4K3AU4BrWBcW3wc+OsJ2SZJmkQ2GRVWdCpya5C1V9ZFpapMkaZYZ6mdVq+ojSX4dmDe4TlWdPaJ2SZJmkaHCIskngKcD1wMPt+ICDAtJ2goMFRbAAmDfqqpRNkaSNDsN+z2Lm4BfHGVDJEmz17A9i92AW5JcBTw4VlhVrxlJqyRJs8qwYfGeUTZCkjS7DXs11JdG3RBJ0uw17NVQP6C7+gngscB2wA+rasdRNUySNHsMdYK7qnaoqh1bODwB+F3g9A2tk+TxSa5K8o0kNyd5byvfJ8nXk6xM8skkj23lj2vzK9vyeQPbemcrvy3JwZv4XCVJm2ijR52tzmeBvjftB4GXV9Vzgf2AQ5I8HzgJOKWqngGsBY5p9Y8B1rbyU1o9kuwLHAk8CzgEOD3JNhvbbknSphv2MNTvDMw+hu57Fz/Z0DrtOxkPtNnt2q2AlwOva+VL6E6enwEcyroT6RcAH02SVn5eVT0IfCvJSuAA4GvDtF2S9PMb9mqoVw9MPwR8m+5NfINaD+Aa4BnA3wD/D/heVT3UqtzNutFr9wTuAqiqh5LcDzyplV85sNnBdQYfaxGwCGDvvfce8mlJkoYx7NVQR2/KxqvqYWC/JDsDFwLP3JTtDPlYZwJnAixYsMBvmkvSFBr2x4/mJrkwyX3t9ukkc4d9kKr6HnAZ8AJg5yRjITUXWNWmVwF7tcfbFtgJ+O5g+QTrSJKmwbAnuP8eWEr3uxZPAT7XyiaVZE7rUZDkCcBvArfShcbhrdpC4KI2vbTN05Zf2s57LAWObFdL7QPMB64ast2SpCkw7DmLOVU1GA4fT/LWnnX2AJa08xaPAc6vqs8nuQU4L8n7geuAs1r9s4BPtBPYa+iugKKqbk5yPnAL3fmSY9vhLUnSNBk2LL6b5PeBc9v8UXSHiCZVVTcAvzpB+R10VzONL/8J8NpJtvUB4ANDtlWSNMWGPQz1B8ARwL3APXSHid40ojZJkmaZYXsWJwALq2otQJJdgf9DFyKSpC3csD2LXxkLCoCqWsMEh5gkSVumYcPiMUl2GZtpPYtheyWSpM3csG/4HwS+luRTbf61eMJZkrYaw36D++wkK+jGdQL4naq6ZXTNkiTNJkMfSmrhYEBI0lZoo4colyRtfQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktRrZGGRZK8klyW5JcnNSf60le+aZFmS29v9Lq08SU5LsjLJDUn2H9jWwlb/9iQLR9VmSdLERtmzeAh4W1XtCzwfODbJvsBxwPKqmg8sb/MArwDmt9si4AzowgU4HngecABw/FjASJKmx8jCoqruqapr2/QPgFuBPYFDgSWt2hLgsDZ9KHB2da4Edk6yB3AwsKyq1lTVWmAZcMio2i1JWt+0nLNIMg/4VeDrwO5VdU9bdC+we5veE7hrYLW7W9lk5eMfY1GSFUlWrF69ekrbL0lbu5GHRZLtgU8Db62q7w8uq6oCaioep6rOrKoFVbVgzpw5U7FJSVIz0rBIsh1dUJxTVZ9pxd9ph5do9/e18lXAXgOrz21lk5VLkqbJKK+GCnAWcGtVfWhg0VJg7IqmhcBFA+VvbFdFPR+4vx2uugQ4KMku7cT2Qa1MkjRNth3htl8IvAG4Mcn1rewvgROB85McA9wJHNGWXQy8ElgJ/Ag4GqCq1iR5H3B1q3dCVa0ZYbslSeOMLCyq6qtAJll84AT1Czh2km0tBhZPXeskSRvDb3BLknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXiMLiySLk9yX5KaBsl2TLEtye7vfpZUnyWlJVia5Icn+A+ssbPVvT7JwVO2VJE1ulD2LjwOHjCs7DlheVfOB5W0e4BXA/HZbBJwBXbgAxwPPAw4Ajh8LGEnS9BlZWFTVl4E144oPBZa06SXAYQPlZ1fnSmDnJHsABwPLqmpNVa0FlrF+AEmSRmy6z1nsXlX3tOl7gd3b9J7AXQP17m5lk5WvJ8miJCuSrFi9evXUtlqStnIzdoK7qgqoKdzemVW1oKoWzJkzZ6o2K0li+sPiO+3wEu3+vla+CthroN7cVjZZuSRpGk13WCwFxq5oWghcNFD+xnZV1POB+9vhqkuAg5Ls0k5sH9TKJEnTaNtRbTjJucBLgd2S3E13VdOJwPlJjgHuBI5o1S8GXgmsBH4EHA1QVWuSvA+4utU7oarGnzSXJI3YyMKiqo6aZNGBE9Qt4NhJtrMYWDyFTZMkbSS/wS1J6mVYSJJ6GRaSpF6GhSSp18hOcGv2+/cTnjPTTRiJvd9940w3Qdri2LOQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MsfP5L0KF968UtmuglT7iVf/tJMN2GzZ89CktTLsJAk9TIsJEm9PGchAS/8yAtnugkjccVbrpjpJmgLsdn0LJIckuS2JCuTHDfT7ZGkrclmERZJtgH+BngFsC9wVJJ9Z7ZVkrT12FwOQx0ArKyqOwCSnAccCtwyo62StEX76Ns+N9NNmHJv/uCrN2m9VNUUN2XqJTkcOKSq/rDNvwF4XlW9eaDOImBRm/0l4LZpb+j6dgP+c6YbMUu4L9ZxX6zjvlhnNuyLp1bVnIkWbC49i15VdSZw5ky3Y1CSFVW1YKbbMRu4L9ZxX6zjvlhntu+LzeKcBbAK2Gtgfm4rkyRNg80lLK4G5ifZJ8ljgSOBpTPcJknaamwWh6Gq6qEkbwYuAbYBFlfVzTPcrGHMqsNiM8x9sY77Yh33xTqzel9sFie4JUkza3M5DCVJmkGGhSSpl2GxAUkOS1JJnrmR670nyZ+36cuTzNrL4TYkyWVJDh5X9tYkZ0xS/y838XF+to+SfDvJje12S5L3J3n8pmx3FJI8nOT6JDcn+UaStyWZ8HWU5ClJLujZ3mu21OFrBvbVTUk+leSJrXzbJKuTnDiu/uVJVgzML0hy+TQ3eyiTPbeZ1t6zRjK6hWGxYUcBX2330y7JTF+AcC7dlWeDjmzlE9nosGhDuYz3sqp6Dt03958G/O3GbneEflxV+1XVs4DfpBuC5vjxlZJsW1X/UVWHb2hjVbW0qk7cUJ3N2Ni+ejbwX8Aft/LfBP4NeG2SjFvnyUleMZ2N3ESTPbcZ094vDqMbEmnKGRaTSLI98CLgGNobZpLtkyxPcm375HvoQP13Jfm3JF+l+wb5oNcmuaot/41Wf16Sr7RtXZvk11v5S1v5UmZ+OJMLgFe1y5VJMg94CrBne/43JTmpLTsReEL7tHVOK/v99ryvT/K3Y8GQ5IEkH0zyDeAFkz14VT1A9yI8LMmuI3yem6Sq7qMbNeDN6bwpydIklwLL29/4JoAkVyZ51ti6Y72pts5HW9nHk5yW5F+T3NFGLiDJY5KcnuSbSZYluXhs2WbkK8Az2vRRwKnAv7P+3/9k4F3T2K6p8BXgGUleneTrSa5L8i9JdgdI8pL2Gri+LdshyR5JvjzQOxl7X3ggySmt57o8yZxWvl/7H7ohyYVJdmnllyf5cOuR/QXwGuDktt2nT+mzrCpvE9yA1wNntel/BX6N7lLjHVvZbsBKIG3ZjcATgR1b+Z+3epcDH2zTrwT+pU0/EXh8m54PrGjTLwV+COwz0/ugtefzwKFt+jhgMd2LfE7bH5cCh7XlDwys98vA54Dt2vzpwBvbdAFHDNS9HFjQpr8N7DauDdfTDe8yG/bHAxOUfQ/YHXgTcDewayufB9zUpv8MeG+b3gO4rU2/Cfhom/448Cm6D3H70o2HBnA4cHEr/0VgLXD4TO+LYfdV+z+5CPgfwOOB/wCeQBe0Hxn/f9D+p17Wpi+f6eexEc9tF9ZdYfqHA6/7zwEvbNPbt3XeBryrlW0D7NCmC3h9m373wP/GDcBL2vQJwIcH9tnpA+36+Kj+N+xZTO4o4Lw2fV6bD/DXSW4A/gXYk+5N4jeAC6vqR1X1fdb/wuBn2v01dG8gANsBf5fkRro3iMGu41VV9a2pfTqbbPBQ1JHAnXQv4NVV9RBwDvDiCdY7kC5Er05yfZt/Wlv2MPDpjWjD+EMVs9myqlozQfn5dG/6AEfQ9dom8tmqeqSqbqH734Kuh/upVn4vcNmUtnh0ntD+9ivoPmCcBfwWcFlV/Zjuf+CwCQ5Fvh/4q2lt6cab6LnNBS5pr+m3A2M9ySuADyX5E2Dn9rq5Gjg6yXuA51TVD1rdR4BPtul/AF6UZKe23tgPiS/h0a+5TzINZvqY+KzUDnm8HHhOkqJL/gJupvtE/WtV9dMk36b7pNTnwXb/MOv2+Z8B3wGeS/eJ8ScD9X/48z6HKXQRcEqS/el6Q9cDw3RvAyypqndOsOwnVfXwMA+eZAe6gP234Zo7vZI8je7vel8rmvBvV1Wrknw3ya8Av8fkx7gfHJjenEJyIj+uqv0GC5IcRfcG+O1W9CS619qysTpVdWmS9wPPn66GboKJnttHgA9V1dIkLwXeA1BVJyb5J7ojC1ckObiqvpzkxcCrgI8n+VBVnT3B4wzzRbhpeb+wZzGxw4FPVNVTq2peVe0FfAvYG7ivBcXLgKe2+l+m+4T0hPbmNswYwDsB91TVI8Ab6AJp1qnuvMFldIefzgWuAl6SZLf2ifAoYOwTz0+TbNemlwOHJ3kydAGc5KlshHTnjU6n+7S99ud/NlOrHU/+v3SHCoZ5UX8SeAewU1XdsBEPdQXwu+3cxe50hyo3O0l2pOuF791eV/OAY5n4ApL30+2rzclOrBuzbuFYYZKnV9WNVXUSXY/ime218J2q+jvgY8D+rfpjWNcDfR3w1aq6H1g7dl6D7v1i7DU33g+AHabqCQ0yLCZ2FHDhuLJP0x0vXtC6mW8EvglQVdfSvRF8A/gC3T9En9OBhe0k7zOZXb2J8c6l6wGdW1X30J27uIzu+V5TVRe1emcCNyQ5px1G+Svgn9thu2V0x+qHcVk7MXwVXRf/j6buqfzcxk7i30x3KPKfgfcOue4FdIfyzt/Ix/w03bmQW+gOTVwL3L+R25gNfhu4tKoGe08XAa9O8rjBilV1MbB6Ohs3Bd4DfCrJNTx6qPG3tpPYNwA/pXuPeCnwjSTX0fU0T211fwgc0P7/X053fgK68Dm5bWO/gfLxzgPe3k6kT+kJbof7kDYDSbavqgeSPIkuRF/Yzl9oC5LkgarafqbbMRHPWUibh88n2Rl4LPA+g0LTzZ6FJKmX5ywkSb0MC0lSL8NCktTLsJAk9TIspCEleW2SW5NMyXAb+TmHr0/ysbThqDMwPHySnZP8z03Y3s+G1pfGMyy0VZpgPKJhHAP896p62VS3Z1NU1R+2Lz/Co4eH3xnY6LCQNsSw0BanDQ3+zSTntJ7ABUmemO6HlU5Kci3dsPFHZdxQ62399cqTvJtuQL+zkpw8yeM+K+uGZL8hyfwMDFPe6vx5GzxuzBsGhqk+oNV5T5Il6YaqvzPJ7yT5361NXxwbUiXrhjkfPzz8icDT2/zJre7bk1zd2vXegfZsaGh96Wf8Up62VL8EHFNVVyRZzLpP2t+tqv2TPAW4km5k3LV0w5IcRvft6JPGl1fVCUleTjf0/Ir1Hq3zx8CpVXVOut8A2YZ1I8dO5olVtV8bVG4x8OxW/nS6Ybr3Bb4G/G5VvSPJhXSDz312bANVdVySN48NbJfud0eePTB/EN0w+AfQDU64tD3eD+mGH9mP7r3gWrqRkaX1GBbaUt1VVVe06X8A/qRNjw3n/N9oQ60DtE/kL6Yb5XOi8p+9OW/A14B3JZkLfKaqbs96PwS3nnMB2iikO7ZvaQN8oQ1YeSNd6Hyxld/IumHuh3VQu13X5renC48daEPrA6T7wS1pQh6G0pZq/NAEY/MjG7Cxqv6R7pfKfgxc3HoiD/Ho19n4Ie0na+eDbZuPAD8dGNX2ETb+Q16A/1Xdz4DuV1XPqKqzNnIb2soZFtpS7Z1k7Cc7X0f3W+qDJhtqfUNDsG9Qut+2uKOqTqMbTfVX6H6z5MlJntRGVv2tcav9Xlv3RcD9bTjqTTE4PPz4YaovAf6gDflOkj3TDR2/KUPrayvlYShtqW4Djm3nK24BzgDeMrawqu5JMjbUeoB/GhtqfbLyIRxBd8L6p8C9wF+3Q0kn0IXQKtqw9gN+0oap3g74g017qsC64eGvrarXJ7minVj/QlW9PckvA19rh8UeAH6/qq5NMja0/n0MN7S+tlIOJKgtTjvB+/mqenZPVUlD8jCUJKmXPQtpIyU5mO7y2kHfqqrfnon2SNPBsJAk9fIwlCSpl2EhSeplWEiSehkWkqRe/x82kLrMZOf9BQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#countplot for check types\n",
    "ax=sns.countplot(x='proof_submitted', data=train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3Vlrc-NWahzq",
    "outputId": "0e046c15-9b2b-40c1-ebb6-b1b1e9eb4e94"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    0.561954\n",
       "1.0    0.438046\n",
       "Name: education, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of education types\n",
    "train_df['education'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "7yfIIL4LWX38",
    "outputId": "dea9ef34-b3cc-4f9e-8ed9-06af066277e0"
   },
   "outputs": [
    {
     "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": [
    "#count plot types of education\n",
    "ax=sns.countplot(x='education', data=train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b7XLmODl_16X",
    "outputId": "5c357972-4b16-41f8-fc44-c62d6c60cbc2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.986714\n",
       "1    0.013286\n",
       "Name: last_delinq_none, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check percentage of 0/1 in last_delinq_none column\n",
    "train_df['last_delinq_none'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "3EnaaI1q_cgs",
    "outputId": "e9eeba10-05cd-4d7e-e6e3-3a85cc3fb787"
   },
   "outputs": [
    {
     "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": [
    "#distribution of 0/1 \n",
    "ax=sns.countplot(x='last_delinq_none', data=train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5NCwW52Ac91L"
   },
   "source": [
    "It can be inferred from the above bar plots that:\n",
    "\n",
    "*   Around 85% of 'proof_submitted' are Aadhar\n",
    "*   Around 10% of 'proof_submitted' are Aadhar\n",
    "*   PAN, Driving, Passport together are around 5% of 'proof_submitted'\n",
    "\n",
    "\n",
    "*   Around 56% of 'education' in the dataset are 2.0\n",
    "*   Around 44% of 'education' in the dataset are 1.0\n",
    "\n",
    "\n",
    "*   Around 99% of applicants has no the loan defaulted in at least one of the past loans\n",
    "*   Just 1% of applicants has the loan defaulted in at least one of the past loans\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 291
    },
    "id": "Lvl4iNb0WXxW",
    "outputId": "26ada9e0-36a2-4db0-bb69-ddf6db165d30"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RpHL66bFq3TmHDrfbspXGJFPCVT0iUg3cA3wUeBX4Jk5ieO4cx88RkbUiskdEdovIfx/nGBGRb4lIs4jsEJHLz+u7MJ5rbusjGBAqivzL+zVuaeSQm5SMMcmRUD9AEfkZsAR4FHiXqp52dz0hIpvPcVoY+FtV3SoipcAWEXlOVffEHfM2YJH7uAr4nvvVpJnm9j5qS/LJ8aHHUEx1cT4BEQ60hXyLwZjpKNEO4T9U1WfjN4hIvqoOq2rTeCe4yeK0+zwkInuBBiA+EdwOPKKqCmwUkQoRmRmXaEyaOHgm5Gv7AEAgR6gtzefgGSsRGJNMiVYN/fM42zYk+iYiMg9YBWwas6sBOB73+oS7zaSRnsFRTvcMUV/mX/tATF1ZPgfOWInAmGSasEQgIvU4v5gLRWQVEKsXKAOKEnkDESkBngT+RlV7zydIEVkDrAFobLSFy1Mt9ot3RnkaJILSAnac6KF/OEyxDyOcjZmOJvtJugWngXg28K9x20PAP0x2cbdH0ZPAj1T1qXEOOQnMiXs92932R1T1PuA+gKamJpt+MsX2tTqJIB1KBDPKnOqp5rY+Vsyp8DkaY6aHCROBqj4MPCwi71HVJ6dyYXHmIXgA2Kuq/3qOw54BPiUiP8ZpJO6x9oH0c6A1RGl+LuWF/vcUnuF2X91/JmSJwJgkmaxq6AOq+v+AeSLy2bH7J/gFD3Ad8EFgp4hsc7f9A9Donvt94Fng7UAzMAB8ZMrfgfHc/tYQi+tLfZljaKyqkjzycnM4aO0ExiTNZFVDxe7XkqleWFVf4vU2hXMdo8Anp3ptkzqqyr7WXt65YpbfoQCQI8LC2hIOWM8hY5JmsqqhH7hf/zE14Zh009o7RO9QmKX1pX6H8pol9aVsPNzpdxjGTBuJTjr3f0SkTESCIvJbEWkXkQ94HZzx3363oXjxjPRJBBfPLOV0zxBnbTF7Y5Ii0XEEf+p2/XwnzlxDC4HPexWUSR+xRJBOJYJlM8sB2Hv6vHojG2PGSDQRxKqQ3gH8RFV7PIrHpJn9rSFmlOVTUZTndyivuXimk5T2WCIwJikSHZHzCxHZBwwCnxCRWmDIu7BMuth/JpRW1UIA1SX51JcVsPuUJQJjkiHRaai/AFwLNKnqKNCPM0+QmcbCkSgH2/rSqlooZtmsMvZYIjAmKaYyRn8pzniC+HMeSXI8Jo0c7RxgJBxlSX2Z36G8wbKZZbx4oJ2h0QgFwYDf4RiT0RKdhvpRYAGwDYi4mxVLBNNabI6hdC0RRKLKwTN9LJ9d7nc4xmS0REsETcAydwCYyRL7WkPkCCysm/J4Qs8tm+mUUnaf6rFEYMwFSrTX0C6g3stATPrZ39rLvOritKx6aawqojQ/l50nrQObMRcq0RJBDbBHRF4GhmMbVfU2T6IyaWF/a4iLZ6Zf+wBATo6wfHY5O05YIjDmQiWaCL7sZRAm/fQOjXK0c4A7Vs/2O5Rzumx2BQ+8dJjhcIT83PQrtRiTKRLtPvoizojioPv8FWCrh3EZn+1yq1wubUi/+vfHNrXw2KYWegdHGY0o33z+oN8hGZPREp1r6GPAT4EfuJsagKe9Csr4L5YIlqdhIohpqCwE4MTZQZ8jMSazJdpY/Emc9QV6AVT1IFDnVVDGfztP9jKrvIDqEn8XrJ9IRWGQ4ryAJQJjLlCiiWBYVV+b6tEdVGZdSaex3Sd70rJaKJ6IMLuyiBNnB/wOxZiMlmgieFFE/gFnEfu3Aj8Bfu5dWMZPoaFRDnf0p3W1UMzsykLaQ8P0Do36HYoxGSvRXkNfAO4FdgIfx1li8n6vgjL+ik3m1tE3wmObWnyOZmJzq4tR4NWWbm5YXOt3OMZkpIQSgapGReRp4GlVbfc4JuOzWEPxrIoCnyOZ3JzKQgTYcrTLEoEx52nCqiFxfFlEOoD9wH53dbIvpiY844edJ3soK8iltCDodyiTyg8GmFlewOZjZ/0OxZiMNVkbwWdwegtdoapVqloFXAVcJyKf8Tw644udJ3toqCj0O4yENVYXs+14N+FI1O9QjMlIkyWCDwJ3qeqR2AZVPQx8APjQRCeKyIMi0iYiu86x/0YR6RGRbe7DShlpoG84zJGOfmZVZk4imFtdxMBIhL2nQ36HYkxGmiwRBFW1Y+xGt51gsnqDh4BbJzlmnaqudB9fmeRYkwK7T/agSkaVCOZWFQGw+ViXz5EYk5kmSwQj57kPVf09YD+ZGSY2m2cmJYKKojxmWTuBMedtsl5DK0RkvPUABUhGl5JrRGQ7cAr4nKruHu8gEVkDrAFobGxMwtuac9l1socZZfkZ0VAcr2leFZuOdKKqiIjf4RiTUSYsEahqQFXLxnmUquqF/qbYCsxV1RXAvzPB3EWqep+qNqlqU22tdRH00s6TPRkxkGyspnmVnOkdtukmjDkPiY4sTjpV7VXVPvf5s0BQRGr8isdAz+Aoh9r7uWx2hd+hTNnquZUAbLHqIWOmzLdEICL14pbhReRKN5ZOv+IxsP14NwCXN1b6HMnULa0voyQ/1xqMjTkPiU4xMWUi8jhwI1AjIieAL+H2NFLV7wN3AJ8QkTAwCNxpayL7a2vLWURgxZxyWroyayK3QI6wqrGCzUetRGDMVHmWCFT1rkn2fxv4tlfvb6Zua0s3S2aUZlxDcUzT3Cr+7bcH6BkYpbwoM78HY/zgW9WQSS/RqPJqy1lWZWC1UMw1C6pRhY1HrIbRmKmwRGAAONTeR2gozKrGzGsojlk5p4LCYID1zW8YA2mMmYAlAgM47QOQmQ3FMXm5OVxxURXrD1mJwJip8KyNwGSG2HoDT209QWEwwMbDnbx8JHN73ly7oJqv/nIfbaEh6krTfxptY9KBlQgMAC1dA8ypKiQnw0flXrugGoANViowJmGWCAxDoxHaQ8M0upO3ZbJLZpVTXhhk3UFrJzAmUVY1ZDjeNYACjVXFfody3uKX1GysKuLXu1pZOaeCD1w918eojMkMViIwtHQNIDgLwU8HS2aUEhoOc7pnyO9QjMkIlggMLV0D1JXlUxAM+B1KUiyaUQLAgTO2UI0xibBEkOWiqhw/OzAt2gdiSguCNFQUcqDVEoExibBEkOXO9A4xNBplXnXmtg+MZ/GMUlq6Bujqn3D9JGMMlgiy3pGOfgAuqpleieCSWWUo8NyeVr9DMSbtWSLIcofb+6ksClJRlOd3KEk1s7yAquI8nt1picCYyVgiyGLRqHK0s5+Lakr8DiXpRIRLZ5Xxh+YOugesesiYiVgiyGIH2kIMjESYP82qhWIubSgnHFV+s+eM36EYk9YsEWSxTYedOYWmW/tATENFIXOqCnn61ZN+h2JMWrNEkMVeau6gsihIZfH0ah+IERH+YvUc1h/q5Fhnv9/hGJO2LBFkqdFIlA2HOlk0o9TvUDz13qY55Ag88cpxv0MxJm1ZIshS24530zccZlHd9GsojldfXsDNS+v4yZYTjEaifodjTFqyRJCl1h1oJ5AjzJ+GPYbGuvvqubSHhvn59lN+h2JMWrJEkKV+f7DDWdoxb3rMLzSRGxfXsrS+lO/+7hDRqPodjjFpx7NpqEXkQeCdQJuqXjrOfgG+CbwdGADuUdWtXsVjXtfVP8KOE918+k8W+R1KSogI/+2mhXz68Vf5zZ4z55x24v1XNaY4MmPSg5clgoeAWyfY/zZgkftYA3zPw1hMnLX72ogq/MnSGX6H4rnHNrXw2KYWegdHqS7O48vP7CaqViowJp5niUBVfw9MtPjt7cAj6tgIVIjITK/iMa97fu8ZZpTlc2lDmd+hpEyOCG+5eAatvUPsPNHjdzjGpBU/2wgagPg+fSfcbW8gImtEZLOIbG5vb09JcNPV0GiEFw+085aLZyAZvj7xVC2fXc7M8gKe23uGiLUVGPOajGgsVtX7VLVJVZtqa2v9DiejbTzcycBIhLcum/7VQmPliPDWZTPo6h9h87GJCqvGZBc/E8FJYE7c69nuNuOhX+8+Q1FegKvnV/sdii+WzChlbnURL+xrYyRs4wqMAX8TwTPAh8RxNdCjqqd9jGfae3TDMf5r20kW1pXw1NaTf7Tge7YQEW5ZVk9oKMyGw51+h2NMWvCy++jjwI1AjYicAL4EBAFU9fvAszhdR5txuo9+xKtYjONwex8DIxEuayj3OxRfzaspZlFdCeubO7huYTW5ORlRQ2qMZzxLBKp61yT7FfikV+9v3mjHyR7yc3Om/fxCibhuYQ0PrT/KzhM9rGqs9DscY3xlfwpliZFwlD2nelk2s4xgwP7bF9WVUFuSz/pDnaiNKzBZzn4jZIm1+9sYHI1w2ezsrhaKERGuWVDNye5BTnYP+h2OMb6yRJAlfrrlBKX5uSyss2qhmJVzKsjNEbYd7/Y7FGN8ZYkgC3T2DbN2Xxsr51QQyMmuQWQTKQgGWDyjlJ0nemzaCZPVLBFkgf/adopwVFk11xpFx1oxp4LQcJgjHbaCmclelgimOVXliVeOs7yhnPqyAr/DSTtL60vJy81hu1UPmSxmiWCa29pylv1nQtxtUyyPKxjIYWl9KXtbQ7ZWgclalgimuR9tbKEkP5d3rZjldyhpa2l9Kf3DYXactFlJTXayRDCNne0f4Rc7T/Pnqxoozvds7GDGW1xXigAv7GvzOxRjfGGJYBr70aZjjISjfODquX6HktaK8nOZU1XEWksEJktZIpimhkYjPLT+GDcsrmVJvY0dmMzS+lJ2nuyhLTTkdyjGpJwlgmnqmW2n6Ogb5mPXz/c7lIyw2J1/6Xf7beEjk30sEUxDqsoP1x3m4pllXLcwO9cdmKqZ5QXUlxVY9ZDJStaCOA19+Zk9HGzr472rZ/P4y8cnP8EgIty0tJafbz/NSDhKXq79jWSyh33ap6F1ze2UFeSy3CaYm5KbltTRNxxm81FbxtJkF0sE08zOEz0cbu/n2gU1tuDKFF23sIa8QA5r91v1kMku9ptimvm35w9QGAxw5UVVfoeScYrzc7lqfpWNJzBZxxLBNLLteDe/3dfG9YtqKAgG/A4nI920pI5D7f20dA74HYoxKWOJYBr5xnMHqCwKcs186yl0vm5eWgfAC/vO+ByJMaljiWCa2HLsLC8eaGfNmxeQb6WB8zavppj5NcW8YOMJTBbxNBGIyK0isl9EmkXkC+Psv0dE2kVkm/v4qJfxTGf/9vwBqorz+NA1Np3EhbppaR0bD3cyMBL2OxRjUsKzRCAiAeA7wNuAZcBdIrJsnEOfUNWV7uN+r+KZzjYe7mTdwQ7+6ob5NrlcEty8tI6RcJSXDnb4HYoxKeFlieBKoFlVD6vqCPBj4HYP3y8r/b+Nx/jsf26jvDBIXiDAY5ta/A4p4115URVVxXk8s/2U36EYkxJeJoIGIH5Y6wl321jvEZEdIvJTEZkz3oVEZI2IbBaRze3tVncb79WWbk51D3HLJfU2GjZJgoEc3rF8Js/tOUNoaNTvcIzxnN+/OX4OzFPVy4DngIfHO0hV71PVJlVtqq2tTWmA6ax/OMxvdrcyp7KQFTaKOKn+bNUshsNRfr3beg+Z6c/LRHASiP8Lf7a77TWq2qmqw+7L+4HVHsYz7Xzvd4cIDYd5x2WzEBG/w5lWLm+sZE5VIT979YTfoRjjOS9bFl8BFonIRTgJ4E7g/fEHiMhMVT3tvrwN2OthPNPK8a4B7lt3mBWzy2msKvI7nGlhbPvKkhmlPL+3jf2tIVvTwUxrnpUIVDUMfAr4Nc4v+P9U1d0i8hURuc097NMisltEtgOfBu7xKp7pRFX5+6d2kpsj3HJJvd/hTFtXX1RNUV6AH7x4yO9QjPGUqKrfMUxJU1OTbt682e8wfPWjTcf4Hz/bxT//2aXkWJWQpw619/HQ+qP87nM3MsdKXiaDicgWVW0ab5/fjcVmivae7uWffrGHNy2s4e6rGv0OZ9r76PUXEcgR/uVZq7U005clggzSPTDCxx/dQnlhkH993wprIE6BmeWF/M1bFvHLXa38cufpyU8wJgNZIsgQAyNh/vKhV2jtGeK7d6+mrrTA75Cyxprr53NpQxn/8+ldNiupmZYsEWSAwZEIax7Zwrbj3XzrrpWsnlvpd0hZJTeQwzfvXEVElQ89uImOvuHJTzImg9jENGlmbBfG4XCER8ioDegAAA9HSURBVDYc42hnP1+/YwW3XjrTp8iy24LaEh748BXcff9G3vv9DfzHPVcwr6bY77CMSQrrNZRm4hPB8GiEhzYc5XjXAO9dPYcVcyr8C8wAcKyzn0c3HqMgGOCHH2qy0pnJGNZrKAMNh19PAu+7otGSQJqYW13MJ25YQFlBLnf9cKNNTGemBUsEaWg4HOHh9U4S+IumOSxvsHmE0kl1ST53XzWXmeUFfPrxV/nA/Zt4dMMxm/nVZCxLBGkmElV+/PJxWtwkcNlsKwmko+L8XO5900VcPb+al5o7ePAPR+gbtoVsTGayRJBmnt11mv1nQrxrxSxLAmkuNyeH21bM4o7VszneNcB31jaz7Xi332EZM2WWCNLIoxuOsuFQJ9ctqOaqi2wB+kxxeWMlf3XDAnIE/uL7G/jxy1ZFZDKLJYI08fsD7Xz553tYMqOUty23LqKZZlZFIZ+8cSFXza/iC0/t5AtP7mBoNOJ3WMYkxMYRpIGDZ0J88kdbWVRXwvua5thEchmqKD+XWy6pJyDCj185zu8PtPP+q+byqZsX+h2aMROyEoHP2kJD/OXDr5AfDPDAPVeQHwz4HZK5ADki/Okl9Xzw6rl0DYzw7bUHeX6PrXJm0puVCHzUPTDCB+9/mc6+ER772NU0VBT6HZJJkotnlvGpmxbx2MvH+Ogjm/nEjQv47FsXEwwk72+vc3VXfb/NSmumyBKBT051D/KXD73CkY5+/uMjV7DSBoxNO1XFeXz8zQvY19rL9353iHUH2/n6HSu4eGZZUq7fOzTKrpM9HOnoJxJVakry7XNkzoslAh/8bn8bf/fTHQyORHjwniu4bmGN3yEZjwQDOfzvd1/G9Ytq+Z9P7+Id31rHe1fP4eM3zGd+bcl5XfNs/wjfWdvMQ+uPEo4qVcV55OfmcPBMHy81d9DaO8QX37WMsoJgkr8bM13ZXEMpoqpsbenmh78/zK92t7KwroS3L59JfZlNJz3dxapqzvaP8O21zTy64RgjkShXzKvkhsW1XL+olksbygnkTNxJ4Gz/CA9vOMoD647QPxJm1ZxKrl9c89qU5AMjYV462MG65g5mVxbyH/dccd7Jxkw/E801ZInAQz2Do/yhuYMX97fz4oF2WnuHKC3I5S+vu4j/dtMCntxy0u8QTQqMrbNvDw3zxCstPPZyC6e6hwDIz81hRlkBb15cw6K6UmZVFFKcHyAcUY6fHWD9oU5e2NvG4GiEP102g8/fsoRXjp4d9/0Wzyjh449uIRxV7vvgaq6ab2NSjCWClPr2C83sONHN/jMhjncNEFUoCOawsLaExTNKWd5Qbj2DzGv6hsM0t/XR0jVAa88gnX0jhMaZqqKuNJ8/uXgG91w7jyX1pcC5G4sBuvpHeHj9Ubr6R3j35Q2saqy0RuQsZ4nAY8PhCM/tOcMTrxxn3cEOAGZVFLB4RimL6kpprCqatNhvDDhViKHhML2Do4xEoghCRWGQ8qLglMeXDI5E+NGmYxzu6OfmpXU88OEmW940i/mWCETkVuCbQAC4X1W/OmZ/PvAIsBroBN6nqkcnuma6JAJV5dXj3Ty19QQ/336ansFRGioKWVpfyuq5lVQU5fkdojGEo1GefvUUW1vOcuW8Kr502zIumWWz2WajiRKBZ72GRCQAfAd4K3ACeEVEnlHVPXGH3QucVdWFInIn8DXgfV7FNBlVZSQSZXAkQv9IhMGRMP3DEQZGIvQPhzndM0hL1wBHOwfYeuwsnf0jFARzuOWSet59+WzetLCGJ1457lf4xrxBbk4O77m8gbnVRazd38Y7vvUSlzdWcOOSOpbUl1JWEKS0IJeCYABQVCGqoCjRKETdPxSjGtunKM7PSlRB1Uk2oaEwPQOj9AyO0j04Qs/gKD2DTsmmZ3CU3qFRAiIU5gUoyA1QXhSkpiSP6uJ8qkvyqC7Jp6bY+VpdkkdlUd4bStHRqDIcjjI4GmFgJMzQaITBkShD4QiBHCEvkENBMIe8QID8YA4Fuc7X/NycpJeEYt9/JKpENfZwXququ53X9kWiSo4IuTlCIEfIzckhEHj9dUCEHB9rDTwrEYjINcCXVfUW9/XfA6jq/4475tfuMRtEJBdoBWp1gqDOt0Swdl8bX3pm92v/ceGoEo0qEVUiEefrSDhKODrx/cjNESqL8phdWciC2hKWzSpzf4iMSW9vX17PTzaf4MmtJ9h/JoRXlQE5AgXBAIXBAIV5ztf8YABVZTQSZTSiDI5E6BsO0z8cZrwwRCAvkIMqRNxfphcSb35uDgXBwGsD+jQuoTlf3aTn/Hs90bnJMX7bhcZyLiL8caLIiU8cQiAg3H3VXP7qhgXneX0fSgRAAxD/5/EJ4KpzHaOqYRHpAaqBjviDRGQNsMZ92Sci+z2JeAoSTEU1jPle0pjF6g2L1RtZGetLwCfO//S559qREQPKVPU+4D6/45gqEdl8rgycbixWb1is3rBYk8vLSedOAnPiXs92t417jFs1VI7TaGyMMSZFvEwErwCLROQiEckD7gSeGXPMM8CH3ed3AC9M1D5gjDEm+TyrGnLr/D8F/Bqn++iDqrpbRL4CbFbVZ4AHgEdFpBnowkkW00kmVWdZrN6wWL1hsSZRxg0oM8YYk1y2MI0xxmQ5SwTGGJPlLBGcBxG5VUT2i0iziHxhnP2fFZE9IrJDRH4rInPj9kVEZJv7GNt47kes94hIe1xMH43b92EROeg+Pjz2XB9i/UZcnAdEpDtuX6rv64Mi0iYiu86xX0TkW+73skNELo/bl+r7Olmsd7sx7hSR9SKyIm7fUXf7NhHxfG6XBGK9UUR64v6vvxi3b8LPjw+xfj4uzl3uZ7TK3ZfS+zopVbXHFB44Dd+HgPlAHrAdWDbmmJuAIvf5J4An4vb1pVms9wDfHufcKuCw+7XSfV7pZ6xjjv9rnA4IKb+v7vu9Gbgc2HWO/W8HfgkIcDWwyY/7mmCs18ZiAN4Wi9V9fRSoSaP7eiPwiwv9/KQi1jHHvgunV6Qv93Wyh5UIpu5KoFlVD6vqCPBj4Pb4A1R1raoOuC834oyh8MOksU7gFuA5Ve1S1bPAc8CtHsUJU4/1LuBxD+OZkKr+Hqen27ncDjyijo1AhYjMJPX3ddJYVXW9Gwv4+3lN5L6ey4V81s/LFGP19fM6GUsEUzfe1BkNExx/L85fhjEFIrJZRDaKyJ95EWCcRGN9j1s18FMRiQ0CnOr3eaESfj+3qu0i4IW4zam8r4k41/eT6vs6VWM/rwr8RkS2uFO9pINrRGS7iPxSRC5xt6XtfRWRIpxk/2Tc5rS6rxkxxUSmEpEPAE3ADXGb56rqSRGZD7wgIjtV9ZA/EQLwc+BxVR0WkY8DDwM3+xhPIu4Efqqqkbht6XZfM46I3ISTCN4Ut/lN7n2tA54TkX3uX8J+2Yrzf90nIm8HngYW+RhPIt4F/EFV40sPaXVfrUQwdYlMnYGIvAX4H8Btqjoc266qJ92vh4HfAav8jFVVO+Piux9nbYiEzk2yqbzfnYwpZqf4vibiXN9Pqu9rQkTkMpz//9tV9bVpXuLuaxvwM5wqGN+oaq+q9rnPnwWCIlJDmt5X10Sf17S4r743UmTaA6cUdRinaiLWKHXJmGNW4TRcLRqzvRLId5/XAAfxsEErwVhnxj3/c2Cj+7wKOOLGXOk+r/IzVve4pTgNbeLXfY1733mcu1HzHfxxY/HLftzXBGNtBJqBa8dsLwZK456vB271OdZ6Xh8IeyXQ4t7jhD4/qYzV3V+O045Q7Pd9nehhVUNTpIlNnfF1oAT4iTgLYrSo6m3AxcAPRCSKUxr7qv7xQj1+xPppEbkNCON8YO9xz+0SkX/CmTMK4Cv6x0VbP2IF56+rH6v7U+RK6X0FEJHHcXqw1IjICeBLQND9Xr4PPIvTc6gZGAA+4u5L6X1NMNYv4kz//l338xpWZ7bMGcDP3G25wGOq+iufY70D+ISIhIFB4E73szDu58fnWMH54+o3qtofd2rK7+tkbIoJY4zJctZGYIwxWc4SgTHGZDlLBMYYk+UsERhjTJazRGCMMWlsssntxjn+L8SZ9HK3iDyW0DnWa8gYY9KXiLwZ6MOZu+rSSY5dBPwncLOqnhWROnUGrU3ISgRmWhGRPr9jSCURmSci7/c7DuMdHWdyOxFZICK/cucqWiciS91dHwO+o+4kgokkAbBEYEymmwdYIsg+9wF/raqrgc8B33W3LwYWi8gf3AkYE5rZ1hKBmZacdWHk6+6CIDtF5H3u9hJxFgva6m6/3d0+T0T2isgP3brV34hI4QTX/5iIvOLOgvmkO8MkIvKQiHzP/SE8LM5CKg+6134o7vy73PffJSJfi9veF/f8jtg57nW/Jc7CMYdF5A73sK8C17sLnHwmeXfQpCsRKcFZQ+InIrIN+AEw092dizMJ3404U1//UEQqJrumJQIzXb0bWAmsAN4CfF2c9QCGgD9X1ctxFhD6v+KO9cf5AfqOql4CdAPvmeD6T6nqFaq6AtiLM2tnTCVwDfAZ4BngG8AlwHIRWSkis4Cv4czyuhK4IsGps2fizAz6TpwEAPAFYJ2qrlTVbyRwDZP5coBu9/889rjY3XcCeEZVR1X1CHCABGZntURgpqs34UyvHVHVM8CLwBU4E5T9i4jsAJ7HmbN+hnvOEVXd5j7fglPtci6XunWzO4G7cX7Rx/zcnf9mJ3BGVXeqahTY7V7zCuB3qtquqmHgRzirXU3maVWNuvMozZj0aDMtqWovcERE3guvlX5jy4s+jVMawJ2VdTHOZHwTskRgss3dQC2wWlVXAmeAAnffcNxxESZer+Mh4FOquhz4x7hrxF8nOuaa0UmuCc6CJTEFY/bFX0swWcGd3G4DsERETojIvTif43tFZDvOHxix1dh+DXSKyB5gLfB5jZtW/Fxs9lEzXa0DPi4iD+NM/fxm4PPA+4A2VR11F2KZe57XLwVOi0gQ54dyKnPfvwx8y/2L7SxOXe6/u/vOiMjFwH6cmStDk1wr5MZipilVvescu97QEOyWRD/rPhJmJQIzXf0M2IEzL/0LwN+paitONUyTW6XzIWDfeV7/fwGbgD9M9Rqqehqnbn+tG98WVf0vd/cXgF/gzFF/OoHL7QAibqO1NRab82IDyowxJstZicAYY7KctREYMwER+Q5w3ZjN31TV//AjHmO8YFVDxhiT5axqyBhjspwlAmOMyXKWCIwxJstZIjDGmCz3/wE49yUuCd0xDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check for normal distribution\n",
    "ax=sns.distplot(train_df['loan_amount'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "zySAJp9-WXp5",
    "outputId": "f4939579-1a3d-4c27-e9c1-d4d321cc919d"
   },
   "outputs": [
    {
     "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": [
    "#check outliers\n",
    "ax=sns.boxplot(train_df['loan_amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 291
    },
    "id": "JNTcDCptWXiE",
    "outputId": "52871160-2f25-4af9-888d-ff12d49d1ed4"
   },
   "outputs": [
    {
     "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": [
    "# check for normal distribution\n",
    "ax=sns.distplot(train_df['asset_cost'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "aG858dY7WXaE",
    "outputId": "a441516b-7aac-41e1-b8f6-7443f9971958"
   },
   "outputs": [
    {
     "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": [
    "#check outliers\n",
    "ax=sns.boxplot(train_df['asset_cost'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "acPvqtKqWXRO",
    "outputId": "0b8aa707-72c9-4604-c024-b0bcf8ff778e"
   },
   "outputs": [
    {
     "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": [
    "# check for normal distribution\n",
    "ax=sns.distplot(train_df['no_of_loans'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "K6c8V9_eWXHd",
    "outputId": "0bc4421f-2f8f-4d34-b035-4ff352b7d60d"
   },
   "outputs": [
    {
     "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": [
    "#check outliers\n",
    "ax=sns.boxplot(train_df['no_of_loans'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "vew60yA0hrJs",
    "outputId": "8974e9d7-443c-47af-9534-522b72bc6ae8"
   },
   "outputs": [
    {
     "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": [
    "# check for normal distribution\n",
    "ax=sns.distplot(train_df['no_of_curr_loans'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "kZ51SGH_lKKx",
    "outputId": "e884b954-c8ae-4d1d-9bce-854f4068a6f9"
   },
   "outputs": [
    {
     "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": [
    "#check outliers\n",
    "ax=sns.boxplot(train_df['no_of_curr_loans'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x-GUdOxZl_ca"
   },
   "source": [
    "Bivariate Analysis...\n",
    "Check for correlation between features and target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 303
    },
    "id": "Pinmt6TsmA9T",
    "outputId": "918019bd-acb4-4982-f846-f78aedc613f7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2f46964d0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ed=pd.crosstab(train_df['education'],train_df['loan_default']) \n",
    "ed.div(ed.sum(1).astype(float), axis=0).plot(kind=\"bar\", figsize=(4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 333
    },
    "id": "CUOG-OYbmA4r",
    "outputId": "a36c4417-1787-4823-c30f-a7180135821d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2f46ba490>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps=pd.crosstab(train_df['proof_submitted'],train_df['loan_default']) \n",
    "ps.div(ps.sum(1).astype(float), axis=0).plot(kind=\"bar\", figsize=(4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "Id1GwS03mAzy",
    "outputId": "c9b8a18d-2e06-4945-fe4a-a881cf24da40"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2f48bc8d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ldn=pd.crosstab(train_df['last_delinq_none'],train_df['loan_default']) \n",
    "ldn.div(ldn.sum(1).astype(float), axis=0).plot(kind=\"bar\", figsize=(4,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VrhdhyhUKk00"
   },
   "source": [
    "'loan_default' is correlated with 'loan_amount' best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "HmX5umC-mAcv",
    "outputId": "61c6fc56-95c2-426b-cb0b-d99bc5db72ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2f4647a10>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df.groupby('loan_default')['asset_cost'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "wNf91XmjWrWZ",
    "outputId": "3df20c79-99d7-49f8-b49b-f14e4d1b4b2f"
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "   loan_id  age  education proof_submitted  loan_amount  asset_cost  \\\n",
       "0        1   27        1.0          Aadhar       504264      820920   \n",
       "1        2   48        1.0          Aadhar       728556      831444   \n",
       "2        3   30        2.0         VoterID       642936      826092   \n",
       "3        4   28        1.0          Aadhar       746556      930924   \n",
       "4        5   29        1.0          Aadhar      1139880     1902000   \n",
       "\n",
       "   no_of_loans  no_of_curr_loans  last_delinq_none  loan_default  \n",
       "0            2                 2                 0             0  \n",
       "1            6                 2                 0             0  \n",
       "2            0                 0                 0             1  \n",
       "3            0                 0                 0             0  \n",
       "4            0                 0                 0             0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "id": "PI01t1X8Mfhb",
    "outputId": "f4a5c695-8f9e-4d74-9521-787190820ebb"
   },
   "outputs": [
    {
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       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row0_col1 {\n",
       "  background-color: #5875e1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row0_col2, #T_68b13_row1_col3, #T_68b13_row1_col4, #T_68b13_row2_col0, #T_68b13_row4_col0, #T_68b13_row4_col1, #T_68b13_row4_col5, #T_68b13_row4_col6, #T_68b13_row4_col7, #T_68b13_row6_col8, #T_68b13_row8_col6 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row0_col3, #T_68b13_row6_col2, #T_68b13_row8_col7 {\n",
       "  background-color: #455cce;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row0_col4 {\n",
       "  background-color: #536edd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row0_col5, #T_68b13_row3_col2, #T_68b13_row7_col0 {\n",
       "  background-color: #4358cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row0_col6 {\n",
       "  background-color: #465ecf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row0_col7 {\n",
       "  background-color: #4055c8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row0_col8, #T_68b13_row5_col3 {\n",
       "  background-color: #4e68d8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row1_col0, #T_68b13_row6_col0 {\n",
       "  background-color: #3f53c6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row1_col2 {\n",
       "  background-color: #6384eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row1_col5 {\n",
       "  background-color: #86a9fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row1_col6 {\n",
       "  background-color: #85a8fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row1_col7, #T_68b13_row8_col2 {\n",
       "  background-color: #506bda;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row1_col8 {\n",
       "  background-color: #4257c9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row2_col1 {\n",
       "  background-color: #7b9ff9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row2_col3, #T_68b13_row2_col7 {\n",
       "  background-color: #4961d2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row2_col4 {\n",
       "  background-color: #7597f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row2_col5 {\n",
       "  background-color: #516ddb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row2_col6, #T_68b13_row3_col1, #T_68b13_row5_col2 {\n",
       "  background-color: #4b64d5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row2_col8 {\n",
       "  background-color: #5673e0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row3_col0, #T_68b13_row3_col7 {\n",
       "  background-color: #3e51c5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row3_col4 {\n",
       "  background-color: #f39475;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_68b13_row3_col5, #T_68b13_row5_col4, #T_68b13_row6_col4, #T_68b13_row7_col2 {\n",
       "  background-color: #4c66d6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row3_col6, #T_68b13_row6_col3, #T_68b13_row7_col8 {\n",
       "  background-color: #4f69d9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row3_col8 {\n",
       "  background-color: #6e90f2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row4_col2 {\n",
       "  background-color: #5e7de7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row4_col3 {\n",
       "  background-color: #f49a7b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_68b13_row4_col8 {\n",
       "  background-color: #5977e3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row5_col0, #T_68b13_row5_col8 {\n",
       "  background-color: #3d50c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row5_col1 {\n",
       "  background-color: #97b8ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_68b13_row5_col6, #T_68b13_row6_col5 {\n",
       "  background-color: #f08a6c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row5_col7 {\n",
       "  background-color: #8db0fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_68b13_row6_col1 {\n",
       "  background-color: #96b7ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_68b13_row6_col7 {\n",
       "  background-color: #9bbcff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_68b13_row7_col1 {\n",
       "  background-color: #6b8df0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row7_col3, #T_68b13_row8_col0 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row7_col4 {\n",
       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row7_col5 {\n",
       "  background-color: #94b6ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_68b13_row7_col6 {\n",
       "  background-color: #a2c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_68b13_row8_col1 {\n",
       "  background-color: #5470de;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row8_col3 {\n",
       "  background-color: #6f92f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row8_col4 {\n",
       "  background-color: #6a8bef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68b13_row8_col5 {\n",
       "  background-color: #3c4ec2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_68b13_\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >loan_id</th>\n",
       "      <th class=\"col_heading level0 col1\" >age</th>\n",
       "      <th class=\"col_heading level0 col2\" >education</th>\n",
       "      <th class=\"col_heading level0 col3\" >loan_amount</th>\n",
       "      <th class=\"col_heading level0 col4\" >asset_cost</th>\n",
       "      <th class=\"col_heading level0 col5\" >no_of_loans</th>\n",
       "      <th class=\"col_heading level0 col6\" >no_of_curr_loans</th>\n",
       "      <th class=\"col_heading level0 col7\" >last_delinq_none</th>\n",
       "      <th class=\"col_heading level0 col8\" >loan_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_68b13_level0_row0\" class=\"row_heading level0 row0\" >loan_id</th>\n",
       "      <td id=\"T_68b13_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_68b13_row0_col1\" class=\"data row0 col1\" >-0.017165</td>\n",
       "      <td id=\"T_68b13_row0_col2\" class=\"data row0 col2\" >-0.034615</td>\n",
       "      <td id=\"T_68b13_row0_col3\" class=\"data row0 col3\" >-0.020757</td>\n",
       "      <td id=\"T_68b13_row0_col4\" class=\"data row0 col4\" >-0.032053</td>\n",
       "      <td id=\"T_68b13_row0_col5\" class=\"data row0 col5\" >-0.023589</td>\n",
       "      <td id=\"T_68b13_row0_col6\" class=\"data row0 col6\" >-0.015454</td>\n",
       "      <td id=\"T_68b13_row0_col7\" class=\"data row0 col7\" >-0.002829</td>\n",
       "      <td id=\"T_68b13_row0_col8\" class=\"data row0 col8\" >0.011921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b13_level0_row1\" class=\"row_heading level0 row1\" >age</th>\n",
       "      <td id=\"T_68b13_row1_col0\" class=\"data row1 col0\" >-0.017165</td>\n",
       "      <td id=\"T_68b13_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_68b13_row1_col2\" class=\"data row1 col2\" >0.102063</td>\n",
       "      <td id=\"T_68b13_row1_col3\" class=\"data row1 col3\" >-0.058011</td>\n",
       "      <td id=\"T_68b13_row1_col4\" class=\"data row1 col4\" >-0.122741</td>\n",
       "      <td id=\"T_68b13_row1_col5\" class=\"data row1 col5\" >0.193026</td>\n",
       "      <td id=\"T_68b13_row1_col6\" class=\"data row1 col6\" >0.184503</td>\n",
       "      <td id=\"T_68b13_row1_col7\" class=\"data row1 col7\" >0.050325</td>\n",
       "      <td id=\"T_68b13_row1_col8\" class=\"data row1 col8\" >-0.029571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b13_level0_row2\" class=\"row_heading level0 row2\" >education</th>\n",
       "      <td id=\"T_68b13_row2_col0\" class=\"data row2 col0\" >-0.034615</td>\n",
       "      <td id=\"T_68b13_row2_col1\" class=\"data row2 col1\" >0.102063</td>\n",
       "      <td id=\"T_68b13_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_68b13_row2_col3\" class=\"data row2 col3\" >-0.004588</td>\n",
       "      <td id=\"T_68b13_row2_col4\" class=\"data row2 col4\" >0.083221</td>\n",
       "      <td id=\"T_68b13_row2_col5\" class=\"data row2 col5\" >0.025889</td>\n",
       "      <td id=\"T_68b13_row2_col6\" class=\"data row2 col6\" >0.004606</td>\n",
       "      <td id=\"T_68b13_row2_col7\" class=\"data row2 col7\" >0.026516</td>\n",
       "      <td id=\"T_68b13_row2_col8\" class=\"data row2 col8\" >0.041701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b13_level0_row3\" class=\"row_heading level0 row3\" >loan_amount</th>\n",
       "      <td id=\"T_68b13_row3_col0\" class=\"data row3 col0\" >-0.020757</td>\n",
       "      <td id=\"T_68b13_row3_col1\" class=\"data row3 col1\" >-0.058011</td>\n",
       "      <td id=\"T_68b13_row3_col2\" class=\"data row3 col2\" >-0.004588</td>\n",
       "      <td id=\"T_68b13_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_68b13_row3_col4\" class=\"data row3 col4\" >0.735176</td>\n",
       "      <td id=\"T_68b13_row3_col5\" class=\"data row3 col5\" >0.010867</td>\n",
       "      <td id=\"T_68b13_row3_col6\" class=\"data row3 col6\" >0.014788</td>\n",
       "      <td id=\"T_68b13_row3_col7\" class=\"data row3 col7\" >-0.009412</td>\n",
       "      <td id=\"T_68b13_row3_col8\" class=\"data row3 col8\" >0.115736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b13_level0_row4\" class=\"row_heading level0 row4\" >asset_cost</th>\n",
       "      <td id=\"T_68b13_row4_col0\" class=\"data row4 col0\" >-0.032053</td>\n",
       "      <td id=\"T_68b13_row4_col1\" class=\"data row4 col1\" >-0.122741</td>\n",
       "      <td id=\"T_68b13_row4_col2\" class=\"data row4 col2\" >0.083221</td>\n",
       "      <td id=\"T_68b13_row4_col3\" class=\"data row4 col3\" >0.735176</td>\n",
       "      <td id=\"T_68b13_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_68b13_row4_col5\" class=\"data row4 col5\" >-0.052643</td>\n",
       "      <td id=\"T_68b13_row4_col6\" class=\"data row4 col6\" >-0.054021</td>\n",
       "      <td id=\"T_68b13_row4_col7\" class=\"data row4 col7\" >-0.023489</td>\n",
       "      <td id=\"T_68b13_row4_col8\" class=\"data row4 col8\" >0.048020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b13_level0_row5\" class=\"row_heading level0 row5\" >no_of_loans</th>\n",
       "      <td id=\"T_68b13_row5_col0\" class=\"data row5 col0\" >-0.023589</td>\n",
       "      <td id=\"T_68b13_row5_col1\" class=\"data row5 col1\" >0.193026</td>\n",
       "      <td id=\"T_68b13_row5_col2\" class=\"data row5 col2\" >0.025889</td>\n",
       "      <td id=\"T_68b13_row5_col3\" class=\"data row5 col3\" >0.010867</td>\n",
       "      <td id=\"T_68b13_row5_col4\" class=\"data row5 col4\" >-0.052643</td>\n",
       "      <td id=\"T_68b13_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_68b13_row5_col6\" class=\"data row5 col6\" >0.774705</td>\n",
       "      <td id=\"T_68b13_row5_col7\" class=\"data row5 col7\" >0.235024</td>\n",
       "      <td id=\"T_68b13_row5_col8\" class=\"data row5 col8\" >-0.045738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b13_level0_row6\" class=\"row_heading level0 row6\" >no_of_curr_loans</th>\n",
       "      <td id=\"T_68b13_row6_col0\" class=\"data row6 col0\" >-0.015454</td>\n",
       "      <td id=\"T_68b13_row6_col1\" class=\"data row6 col1\" >0.184503</td>\n",
       "      <td id=\"T_68b13_row6_col2\" class=\"data row6 col2\" >0.004606</td>\n",
       "      <td id=\"T_68b13_row6_col3\" class=\"data row6 col3\" >0.014788</td>\n",
       "      <td id=\"T_68b13_row6_col4\" class=\"data row6 col4\" >-0.054021</td>\n",
       "      <td id=\"T_68b13_row6_col5\" class=\"data row6 col5\" >0.774705</td>\n",
       "      <td id=\"T_68b13_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_68b13_row6_col7\" class=\"data row6 col7\" >0.272716</td>\n",
       "      <td id=\"T_68b13_row6_col8\" class=\"data row6 col8\" >-0.057306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b13_level0_row7\" class=\"row_heading level0 row7\" >last_delinq_none</th>\n",
       "      <td id=\"T_68b13_row7_col0\" class=\"data row7 col0\" >-0.002829</td>\n",
       "      <td id=\"T_68b13_row7_col1\" class=\"data row7 col1\" >0.050325</td>\n",
       "      <td id=\"T_68b13_row7_col2\" class=\"data row7 col2\" >0.026516</td>\n",
       "      <td id=\"T_68b13_row7_col3\" class=\"data row7 col3\" >-0.009412</td>\n",
       "      <td id=\"T_68b13_row7_col4\" class=\"data row7 col4\" >-0.023489</td>\n",
       "      <td id=\"T_68b13_row7_col5\" class=\"data row7 col5\" >0.235024</td>\n",
       "      <td id=\"T_68b13_row7_col6\" class=\"data row7 col6\" >0.272716</td>\n",
       "      <td id=\"T_68b13_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_68b13_row7_col8\" class=\"data row7 col8\" >0.014772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b13_level0_row8\" class=\"row_heading level0 row8\" >loan_default</th>\n",
       "      <td id=\"T_68b13_row8_col0\" class=\"data row8 col0\" >0.011921</td>\n",
       "      <td id=\"T_68b13_row8_col1\" class=\"data row8 col1\" >-0.029571</td>\n",
       "      <td id=\"T_68b13_row8_col2\" class=\"data row8 col2\" >0.041701</td>\n",
       "      <td id=\"T_68b13_row8_col3\" class=\"data row8 col3\" >0.115736</td>\n",
       "      <td id=\"T_68b13_row8_col4\" class=\"data row8 col4\" >0.048020</td>\n",
       "      <td id=\"T_68b13_row8_col5\" class=\"data row8 col5\" >-0.045738</td>\n",
       "      <td id=\"T_68b13_row8_col6\" class=\"data row8 col6\" >-0.057306</td>\n",
       "      <td id=\"T_68b13_row8_col7\" class=\"data row8 col7\" >0.014772</td>\n",
       "      <td id=\"T_68b13_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe2f46dba50>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "corr = train_df.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gF8fqLb4o717",
    "outputId": "3d7eb2c1-f7fc-4e1f-c24b-12217452b8ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_id               0\n",
       "age                   0\n",
       "education           245\n",
       "proof_submitted       0\n",
       "loan_amount           0\n",
       "asset_cost            0\n",
       "no_of_loans           0\n",
       "no_of_curr_loans      0\n",
       "last_delinq_none      0\n",
       "loan_default          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QRTVMiG6o7v6",
    "outputId": "d89c5b3d-c3c6-44b8-de1d-fd709f6f82b7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_id', 'age', 'education', 'proof_submitted', 'loan_amount',\n",
       "       'asset_cost', 'no_of_loans', 'no_of_curr_loans', 'last_delinq_none',\n",
       "       'loan_default'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Fw1Or8iNo7hw",
    "outputId": "b85bb5b4-58e2-46ea-a6af-b8b5b2f26aba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_id               int64\n",
       "age                   int64\n",
       "education           float64\n",
       "proof_submitted      object\n",
       "loan_amount           int64\n",
       "asset_cost            int64\n",
       "no_of_loans           int64\n",
       "no_of_curr_loans      int64\n",
       "last_delinq_none      int64\n",
       "loan_default          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "oFxZ7HPDa6ZT",
    "outputId": "3d9462d0-2758-479f-d69a-be2c71b73887"
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "   loan_id  age  education proof_submitted  loan_amount  asset_cost  \\\n",
       "0        1   27        1.0          Aadhar       504264      820920   \n",
       "1        2   48        1.0          Aadhar       728556      831444   \n",
       "2        3   30        2.0         VoterID       642936      826092   \n",
       "3        4   28        1.0          Aadhar       746556      930924   \n",
       "4        5   29        1.0          Aadhar      1139880     1902000   \n",
       "\n",
       "   no_of_loans  no_of_curr_loans  last_delinq_none  loan_default  \n",
       "0            2                 2                 0             0  \n",
       "1            6                 2                 0             0  \n",
       "2            0                 0                 0             1  \n",
       "3            0                 0                 0             0  \n",
       "4            0                 0                 0             0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sIrsmsy5sJQD",
    "outputId": "1a01ef15-741a-4abc-876a-3029af45f9ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6755"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['education'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kgHNzK3TsJNk",
    "outputId": "4f25f436-c826-4b40-b938-1c24d2054bd3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2., nan])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "id": "Lqd5Ek7osJK4",
    "outputId": "6e9d186e-ba53-4356-f3ca-10a03cec90e7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "ax = sns.barplot(x=\"education\", y=\"age\", data=train_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DxKueSIFsJIK",
    "outputId": "b9621e74-167c-4651-cf3d-51852fc5786f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27, 48, 30, 28, 29, 34, 46, 32, 36, 25, 38, 40, 41, 44, 47, 33, 42,\n",
       "       26, 45, 37, 31, 43, 49, 35, 24, 50, 39, 23, 22, 21])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "bVxogyyjsJFk",
    "outputId": "ad7151ad-8575-4ba9-f5f1-0b15fcaade38"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mgvVKS0slnfsXHrqOJec8Tp48KY/HE7pdWVmpPn36qG/fvkpOTtb27dslSdu3b1dycjKHrAAgylmy59Ha2qoFCxaotbVVTqdTffr0UUlJiRwOh5YtW6aFCxfq+eef1xVXXKGioiIrIgEAzoMl5dGvXz9t2rSpw/tGjBihzZs3WxEDANBF+IY5AMAYEyOGye9t0qnDb9kdw3aBttOfWHN2625zEvv5vU2SEuyOAdiC8ggDU17/R82/p7wePpxfmlICYwOXLMojDEx5/R9MeQ1A4pwHAKATKA8AgDHKAwBgjPIAABijPAAAxigPAIAxygMAYIzyAAAYozwAAMYoDwCAMcoDAGCM8gAAGKM8AADGKA8AgDHKAwBgjPIAABijPAAAxigPAIAxygMAYIzyAAAYozwAAMYoDwCAMcoDAGCM8gAAGKM8AADGulmxkcbGRj366KM6cuSIXC6XhgwZouXLlys+Pl5JSUkaOXKknM7TPbZq1SolJSVZEQsA0EmWlIfD4dC8efN04403SpKKioq0evVqrVy5UpK0ceNG9ezZ04ooAIAuYMlhq7i4uFBxSNJ1112n2tpaKzYNAIgAS/Y8zhQIBPTKK68oIyMjtGzOnDny+/0aN26ccnNz5XK5rI4FADBgeXk88cQT6tGjh+68805J0jvvvCO3262Wlhbl5+eruLhYDz/8cJdvt7q6Wl6vt8vXe6lpbm6WJO3Zs8fmJMDZGJ9dLy0trcPllpZHUVGRDh8+rJKSktAJcrfbLUnq1auXZsyYofXr14e9PpfLpVGjRoW1p5KSktK50GintLRU0rkHFGAnxqd1LPuo7lNPPaWqqioVFxeHftl/9913ob2BtrY27dy5U8nJyWGv0+FwKDY2Vg6HIyKZAQAds2TP4+DBg3rhhRc0dOhQ3XHHHZKkQYMGad68eSooKJDD4VBbW5tSU1O1YMECKyIBAM6DJeVx9dVX6/PPP+/wvrKyMisiAAC6EN8wBwAYcwSDwaDdIRCeyspK7dq1y9YMNTU1kqThw4fbmkOSsrKy2n3kG/aJhrEpRc/4vBTGpuUf1cWFLT4+3u4IwDkxPq3DngcAwBjnPAAAxigPAIAxygMAYIzyAAAYozwAAMYoDwCAMcoDAGCM8gAAGKM8AADGKA8AgDHKAwBgjPIAABijPAAAxigPAIAxygMAYIzyAAAYozwAAMa4DC2MBINB+Xw+u2MA/5XL5ZLD4bA7xkWN8oARn8+nqqoqu2MA/9WoUaMUGxtrd4yLGtcwhxH2PHAhYM8j8igPAIAxTpgDAIxRHgAAY5QHAMAY5QEAMEZ5AACMUR4AAGOUBwDAGOWBDhUVFSkjI0NJSUk6cOBAh4/x+/364x//qAkTJigrK0ubN2+2OCUuRY2NjcrJydHEiRM1depUPfDAA2poaDjrca2trXrooYeUlZWlSZMm6e2337Yh7cWL8kCHMjMztWHDBg0cOPCcjykrK9ORI0f05ptv6tVXX9Wzzz6rb775xsKUuBQ5HA7NmzdPO3fuVFlZmQYPHqzVq1ef9bh169apV69e2rVrl0pKSvT444/r5MmTNiS+OFEe6FB6errcbvd/fUx5eblmzJghp9Op+Ph4TZgwQTt27LAoIS5VcXFxuvHGG0O3r7vuOtXW1p71uDfeeEMzZ86UJA0dOlSjRo3Se++9Z1nOix3lgU7zeDwaMGBA6Lbb7dbRo0dtTIRLTSAQ0CuvvKKMjIyz7qutrW2358z47FqUB4AL1hNPPKEePXrozjvvtDvKJYfyQKe53e52hws8Ho8SExNtTIRLSVFRkQ4fPqynn35aTufZv8oGDBigb7/9NnSb8dm1KA902qRJk7R582YFAgE1NDSooqJCEydOtDsWLgFPPfWUqqqqVFxcLJfL1eFjJk2apFdffVWS9NVXX+mTTz7R2LFjrYx5UWNKdnToT3/6k958800dP35cV155peLi4vT6668rJydHDz74oK655hr5/X4tX75cH3zwgSQpJycndIISiJSDBw9qypQpGjp0qLp37y5JGjRokIqLi5Wdna0XX3xRCQkJOnXqlBYuXKjPPvtMTqdT+fn5mjBhgs3pLx6UBwDAGIetAADGKA8AgDHKAwBgjPIAABijPAAAxigPoJM++ugjjRs3zvLtlpSUaPHixZZvFzhTN7sDADi3jz76SPn5+e0m9Lv33nttTAScxp4HAMAY5QH8SF1dnXJzczVmzBhlZGTo5ZdfliR5vV4tXLhQ119/vX7zm9/ok08+afe8pKQkHT58OHR74cKF+stf/hK6XVFRoezsbI0ePVoTJkwI7U289tprmjx5slJTU5WZmamNGzdKkk6dOqWcnBzV19crNTVVqampqqur07PPPqu8vLzQet966y3dcsstSk9P15w5c3To0KHQfRkZGVq3bp2mTp2qtLQ0PfTQQ/r++++7/kXDJYfDVsAZAoGA7rvvPmVkZOjPf/6z6urqdNddd2nYsGH66KOPdOTIEe3atUutra3KyckJe7379+/XH/7wBz3zzDP6xS9+oWPHjqmlpUWS1LdvX73wwgsaPHiwdu/erZycHF1zzTVKSUnRSy+9dNZhqzN9+eWX+v3vf6/i4mLdcMMN+tvf/qZ7771Xr7/+emjOpzfeeENr165VbGysZs2apS1btmjWrFnn/2LhksaeB3CGTz75RA0NDXrggQfkcrk0ePBg/e53v1N5ebneeOMN3XvvvYqLi5Pb7dacOXPCXm9paal++9vf6qabbpLT6VRCQoJGjBghSfrVr36ln/zkJ3I4HLrhhht000036eOPPw5rveXl5Ro/frxuuukmXXbZZbrnnnvk9Xq1d+/e0GPmzJmjhIQExcXF6eabb9Znn31m9qIAHWDPAzjDt99+q/r6eqWnp4eW+f1+paenq76+vt3VFc+8ENb/4vF4NH78+A7ve/fdd1VcXKyvvvpKgUBAXq9XI0eODGu99fX17XI4nU653W7V1dWFll111VWhny+//HLV19eHnRs4F8oDOIPb7dagQYP05ptvnnVfRkaGPB6Prr76akmnC+FMl19+uVpbW0O3jx07poSEhNB6jxw5ctY6fT6fHnzwQRUVFSkzM1OXXXaZ7r//fv0wX6nD4fivefv3768DBw6EbgeDQXk8ntB2gUjhsBVwhmuvvVY9e/bUiy++KK/XK7/frwMHDmj//v2aPHmyXnzxRX333Xc6evSo/v73v7d77s9+9jNt375dfr9f7733nnbv3h26b/r06dqyZYs+/PBDBQIB1dXV6dChQ/L5fPL5fIqPj1e3bt307rvvhqa4l06fD2lqalJzc3OHeSdPnqx3331XH374of71r3/pr3/9q1wul1JTUyPzAgH/RnkAZ4iJiVFJSYn++c9/KjMzU2PGjNHjjz+ulpYWPfDAAxowYIAyMzN19913Kzs7u91zFy9erLffflvp6ekqKytrd+2Ia6+9VoWFhVq5cqXS0tJ05513qra2Vr169dLjjz+uhx56SNdff722b9/e7nrcI0aM0C233KIJEyYoPT293eEoSRo+fLiefPJJPfHEExozZozefvttlZSUnPMCSUBX4XoeAABj7HkAAIxRHgAAY5QHAMAY5QEAMEZ5AACMUR4AAGOUBwDAGOUBADBGeQAAjP0/ijsIP+b/awsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"education\", y=\"age\", data=train_df)\n",
    "sns.despine(offset=10, trim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "0IokrZsPsJDJ",
    "outputId": "e841054e-36fe-4158-e857-1a1e489b3634"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-a5096810-bbd9-4359-af4d-69f4678ccd29\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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></th>\n",
       "      <th>loan_id</th>\n",
       "      <th>proof_submitted</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>asset_cost</th>\n",
       "      <th>no_of_loans</th>\n",
       "      <th>no_of_curr_loans</th>\n",
       "      <th>last_delinq_none</th>\n",
       "      <th>loan_default</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"27\" valign=\"top\">1.0</th>\n",
       "      <th>24</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"27\" valign=\"top\">2.0</th>\n",
       "      <th>24</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>142</td>\n",
       "      <td>142</td>\n",
       "      <td>142</td>\n",
       "      <td>142</td>\n",
       "      <td>142</td>\n",
       "      <td>142</td>\n",
       "      <td>142</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>152</td>\n",
       "      <td>152</td>\n",
       "      <td>152</td>\n",
       "      <td>152</td>\n",
       "      <td>152</td>\n",
       "      <td>152</td>\n",
       "      <td>152</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>125</td>\n",
       "      <td>125</td>\n",
       "      <td>125</td>\n",
       "      <td>125</td>\n",
       "      <td>125</td>\n",
       "      <td>125</td>\n",
       "      <td>125</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5096810-bbd9-4359-af4d-69f4678ccd29')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
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       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "          document.querySelector('#df-a5096810-bbd9-4359-af4d-69f4678ccd29 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "          const element = document.querySelector('#df-a5096810-bbd9-4359-af4d-69f4678ccd29');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "               loan_id  proof_submitted  loan_amount  asset_cost  no_of_loans  \\\n",
       "education age                                                                   \n",
       "1.0       24        28               28           28          28           28   \n",
       "          25       145              145          145         145          145   \n",
       "          26       176              176          176         176          176   \n",
       "          27       167              167          167         167          167   \n",
       "          28       163              163          163         163          163   \n",
       "          29       162              162          162         162          162   \n",
       "          30       128              128          128         128          128   \n",
       "          31       122              122          122         122          122   \n",
       "          32       132              132          132         132          132   \n",
       "          33       127              127          127         127          127   \n",
       "          34       120              120          120         120          120   \n",
       "          35       115              115          115         115          115   \n",
       "          36        99               99           99          99           99   \n",
       "          37       107              107          107         107          107   \n",
       "          38       104              104          104         104          104   \n",
       "          39       104              104          104         104          104   \n",
       "          40        84               84           84          84           84   \n",
       "          41        93               93           93          93           93   \n",
       "          42        87               87           87          87           87   \n",
       "          43        79               79           79          79           79   \n",
       "          44        94               94           94          94           94   \n",
       "          45       104              104          104         104          104   \n",
       "          46       103              103          103         103          103   \n",
       "          47       109              109          109         109          109   \n",
       "          48       108              108          108         108          108   \n",
       "          49        68               68           68          68           68   \n",
       "          50        31               31           31          31           31   \n",
       "2.0       24        33               33           33          33           33   \n",
       "          25       111              111          111         111          111   \n",
       "          26       142              142          142         142          142   \n",
       "          27       128              128          128         128          128   \n",
       "          28       172              172          172         172          172   \n",
       "          29       160              160          160         160          160   \n",
       "          30       152              152          152         152          152   \n",
       "          31       154              154          154         154          154   \n",
       "          32       151              151          151         151          151   \n",
       "          33       153              153          153         153          153   \n",
       "          34       156              156          156         156          156   \n",
       "          35       153              153          153         153          153   \n",
       "          36       163              163          163         163          163   \n",
       "          37       168              168          168         168          168   \n",
       "          38       139              139          139         139          139   \n",
       "          39       125              125          125         125          125   \n",
       "          40       154              154          154         154          154   \n",
       "          41       135              135          135         135          135   \n",
       "          42       158              158          158         158          158   \n",
       "          43       139              139          139         139          139   \n",
       "          44       143              143          143         143          143   \n",
       "          45       174              174          174         174          174   \n",
       "          46       150              150          150         150          150   \n",
       "          47       179              179          179         179          179   \n",
       "          48       139              139          139         139          139   \n",
       "          49       111              111          111         111          111   \n",
       "          50        54               54           54          54           54   \n",
       "\n",
       "               no_of_curr_loans  last_delinq_none  loan_default  \n",
       "education age                                                    \n",
       "1.0       24                 28                28            28  \n",
       "          25                145               145           145  \n",
       "          26                176               176           176  \n",
       "          27                167               167           167  \n",
       "          28                163               163           163  \n",
       "          29                162               162           162  \n",
       "          30                128               128           128  \n",
       "          31                122               122           122  \n",
       "          32                132               132           132  \n",
       "          33                127               127           127  \n",
       "          34                120               120           120  \n",
       "          35                115               115           115  \n",
       "          36                 99                99            99  \n",
       "          37                107               107           107  \n",
       "          38                104               104           104  \n",
       "          39                104               104           104  \n",
       "          40                 84                84            84  \n",
       "          41                 93                93            93  \n",
       "          42                 87                87            87  \n",
       "          43                 79                79            79  \n",
       "          44                 94                94            94  \n",
       "          45                104               104           104  \n",
       "          46                103               103           103  \n",
       "          47                109               109           109  \n",
       "          48                108               108           108  \n",
       "          49                 68                68            68  \n",
       "          50                 31                31            31  \n",
       "2.0       24                 33                33            33  \n",
       "          25                111               111           111  \n",
       "          26                142               142           142  \n",
       "          27                128               128           128  \n",
       "          28                172               172           172  \n",
       "          29                160               160           160  \n",
       "          30                152               152           152  \n",
       "          31                154               154           154  \n",
       "          32                151               151           151  \n",
       "          33                153               153           153  \n",
       "          34                156               156           156  \n",
       "          35                153               153           153  \n",
       "          36                163               163           163  \n",
       "          37                168               168           168  \n",
       "          38                139               139           139  \n",
       "          39                125               125           125  \n",
       "          40                154               154           154  \n",
       "          41                135               135           135  \n",
       "          42                158               158           158  \n",
       "          43                139               139           139  \n",
       "          44                143               143           143  \n",
       "          45                174               174           174  \n",
       "          46                150               150           150  \n",
       "          47                179               179           179  \n",
       "          48                139               139           139  \n",
       "          49                111               111           111  \n",
       "          50                 54                54            54  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.groupby( [ \"education\", \"age\"] ).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "plJ1-f9osJAO",
    "outputId": "79418879-7582-45ee-94d9-3abe4f868151"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_id</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>asset_cost</th>\n",
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       "      <th>loan_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>6755.000000</td>\n",
       "      <td>7.000000e+03</td>\n",
       "      <td>7.000000e+03</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3500.500000</td>\n",
       "      <td>36.096571</td>\n",
       "      <td>1.561954</td>\n",
       "      <td>6.633552e+05</td>\n",
       "      <td>9.162998e+05</td>\n",
       "      <td>2.853286</td>\n",
       "      <td>1.371143</td>\n",
       "      <td>0.013286</td>\n",
       "      <td>0.400000</td>\n",
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       "      <th>std</th>\n",
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       "      <td>7.587700</td>\n",
       "      <td>0.496184</td>\n",
       "      <td>1.498128e+05</td>\n",
       "      <td>2.144922e+05</td>\n",
       "      <td>5.471932</td>\n",
       "      <td>2.189278</td>\n",
       "      <td>0.114504</td>\n",
       "      <td>0.489933</td>\n",
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       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.678800e+05</td>\n",
       "      <td>4.733520e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1750.750000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.777880e+05</td>\n",
       "      <td>7.979010e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3500.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.571080e+05</td>\n",
       "      <td>8.584260e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5250.250000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.373640e+05</td>\n",
       "      <td>9.576750e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7000.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.781376e+06</td>\n",
       "      <td>2.419200e+06</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "           loan_id          age    education   loan_amount    asset_cost  \\\n",
       "count  7000.000000  7000.000000  6755.000000  7.000000e+03  7.000000e+03   \n",
       "mean   3500.500000    36.096571     1.561954  6.633552e+05  9.162998e+05   \n",
       "std    2020.870275     7.587700     0.496184  1.498128e+05  2.144922e+05   \n",
       "min       1.000000    21.000000     1.000000  1.678800e+05  4.733520e+05   \n",
       "25%    1750.750000    29.000000     1.000000  5.777880e+05  7.979010e+05   \n",
       "50%    3500.500000    36.000000     2.000000  6.571080e+05  8.584260e+05   \n",
       "75%    5250.250000    43.000000     2.000000  7.373640e+05  9.576750e+05   \n",
       "max    7000.000000    50.000000     2.000000  1.781376e+06  2.419200e+06   \n",
       "\n",
       "       no_of_loans  no_of_curr_loans  last_delinq_none  loan_default  \n",
       "count  7000.000000       7000.000000       7000.000000   7000.000000  \n",
       "mean      2.853286          1.371143          0.013286      0.400000  \n",
       "std       5.471932          2.189278          0.114504      0.489933  \n",
       "min       0.000000          0.000000          0.000000      0.000000  \n",
       "25%       0.000000          0.000000          0.000000      0.000000  \n",
       "50%       0.000000          0.000000          0.000000      0.000000  \n",
       "75%       3.000000          2.000000          0.000000      1.000000  \n",
       "max     109.000000         33.000000          1.000000      1.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ebxvijaPF9QA"
   },
   "source": [
    "Feature imputing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cY1Mx_5FsI9k",
    "outputId": "c0e8c5d6-80fc-43c1-947a-73afb384909b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find mode to fill nan values\n",
    "mode_val=train_df['education'].mode()[0]\n",
    "mode_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "id": "BZrjxDHPUm2a"
   },
   "outputs": [],
   "source": [
    "train_rf=train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qSUMdwwnUmwh"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XwTJnEMWUmps"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "--9YNhjfUmgf"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "id": "24yS5pYwsI6a"
   },
   "outputs": [],
   "source": [
    "#crete new df to clean data\n",
    "train_dfclean=train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "id": "stNwPfwcsI2v"
   },
   "outputs": [],
   "source": [
    "#fill all nan values by mode\n",
    "train_dfclean['education']=train_df['education'].fillna(value=mode_val)\n",
    "train_rf['education']=train_df['education'].fillna(value=mode_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d0ECDLMXF60F",
    "outputId": "6d267c08-3d40-40b1-c02b-19916265b4e9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2.])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check \n",
    "train_dfclean['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ne5rxmIC6roI",
    "outputId": "61530c77-6bed-42e5-e624-ac835242f552"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_id               int64\n",
       "age                   int64\n",
       "education           float64\n",
       "proof_submitted      object\n",
       "loan_amount           int64\n",
       "asset_cost            int64\n",
       "no_of_loans           int64\n",
       "no_of_curr_loans      int64\n",
       "last_delinq_none      int64\n",
       "loan_default          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check datatypes\n",
    "train_dfclean.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "id": "vyHTuhiV6rmC"
   },
   "outputs": [],
   "source": [
    "#change type of education\n",
    "train_dfclean['education'] = train_dfclean['education'].astype(int)\n",
    "train_rf['education']=train_rf['education'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OBxVOlD36rei",
    "outputId": "ecb6bb7c-7fb2-47f9-c6be-686ac531f796"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Aadhar', 'VoterID', 'Driving', 'PAN', 'Passport'], dtype=object)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check unique values of 'proof_submitted'\n",
    "train_dfclean['proof_submitted'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "id": "Wa5hZr2I6rb4"
   },
   "outputs": [],
   "source": [
    "# Get one hot encoding of columns\n",
    "one_hot = pd.get_dummies(train_dfclean['proof_submitted'])\n",
    "train_dfclean = train_dfclean.drop('proof_submitted',axis = 1)\n",
    "# Join the encoded df\n",
    "train_dfclean = train_dfclean.join(one_hot)\n",
    "\n",
    "\n",
    "# Get one hot encoding of columns\n",
    "one_hot = pd.get_dummies(train_rf['proof_submitted'])\n",
    "train_rf = train_rf.drop('proof_submitted',axis = 1)\n",
    "# Join the encoded df\n",
    "train_rf = train_rf.join(one_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FFJ2yt046rSZ",
    "outputId": "163dc5fa-13dc-441b-ff23-7421fc6a825f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_id               int64\n",
       "age                   int64\n",
       "education           float64\n",
       "proof_submitted      object\n",
       "loan_amount           int64\n",
       "asset_cost            int64\n",
       "no_of_loans           int64\n",
       "no_of_curr_loans      int64\n",
       "last_delinq_none      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check types in test df\n",
    "test_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FXMolqu36rKY",
    "outputId": "32c0dc32-0122-4451-9361-f44176965bc2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Aadhar', 'VoterID', 'Driving', 'PAN', 'Passport'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for test df\n",
    "test_df['proof_submitted'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "id": "tgeOx60XAcml"
   },
   "outputs": [],
   "source": [
    "#create new test for cleaning data\n",
    "test_dfclean=test_df\n",
    "test_rf=test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "id": "ex8CR_Z6AcjM"
   },
   "outputs": [],
   "source": [
    "#check mode for education in test df\n",
    "mode_val=test_df['education'].mode()[0]\n",
    "mode_val\n",
    "\n",
    "test_dfclean['education']=test_df['education'].fillna(value=mode_val)\n",
    "test_rf['education']=test_df['education'].fillna(value=mode_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "id": "xPAxPzSm-5uY"
   },
   "outputs": [],
   "source": [
    "#change the type of education\n",
    "test_dfclean['education'] = test_dfclean['education'].astype(int)\n",
    "test_rf['education'] = test_rf['education'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "id": "f3IGALBVBzYi"
   },
   "outputs": [],
   "source": [
    "# Get one hot encoding of columns\n",
    "one_hot = pd.get_dummies(test_dfclean['proof_submitted'])\n",
    "test_dfclean = test_dfclean.drop('proof_submitted',axis = 1)\n",
    "# Join the encoded df\n",
    "test_dfclean = test_dfclean.join(one_hot)\n",
    "\n",
    "\n",
    "# Get one hot encoding of columns\n",
    "one_hot = pd.get_dummies(test_rf['proof_submitted'])\n",
    "test_rf = test_rf.drop('proof_submitted',axis = 1)\n",
    "# Join the encoded df\n",
    "test_rf = test_rf.join(one_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "roxKUzdYCdHB",
    "outputId": "05323426-f3e5-474c-e79e-050f67ba6fe2"
   },
   "outputs": [
    {
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      "text/plain": [
       "   loan_id  age  education  loan_amount  asset_cost  no_of_loans  \\\n",
       "0     7001   29          1       636936      768240            2   \n",
       "1     7002   28          1       548988      693060            3   \n",
       "2     7003   28          1       651756      936600            0   \n",
       "3     7004   45          2       614676      744840            4   \n",
       "4     7005   48          1       625236      839400            0   \n",
       "\n",
       "   no_of_curr_loans  last_delinq_none  Aadhar  Driving  PAN  Passport  VoterID  \n",
       "0                 2                 0       1        0    0         0        0  \n",
       "1                 3                 0       1        0    0         0        0  \n",
       "2                 0                 0       1        0    0         0        0  \n",
       "3                 3                 0       1        0    0         0        0  \n",
       "4                 0                 0       1        0    0         0        0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for new test df\n",
    "test_dfclean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0gWwytxduJhu",
    "outputId": "4efb04e1-63c0-44bc-f1ed-14ed870ceb09"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167880"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for min in 'loan_amount'\n",
    "train_dfclean['loan_amount'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V-xgFMSLuI3Y",
    "outputId": "0bb5449e-9566-487c-a1fe-319273aee9ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1781376"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for max in 'loan_amount'\n",
    "train_dfclean['loan_amount'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XtZixfB5vNIG",
    "outputId": "9219de13-9515-435b-cb82-b63d298f1b24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "473352"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dfclean['asset_cost'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "id": "NN5XlHO3GFEK"
   },
   "outputs": [],
   "source": [
    "#normalize columns\n",
    "num_cols=['loan_amount', 'asset_cost']\n",
    "for i in num_cols:\n",
    "    train_dfclean[i] = np.log(train_dfclean[i])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "id": "U1Vq7etKyb_6"
   },
   "outputs": [],
   "source": [
    "# numerical and categorical columns\n",
    "num_cols=['age','loan_amount', 'asset_cost', 'no_of_loans', 'no_of_curr_loans']\n",
    "cat_cols=['last_delinq_none', 'education']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "id": "fNHemRk6yb30"
   },
   "outputs": [],
   "source": [
    "#drop id \n",
    "train_dfclean=train_dfclean.drop('loan_id',axis=1) \n",
    "test_dfclean=test_dfclean.drop('loan_id',axis=1)\n",
    "\n",
    "train_rf=train_rf.drop('loan_id',axis=1) \n",
    "test_rf=test_rf.drop('loan_id',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "id": "O67RzgWNybvQ"
   },
   "outputs": [],
   "source": [
    "#scaling numerical features\n",
    "train_dfclean[num_cols] = MinMaxScaler().fit_transform(train_dfclean[num_cols])\n",
    "test_dfclean[num_cols] = MinMaxScaler().fit_transform(test_dfclean[num_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "id": "Tu43fPHjHVJM"
   },
   "outputs": [],
   "source": [
    "#encoding 'education' column \n",
    "le = LabelEncoder()\n",
    "train_dfclean['education'] = le.fit_transform(train_dfclean['education'])\n",
    "train_rf['education'] = le.fit_transform(train_rf['education'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "id": "MQg3cKr1EwbN"
   },
   "outputs": [],
   "source": [
    "#encoding 'education' column \n",
    "le = LabelEncoder()\n",
    "test_dfclean['education'] = le.fit_transform(test_dfclean['education'])\n",
    "test_rf['education'] = le.fit_transform(test_rf['education'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "33CGMFFTauUm",
    "outputId": "2cb748a1-8e9e-48aa-ff78-4e56f0adffe7"
   },
   "outputs": [
    {
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       "      <th>Passport</th>\n",
       "      <th>VoterID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>504264</td>\n",
       "      <td>820920</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>728556</td>\n",
       "      <td>831444</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>642936</td>\n",
       "      <td>826092</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>746556</td>\n",
       "      <td>930924</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1139880</td>\n",
       "      <td>1902000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8b1a2d3-cdf7-49b1-a5f6-51784ac29113')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-d8b1a2d3-cdf7-49b1-a5f6-51784ac29113 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-d8b1a2d3-cdf7-49b1-a5f6-51784ac29113');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   age  education  loan_amount  asset_cost  no_of_loans  no_of_curr_loans  \\\n",
       "0   27          0       504264      820920            2                 2   \n",
       "1   48          0       728556      831444            6                 2   \n",
       "2   30          1       642936      826092            0                 0   \n",
       "3   28          0       746556      930924            0                 0   \n",
       "4   29          0      1139880     1902000            0                 0   \n",
       "\n",
       "   last_delinq_none  loan_default  Aadhar  Driving  PAN  Passport  VoterID  \n",
       "0                 0             0       1        0    0         0        0  \n",
       "1                 0             0       1        0    0         0        0  \n",
       "2                 0             1       0        0    0         0        1  \n",
       "3                 0             0       1        0    0         0        0  \n",
       "4                 0             0       1        0    0         0        0  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_rf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z8eaOQsxH5ul"
   },
   "source": [
    "Modelling. Drop target column from train df. I use random Forest Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "id": "1xrMINgQE7ex"
   },
   "outputs": [],
   "source": [
    "# split for independent features and target\n",
    "y=train_dfclean['loan_default']\n",
    "x=train_dfclean.drop('loan_default', axis=1)\n",
    "\n",
    "y_rf=train_rf['loan_default']\n",
    "x_rf=train_rf.drop('loan_default', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "id": "MQUrOjO9FZv9"
   },
   "outputs": [],
   "source": [
    "#train test split\n",
    "x_train, x_val, y_train, y_val=train_test_split(x, y, test_size=0.3,random_state=11, stratify = y)\n",
    "\n",
    "#train test split\n",
    "xrf_train, xrf_val, yrf_train, yrf_val=train_test_split(x_rf, y_rf, test_size=0.3,random_state=11, stratify = y_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6Oi9H7nFF6S8",
    "outputId": "fadc1914-6ac2-44f4-9f8e-33b6e4835deb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4900, 12)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape \n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Cpt2o7FPGBrl",
    "outputId": "902ef6a7-c45e-4f54-f1ad-557b031af24b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2100, 12)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape\n",
    "x_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CpJNoTpybdLy",
    "outputId": "41522319-2568-404d-c487-c74f6eaeeadc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 18 candidates, totalling 90 fits\n",
      "[CV 1/5] END max_depth=2, n_estimators=100, random_state=50;, score=0.600 total time=   0.2s\n",
      "[CV 2/5] END max_depth=2, n_estimators=100, random_state=50;, score=0.600 total time=   0.3s\n",
      "[CV 3/5] END max_depth=2, n_estimators=100, random_state=50;, score=0.600 total time=   0.2s\n",
      "[CV 4/5] END max_depth=2, n_estimators=100, random_state=50;, score=0.600 total time=   0.2s\n",
      "[CV 5/5] END max_depth=2, n_estimators=100, random_state=50;, score=0.600 total time=   0.2s\n",
      "[CV 1/5] END max_depth=2, n_estimators=250, random_state=50;, score=0.599 total time=   0.6s\n",
      "[CV 2/5] END max_depth=2, n_estimators=250, random_state=50;, score=0.600 total time=   0.6s\n",
      "[CV 3/5] END max_depth=2, n_estimators=250, random_state=50;, score=0.600 total time=   0.6s\n",
      "[CV 4/5] END max_depth=2, n_estimators=250, random_state=50;, score=0.600 total time=   0.6s\n",
      "[CV 5/5] END max_depth=2, n_estimators=250, random_state=50;, score=0.600 total time=   0.6s\n",
      "[CV 1/5] END max_depth=2, n_estimators=1000, random_state=50;, score=0.601 total time=   2.4s\n",
      "[CV 2/5] END max_depth=2, n_estimators=1000, random_state=50;, score=0.600 total time=   2.3s\n",
      "[CV 3/5] END max_depth=2, n_estimators=1000, random_state=50;, score=0.600 total time=   2.3s\n",
      "[CV 4/5] END max_depth=2, n_estimators=1000, random_state=50;, score=0.600 total time=   2.3s\n",
      "[CV 5/5] END max_depth=2, n_estimators=1000, random_state=50;, score=0.600 total time=   2.3s\n",
      "[CV 1/5] END max_depth=4, n_estimators=100, random_state=50;, score=0.598 total time=   0.3s\n",
      "[CV 2/5] END max_depth=4, n_estimators=100, random_state=50;, score=0.598 total time=   0.3s\n",
      "[CV 3/5] END max_depth=4, n_estimators=100, random_state=50;, score=0.605 total time=   0.3s\n",
      "[CV 4/5] END max_depth=4, n_estimators=100, random_state=50;, score=0.600 total time=   0.3s\n",
      "[CV 5/5] END max_depth=4, n_estimators=100, random_state=50;, score=0.601 total time=   0.3s\n",
      "[CV 1/5] END max_depth=4, n_estimators=250, random_state=50;, score=0.599 total time=   0.7s\n",
      "[CV 2/5] END max_depth=4, n_estimators=250, random_state=50;, score=0.598 total time=   0.7s\n",
      "[CV 3/5] END max_depth=4, n_estimators=250, random_state=50;, score=0.608 total time=   0.7s\n",
      "[CV 4/5] END max_depth=4, n_estimators=250, random_state=50;, score=0.604 total time=   0.7s\n",
      "[CV 5/5] END max_depth=4, n_estimators=250, random_state=50;, score=0.606 total time=   0.7s\n",
      "[CV 1/5] END max_depth=4, n_estimators=1000, random_state=50;, score=0.599 total time=   2.8s\n",
      "[CV 2/5] END max_depth=4, n_estimators=1000, random_state=50;, score=0.598 total time=   2.8s\n",
      "[CV 3/5] END max_depth=4, n_estimators=1000, random_state=50;, score=0.607 total time=   2.8s\n",
      "[CV 4/5] END max_depth=4, n_estimators=1000, random_state=50;, score=0.602 total time=   2.8s\n",
      "[CV 5/5] END max_depth=4, n_estimators=1000, random_state=50;, score=0.605 total time=   2.8s\n",
      "[CV 1/5] END max_depth=8, n_estimators=100, random_state=50;, score=0.599 total time=   0.4s\n",
      "[CV 2/5] END max_depth=8, n_estimators=100, random_state=50;, score=0.602 total time=   0.4s\n",
      "[CV 3/5] END max_depth=8, n_estimators=100, random_state=50;, score=0.602 total time=   0.4s\n",
      "[CV 4/5] END max_depth=8, n_estimators=100, random_state=50;, score=0.586 total time=   0.4s\n",
      "[CV 5/5] END max_depth=8, n_estimators=100, random_state=50;, score=0.602 total time=   0.4s\n",
      "[CV 1/5] END max_depth=8, n_estimators=250, random_state=50;, score=0.602 total time=   1.0s\n",
      "[CV 2/5] END max_depth=8, n_estimators=250, random_state=50;, score=0.598 total time=   1.0s\n",
      "[CV 3/5] END max_depth=8, n_estimators=250, random_state=50;, score=0.607 total time=   1.0s\n",
      "[CV 4/5] END max_depth=8, n_estimators=250, random_state=50;, score=0.600 total time=   0.9s\n",
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      "[CV 1/5] END max_depth=8, n_estimators=1000, random_state=50;, score=0.606 total time=   3.8s\n",
      "[CV 2/5] END max_depth=8, n_estimators=1000, random_state=50;, score=0.598 total time=   3.8s\n",
      "[CV 3/5] END max_depth=8, n_estimators=1000, random_state=50;, score=0.611 total time=   3.8s\n",
      "[CV 4/5] END max_depth=8, n_estimators=1000, random_state=50;, score=0.603 total time=   3.8s\n",
      "[CV 5/5] END max_depth=8, n_estimators=1000, random_state=50;, score=0.604 total time=   3.8s\n",
      "[CV 1/5] END max_depth=16, n_estimators=100, random_state=50;, score=0.595 total time=   0.6s\n",
      "[CV 2/5] END max_depth=16, n_estimators=100, random_state=50;, score=0.586 total time=   0.6s\n",
      "[CV 3/5] END max_depth=16, n_estimators=100, random_state=50;, score=0.581 total time=   0.6s\n",
      "[CV 4/5] END max_depth=16, n_estimators=100, random_state=50;, score=0.567 total time=   0.6s\n",
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      "[CV 1/5] END max_depth=16, n_estimators=250, random_state=50;, score=0.604 total time=   1.4s\n",
      "[CV 2/5] END max_depth=16, n_estimators=250, random_state=50;, score=0.585 total time=   1.4s\n",
      "[CV 3/5] END max_depth=16, n_estimators=250, random_state=50;, score=0.587 total time=   1.4s\n",
      "[CV 4/5] END max_depth=16, n_estimators=250, random_state=50;, score=0.577 total time=   1.4s\n",
      "[CV 5/5] END max_depth=16, n_estimators=250, random_state=50;, score=0.572 total time=   1.4s\n",
      "[CV 1/5] END max_depth=16, n_estimators=1000, random_state=50;, score=0.589 total time=   5.7s\n",
      "[CV 2/5] END max_depth=16, n_estimators=1000, random_state=50;, score=0.594 total time=   5.6s\n",
      "[CV 3/5] END max_depth=16, n_estimators=1000, random_state=50;, score=0.589 total time=   5.6s\n",
      "[CV 4/5] END max_depth=16, n_estimators=1000, random_state=50;, score=0.578 total time=   5.6s\n",
      "[CV 5/5] END max_depth=16, n_estimators=1000, random_state=50;, score=0.566 total time=   5.6s\n",
      "[CV 1/5] END max_depth=32, n_estimators=100, random_state=50;, score=0.598 total time=   0.6s\n",
      "[CV 2/5] END max_depth=32, n_estimators=100, random_state=50;, score=0.589 total time=   0.7s\n",
      "[CV 3/5] END max_depth=32, n_estimators=100, random_state=50;, score=0.583 total time=   0.7s\n",
      "[CV 4/5] END max_depth=32, n_estimators=100, random_state=50;, score=0.560 total time=   0.6s\n",
      "[CV 5/5] END max_depth=32, n_estimators=100, random_state=50;, score=0.563 total time=   0.6s\n",
      "[CV 1/5] END max_depth=32, n_estimators=250, random_state=50;, score=0.590 total time=   1.6s\n",
      "[CV 2/5] END max_depth=32, n_estimators=250, random_state=50;, score=0.598 total time=   1.6s\n",
      "[CV 3/5] END max_depth=32, n_estimators=250, random_state=50;, score=0.583 total time=   1.6s\n",
      "[CV 4/5] END max_depth=32, n_estimators=250, random_state=50;, score=0.557 total time=   1.6s\n",
      "[CV 5/5] END max_depth=32, n_estimators=250, random_state=50;, score=0.553 total time=   1.6s\n",
      "[CV 1/5] END max_depth=32, n_estimators=1000, random_state=50;, score=0.585 total time=   6.3s\n",
      "[CV 2/5] END max_depth=32, n_estimators=1000, random_state=50;, score=0.590 total time=   6.3s\n",
      "[CV 3/5] END max_depth=32, n_estimators=1000, random_state=50;, score=0.572 total time=   6.4s\n",
      "[CV 4/5] END max_depth=32, n_estimators=1000, random_state=50;, score=0.563 total time=   6.5s\n",
      "[CV 5/5] END max_depth=32, n_estimators=1000, random_state=50;, score=0.560 total time=   6.3s\n",
      "[CV 1/5] END max_depth=None, n_estimators=100, random_state=50;, score=0.587 total time=   0.7s\n",
      "[CV 2/5] END max_depth=None, n_estimators=100, random_state=50;, score=0.585 total time=   0.6s\n",
      "[CV 3/5] END max_depth=None, n_estimators=100, random_state=50;, score=0.580 total time=   0.7s\n",
      "[CV 4/5] END max_depth=None, n_estimators=100, random_state=50;, score=0.551 total time=   0.7s\n",
      "[CV 5/5] END max_depth=None, n_estimators=100, random_state=50;, score=0.567 total time=   0.7s\n",
      "[CV 1/5] END max_depth=None, n_estimators=250, random_state=50;, score=0.583 total time=   1.6s\n",
      "[CV 2/5] END max_depth=None, n_estimators=250, random_state=50;, score=0.591 total time=   1.6s\n",
      "[CV 3/5] END max_depth=None, n_estimators=250, random_state=50;, score=0.578 total time=   1.6s\n",
      "[CV 4/5] END max_depth=None, n_estimators=250, random_state=50;, score=0.558 total time=   1.8s\n",
      "[CV 5/5] END max_depth=None, n_estimators=250, random_state=50;, score=0.551 total time=   1.6s\n",
      "[CV 1/5] END max_depth=None, n_estimators=1000, random_state=50;, score=0.585 total time=   6.5s\n",
      "[CV 2/5] END max_depth=None, n_estimators=1000, random_state=50;, score=0.587 total time=   6.5s\n",
      "[CV 3/5] END max_depth=None, n_estimators=1000, random_state=50;, score=0.568 total time=   6.4s\n",
      "[CV 4/5] END max_depth=None, n_estimators=1000, random_state=50;, score=0.563 total time=   6.4s\n",
      "[CV 5/5] END max_depth=None, n_estimators=1000, random_state=50;, score=0.557 total time=   6.3s\n",
      "Mean accuracy: %.3f 0.6044897959183674\n",
      "config: %s {'max_depth': 8, 'n_estimators': 1000, 'random_state': 50}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "rf_paramgrid={\n",
    "    \"n_estimators\":[100,250,1000],\n",
    "    \"max_depth\":[2,4,8,16,32,None],\n",
    "    'random_state':[50]\n",
    "}\n",
    "\n",
    "rf_search=GridSearchCV(RandomForestClassifier(), rf_paramgrid, refit=True, verbose=3, cv=5)\n",
    "rf_search.fit(xrf_train, yrf_train)\n",
    "rf_search.best_params_\n",
    "print('Mean accuracy: %.3f', rf_search.best_score_)\n",
    "print(\"config: %s\",  rf_search.best_params_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DYAUEO-IbdCn",
    "outputId": "24d210d5-2ff4-4378-fa64-96ffef4f63d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4553872558443765\n"
     ]
    }
   ],
   "source": [
    "yrf_pred=rf_search.predict(xrf_val)\n",
    "f1_reg=f1_score(yrf_val, yrf_pred, average='macro')\n",
    "print(f1_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4fS__lvcWuQN",
    "outputId": "2d75b200-77c2-48ed-8b93-166767185cb9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9997874330321866\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "RF_reg = RandomForestClassifier()\n",
    "\n",
    "RF_reg.fit(xrf_train, yrf_train)\n",
    "yrf_pred=RF_reg.predict(xrf_train)\n",
    "\n",
    "f1_reg=f1_score(yrf_train, yrf_pred, average='macro')\n",
    "print(f1_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8EbTL6b3W7bG",
    "outputId": "e4e61cf0-526c-44e9-b2c3-84a67919b1f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5409483428525796\n"
     ]
    }
   ],
   "source": [
    "yrf_pred = RF_reg.predict(xrf_val)\n",
    "\n",
    "# Calculating F1 score of prediction on val data\n",
    "f1_reg = f1_score(yrf_val, yrf_pred, average='macro')\n",
    "print(f1_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "id": "WYYPiNOeWuOA"
   },
   "outputs": [],
   "source": [
    "#prediction for test df\n",
    "pred_test = RF_reg.predict(test_rf)\n",
    "submission=pd.read_csv(\"sample_submission_tbPU9qQ.csv\")\n",
    "#fill submission file\n",
    "submission['loan_default']=pred_test \n",
    "submission['loan_id']=test_df['loan_id']\n",
    "#create file with answers\n",
    "pd.DataFrame(submission, columns=['loan_id','loan_default']).to_csv('randomforestfinal3_classifier.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1S1gI4BcYHjc"
   },
   "source": [
    "# Новый раздел"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "U_K9WyoTWuLk"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "agWnepkzWuIZ"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9S2VG1_uWuCU"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JfRw0Si_ISVo",
    "outputId": "4c3027ee-6103-4c76-b13c-03daf084171b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9997874330321866\n"
     ]
    }
   ],
   "source": [
    "#random forest\n",
    "#create classifier\n",
    "#fit classifier\n",
    "#check macro f1 score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "RF_reg = RandomForestClassifier()\n",
    "\n",
    "RF_reg.fit(x_train, y_train)\n",
    "y_pred=RF_reg.predict(x_train)\n",
    "\n",
    "f1_reg=f1_score(y_train, y_pred, average='macro')\n",
    "print(f1_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m6h-6G-lR7cu",
    "outputId": "5b1b7cad-1512-49fa-8442-9b64a8df0a9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5245649094816358\n"
     ]
    }
   ],
   "source": [
    "y_pred = RF_reg.predict(x_val)\n",
    "\n",
    "# Calculating F1 score of prediction on val data\n",
    "f1_reg = f1_score(y_val, y_pred, average='macro')\n",
    "print(f1_reg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "id": "pXhCHOm-d-zm"
   },
   "outputs": [],
   "source": [
    "#prediction for test df\n",
    "pred_test = RF_reg.predict(test_dfclean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "id": "Ck8fL8DvtBkM"
   },
   "outputs": [],
   "source": [
    "#read submission file\n",
    "submission=pd.read_csv(\"sample_submission_tbPU9qQ.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "5ZxdlX1bxw0E",
    "outputId": "d1d8ed27-024e-484d-81d8-a4ce65f5bb5d"
   },
   "outputs": [
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       "   loan_id  loan_default\n",
       "0     7001             1\n",
       "1     7002             1\n",
       "2     7003             1\n",
       "3     7004             1\n",
       "4     7005             1"
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   "execution_count": 99,
   "metadata": {
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    "submission['loan_default']=pred_test \n",
    "submission['loan_id']=test_df['loan_id']"
   ]
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      ],
      "text/plain": [
       "   loan_id  loan_default\n",
       "0     7001             0\n",
       "1     7002             0\n",
       "2     7003             0\n",
       "3     7004             0\n",
       "4     7005             0"
      ]
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "id": "VFtul4pmypJa"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "id": "9ptD13FIt-Jk"
   },
   "outputs": [],
   "source": [
    "#create file with answers\n",
    "pd.DataFrame(submission, columns=['loan_id','loan_default']).to_csv('randomforestfinal_classifier.csv', index=False)"
   ]
  }
 ],
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