analytics_vidya_loan_prediction.ipynb

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      "name": "analytics_vidya_loan_prediction.ipynb",
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      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOo+sq6R31H/4Tk2O060YKN",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/srinivas365/Analytics_Vidya_loan_prediction/blob/master/analytics_vidya_loan_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ICmQsdGLit8D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV4LRHdjsdD2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "raw_train=pd.read_csv('train.csv')\n",
        "raw_test=pd.read_csv('test.csv')\n",
        "sub=pd.read_csv('sample_submission.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMmw2yeQsmVY",
        "colab_type": "code",
        "outputId": "795ab101-96f0-4f1c-9888-3a8c5550a27f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "raw_train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        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>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Loan_ID Gender Married  ... Credit_History Property_Area Loan_Status\n",
              "0  LP001002   Male      No  ...            1.0         Urban           Y\n",
              "1  LP001003   Male     Yes  ...            1.0         Rural           N\n",
              "2  LP001005   Male     Yes  ...            1.0         Urban           Y\n",
              "3  LP001006   Male     Yes  ...            1.0         Urban           Y\n",
              "4  LP001008   Male      No  ...            1.0         Urban           Y\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymftLYrw90e3",
        "colab_type": "code",
        "outputId": "4b7738b0-8085-4ee5-a51b-6d9e2d0a3cf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "raw_test.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\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>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001015</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5720</td>\n",
              "      <td>0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001022</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3076</td>\n",
              "      <td>1500</td>\n",
              "      <td>126.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001031</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5000</td>\n",
              "      <td>1800</td>\n",
              "      <td>208.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001035</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2340</td>\n",
              "      <td>2546</td>\n",
              "      <td>100.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Urban</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001051</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3276</td>\n",
              "      <td>0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Loan_ID Gender Married  ... Loan_Amount_Term Credit_History Property_Area\n",
              "0  LP001015   Male     Yes  ...            360.0            1.0         Urban\n",
              "1  LP001022   Male     Yes  ...            360.0            1.0         Urban\n",
              "2  LP001031   Male     Yes  ...            360.0            1.0         Urban\n",
              "3  LP001035   Male     Yes  ...            360.0            NaN         Urban\n",
              "4  LP001051   Male      No  ...            360.0            1.0         Urban\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhx2mfb7-M_C",
        "colab_type": "code",
        "outputId": "6ec1eb80-3cb0-4ecd-e9f9-15395bf5a8fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"Shape of train:\",raw_train.shape)\n",
        "print(\"Shape of test:\",raw_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of train: (614, 13)\n",
            "Shape of test: (367, 12)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-DwQkyslsnj0",
        "colab_type": "code",
        "outputId": "f4c74982-c897-42fe-ced2-1c7a4eeb702e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "df=pd.concat([raw_train,raw_test])\n",
        "df.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID                0\n",
              "Gender                24\n",
              "Married                3\n",
              "Dependents            25\n",
              "Education              0\n",
              "Self_Employed         55\n",
              "ApplicantIncome        0\n",
              "CoapplicantIncome      0\n",
              "LoanAmount            27\n",
              "Loan_Amount_Term      20\n",
              "Credit_History        79\n",
              "Property_Area          0\n",
              "Loan_Status          367\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFig2utDsr1j",
        "colab_type": "code",
        "outputId": "f92842b9-8b16-4c39-f1c2-447abd957504",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 981 entries, 0 to 366\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            981 non-null    object \n",
            " 1   Gender             957 non-null    object \n",
            " 2   Married            978 non-null    object \n",
            " 3   Dependents         956 non-null    object \n",
            " 4   Education          981 non-null    object \n",
            " 5   Self_Employed      926 non-null    object \n",
            " 6   ApplicantIncome    981 non-null    int64  \n",
            " 7   CoapplicantIncome  981 non-null    float64\n",
            " 8   LoanAmount         954 non-null    float64\n",
            " 9   Loan_Amount_Term   961 non-null    float64\n",
            " 10  Credit_History     902 non-null    float64\n",
            " 11  Property_Area      981 non-null    object \n",
            " 12  Loan_Status        614 non-null    object \n",
            "dtypes: float64(4), int64(1), object(8)\n",
            "memory usage: 107.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zD0aS2oHs0J-",
        "colab_type": "code",
        "outputId": "340119c3-d907-490a-cf45-eab18efe5c38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "discrete=[var for var in df.columns if df[var].dtype!='O' and var!='Loan_Status' and df[var].nunique()<10]\n",
        "continuous=[var for var in df.columns if df[var].dtype!='O' and var!='Loan_Status' and var not in discrete]\n",
        "\n",
        "categorical=[var for var in df.columns if df[var].dtype=='O']\n",
        "\n",
        "print('There are {} discrete variables'.format(len(discrete)))\n",
        "print('There are {} continuous variables'.format(len(continuous)))\n",
        "print('There are {} categorical variables'.format(len(categorical)))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 1 discrete variables\n",
            "There are 4 continuous variables\n",
            "There are 8 categorical variables\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLAz5auVt1so",
        "colab_type": "code",
        "outputId": "2b6e6181-06c1-476b-9c24-2b4c903562e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "discrete"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Credit_History']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tv5dfSpVt447",
        "colab_type": "code",
        "outputId": "94172747-2971-4fa9-fed2-72597fc0b644",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "categorical"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Loan_ID',\n",
              " 'Gender',\n",
              " 'Married',\n",
              " 'Dependents',\n",
              " 'Education',\n",
              " 'Self_Employed',\n",
              " 'Property_Area',\n",
              " 'Loan_Status']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsGkCiSDt92l",
        "colab_type": "code",
        "outputId": "7122e7d3-c7c6-4258-e467-3e94de902ad2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "continuous"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GzICzDS7uD0M",
        "colab_type": "code",
        "outputId": "60cfa60b-dde6-41d7-f740-ef0e9a212b97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 981 entries, 0 to 366\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            981 non-null    object \n",
            " 1   Gender             981 non-null    object \n",
            " 2   Married            981 non-null    object \n",
            " 3   Dependents         981 non-null    object \n",
            " 4   Education          981 non-null    object \n",
            " 5   Self_Employed      981 non-null    object \n",
            " 6   ApplicantIncome    981 non-null    int64  \n",
            " 7   CoapplicantIncome  981 non-null    float64\n",
            " 8   LoanAmount         981 non-null    float64\n",
            " 9   Loan_Amount_Term   981 non-null    float64\n",
            " 10  Credit_History     981 non-null    float64\n",
            " 11  Property_Area      981 non-null    object \n",
            " 12  Loan_Status        614 non-null    object \n",
            "dtypes: float64(4), int64(1), object(8)\n",
            "memory usage: 107.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8QhLgGwTwtF7",
        "colab_type": "text"
      },
      "source": [
        "# Feature Engineering part-1\n",
        "\n",
        "1. Replacing null values with 'Missing' label for categorical variables.\n",
        "2. Replacing the null values for numerical with -999\n",
        "3. LabelEncoding and onehotencoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyY13BMIwKlW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#categorical\n",
        "df['Gender']=df.Gender.fillna(value='Missing')\n",
        "df['Married']=df.Married.fillna(value='Missing')\n",
        "df['Dependents']=df.Dependents.fillna(value='Missing')\n",
        "df['Self_Employed']=df.Self_Employed.fillna(value='Missing')\n",
        "#numerical\n",
        "df['LoanAmount']=df.LoanAmount.fillna(value=-999)\n",
        "df['Loan_Amount_Term']=df.Loan_Amount_Term.fillna(value=-999)\n",
        "df['Credit_History']=df.Credit_History.fillna(value=-999)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8RyCUBdx_AQr",
        "colab_type": "code",
        "outputId": "5d365d7b-ba06-4e26-968f-dd44e9a2774b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "X=df.drop(['Loan_ID','Loan_Status'],axis=1)\n",
        "y=df['Loan_Status']\n",
        "X_dum=pd.get_dummies(X,drop_first=True)\n",
        "X_dum.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 981 entries, 0 to 366\n",
            "Data columns (total 18 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   ApplicantIncome          981 non-null    int64  \n",
            " 1   CoapplicantIncome        981 non-null    float64\n",
            " 2   LoanAmount               981 non-null    float64\n",
            " 3   Loan_Amount_Term         981 non-null    float64\n",
            " 4   Credit_History           981 non-null    float64\n",
            " 5   Gender_Male              981 non-null    uint8  \n",
            " 6   Gender_Missing           981 non-null    uint8  \n",
            " 7   Married_No               981 non-null    uint8  \n",
            " 8   Married_Yes              981 non-null    uint8  \n",
            " 9   Dependents_1             981 non-null    uint8  \n",
            " 10  Dependents_2             981 non-null    uint8  \n",
            " 11  Dependents_3+            981 non-null    uint8  \n",
            " 12  Dependents_Missing       981 non-null    uint8  \n",
            " 13  Education_Not Graduate   981 non-null    uint8  \n",
            " 14  Self_Employed_No         981 non-null    uint8  \n",
            " 15  Self_Employed_Yes        981 non-null    uint8  \n",
            " 16  Property_Area_Semiurban  981 non-null    uint8  \n",
            " 17  Property_Area_Urban      981 non-null    uint8  \n",
            "dtypes: float64(4), int64(1), uint8(13)\n",
            "memory usage: 78.4 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXuLMaqXJfU0",
        "colab_type": "code",
        "outputId": "ae55e9d7-9477-490a-a981-76750c8700c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "y.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y    422\n",
              "N    192\n",
              "Name: Loan_Status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKCQUw-xAeGZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_X=X_dum.iloc[:614,:]\n",
        "train_y=y[:614]\n",
        "\n",
        "# test for submission\n",
        "test_X=X_dum.iloc[614:,:]\n",
        "test_y=y[614:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gY_phFzQD2-P",
        "colab_type": "code",
        "outputId": "f9c64f80-0673-4288-ae4d-627b16a0dea9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_X.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(367, 18)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xd1jnh3m1jgf",
        "colab_type": "text"
      },
      "source": [
        "# Model Building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cc4btPB41WPY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier, AdaBoostClassifier,VotingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier,plot_tree\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report,roc_auc_score\n",
        "from sklearn.model_selection import train_test_split, cross_val_score,GridSearchCV, RandomizedSearchCV"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Q5jJUj6668D",
        "colab_type": "code",
        "outputId": "38227274-0262-4b95-a55a-0f6e2ce7e2d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(train_X,train_y,test_size=0.2,stratify=train_y)\n",
        "print(\"Shape of X_train:\",X_train.shape)\n",
        "print(\"Shape of X_test:\",X_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of X_train: (491, 18)\n",
            "Shape of X_test: (123, 18)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNS5HUx0KfHh",
        "colab_type": "code",
        "outputId": "d6965bcb-32c5-483e-e034-e9e06f942956",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "y_train.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y    337\n",
              "N    154\n",
              "Name: Loan_Status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMbj8kIJJm3y",
        "colab_type": "code",
        "outputId": "d10624f8-3e6b-43c7-be84-12d3878f47d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "y_test.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y    85\n",
              "N    38\n",
              "Name: Loan_Status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjWcvWAZ2DSv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_scores={}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMcodm1x1nXc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def build_model(model,X_train,X_test,y_train,y_test):\n",
        "    model.fit(X_train,y_train)\n",
        "    y_pred=model.predict(X_test)\n",
        "    y_train_pred=model.predict(X_train)\n",
        "    \n",
        "    model_name = type(model).__name__\n",
        "    train_acc=accuracy_score(y_train,y_train_pred)\n",
        "    test_acc=accuracy_score(y_test,y_pred)\n",
        "    \n",
        "    print(confusion_matrix(y_test,y_pred))\n",
        "\n",
        "    print(classification_report(y_test,y_pred))\n",
        "    \n",
        "\n",
        "\n",
        "    \n",
        "    print(\"Training accuracy: : \",train_acc)\n",
        "    print(\"Testing  accuracy: : \",test_acc)\n",
        "    \n",
        "    cv_results=cross_val_score(model,X_train,y_train,cv=10,n_jobs=-1)\n",
        "    print(\"Cross validation Log Loss:\", cv_results.mean())\n",
        "\n",
        "    cv_acc=cv_results.mean()\n",
        "    model_scores.setdefault(model_name,[train_acc,test_acc,cv_acc])\n",
        "\n",
        "    y_test=y_test.map({'Y':1,'N':0})\n",
        "    y_pred=pd.Series(y_pred).map({'Y':1,'N':0})\n",
        "\n",
        "\n",
        "    print(\"ROC_AUC-score:\",roc_auc_score(y_test,y_pred))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yROQI1y7F7CF",
        "colab_type": "text"
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whBgTjzg19dH",
        "colab_type": "code",
        "outputId": "8bbd5985-479c-4410-bf40-d43195bf63f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "std=StandardScaler()\n",
        "Xtr_std=std.fit_transform(X_train[['']])\n",
        "Xte_std=std.transform(X_test)\n",
        "lr=LogisticRegression()\n",
        "build_model(lr,Xtr_std,Xte_std,y_train,y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 3 35]\n",
            " [ 8 77]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.27      0.08      0.12        38\n",
            "           Y       0.69      0.91      0.78        85\n",
            "\n",
            "    accuracy                           0.65       123\n",
            "   macro avg       0.48      0.49      0.45       123\n",
            "weighted avg       0.56      0.65      0.58       123\n",
            "\n",
            "Training accuracy: :  0.6985743380855397\n",
            "Testing  accuracy: :  0.6504065040650406\n",
            "Cross validation Log Loss: 0.6698775510204081\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6nxk0qe9dhE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lr_preds=lr.predict(std.transform(test_X))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCpFLLppD9ec",
        "colab_type": "code",
        "outputId": "7de0e8c5-9c6a-41da-d610-1cfdaacdb7dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        }
      },
      "source": [
        "lr_preds"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'N', 'Y', 'Y', 'N',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N',\n",
              "       'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
              "       'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y',\n",
              "       'Y', 'Y', 'Y'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "swuzdj-yG_Bb",
        "colab_type": "text"
      },
      "source": [
        "# SVC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9eSV8ET0Gnsh",
        "colab_type": "code",
        "outputId": "c9d508a6-e737-4efd-e6af-f82acf69930e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "svc=SVC()\n",
        "build_model(svc,Xtr_std,Xte_std,y_train,y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 1 37]\n",
            " [ 3 82]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.25      0.03      0.05        38\n",
            "           Y       0.69      0.96      0.80        85\n",
            "\n",
            "    accuracy                           0.67       123\n",
            "   macro avg       0.47      0.50      0.43       123\n",
            "weighted avg       0.55      0.67      0.57       123\n",
            "\n",
            "Training accuracy: :  0.7331975560081466\n",
            "Testing  accuracy: :  0.6747967479674797\n",
            "Cross validation Log Loss: 0.6618367346938775\n",
            "ROC_AUC-score: 0.4955108359133127\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQQmaeDUJNro",
        "colab_type": "text"
      },
      "source": [
        "# DecisionTree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IzJnvXoEJNAm",
        "colab_type": "code",
        "outputId": "c5edeff0-0d5d-4b56-9e73-b102487462c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "dt=DecisionTreeClassifier()\n",
        "build_model(dt,X_train,X_test,y_train,y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[18 20]\n",
            " [24 61]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.43      0.47      0.45        38\n",
            "           Y       0.75      0.72      0.73        85\n",
            "\n",
            "    accuracy                           0.64       123\n",
            "   macro avg       0.59      0.60      0.59       123\n",
            "weighted avg       0.65      0.64      0.65       123\n",
            "\n",
            "Training accuracy: :  1.0\n",
            "Testing  accuracy: :  0.6422764227642277\n",
            "Cross validation Log Loss: 0.7330204081632653\n",
            "ROC_AUC-score: 0.5956656346749226\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGKLJ_HKKoUs",
        "colab_type": "code",
        "outputId": "5ee9db9d-753d-4b18-ab5d-97409f14f8c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dt.get_depth()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBDJXsuSHIEB",
        "colab_type": "code",
        "outputId": "f52c1c9f-082b-4777-f090-9815358a61eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 913
        }
      },
      "source": [
        "plt.figure(figsize=(30,30))\n",
        "plot_tree(dt,fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wt6FqM5BJ_zB",
        "colab_type": "code",
        "outputId": "12bea46d-bdab-4b25-9687-0ece9c6551d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "dt_max_10=DecisionTreeClassifier(max_depth=10)\n",
        "build_model(dt_max_10,X_train,X_test,y_train,y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[17 21]\n",
            " [15 70]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.53      0.45      0.49        38\n",
            "           Y       0.77      0.82      0.80        85\n",
            "\n",
            "    accuracy                           0.71       123\n",
            "   macro avg       0.65      0.64      0.64       123\n",
            "weighted avg       0.70      0.71      0.70       123\n",
            "\n",
            "Training accuracy: :  0.9164969450101833\n",
            "Testing  accuracy: :  0.7073170731707317\n",
            "Cross validation Log Loss: 0.7676326530612244\n",
            "ROC_AUC-score: 0.6354489164086687\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFcvny_AK_-_",
        "colab_type": "code",
        "outputId": "4076c8a7-114f-4d6e-891c-69616c271817",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for mt in range(1,18):\n",
        "    print(\"MAX Depth : \",mt)\n",
        "    build_model(DecisionTreeClassifier(max_depth=mt,random_state=42),X_train,X_test,y_train,y_test)\n",
        "    print('*'*50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAX Depth :  1\n",
            "[[20 18]\n",
            " [ 8 77]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.71      0.53      0.61        38\n",
            "           Y       0.81      0.91      0.86        85\n",
            "\n",
            "    accuracy                           0.79       123\n",
            "   macro avg       0.76      0.72      0.73       123\n",
            "weighted avg       0.78      0.79      0.78       123\n",
            "\n",
            "Training accuracy: :  0.7657841140529531\n",
            "Testing  accuracy: :  0.7886178861788617\n",
            "Cross validation Log Loss: 0.7657142857142857\n",
            "ROC_AUC-score: 0.7160990712074303\n",
            "**************************************************\n",
            "MAX Depth :  2\n",
            "[[16 22]\n",
            " [ 0 85]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       1.00      0.42      0.59        38\n",
            "           Y       0.79      1.00      0.89        85\n",
            "\n",
            "    accuracy                           0.82       123\n",
            "   macro avg       0.90      0.71      0.74       123\n",
            "weighted avg       0.86      0.82      0.79       123\n",
            "\n",
            "Training accuracy: :  0.8065173116089613\n",
            "Testing  accuracy: :  0.8211382113821138\n",
            "Cross validation Log Loss: 0.8023673469387755\n",
            "ROC_AUC-score: 0.7105263157894737\n",
            "**************************************************\n",
            "MAX Depth :  3\n",
            "[[17 21]\n",
            " [ 2 83]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.89      0.45      0.60        38\n",
            "           Y       0.80      0.98      0.88        85\n",
            "\n",
            "    accuracy                           0.81       123\n",
            "   macro avg       0.85      0.71      0.74       123\n",
            "weighted avg       0.83      0.81      0.79       123\n",
            "\n",
            "Training accuracy: :  0.8167006109979633\n",
            "Testing  accuracy: :  0.8130081300813008\n",
            "Cross validation Log Loss: 0.7962857142857144\n",
            "ROC_AUC-score: 0.7119195046439628\n",
            "**************************************************\n",
            "MAX Depth :  4\n",
            "[[17 21]\n",
            " [ 5 80]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.77      0.45      0.57        38\n",
            "           Y       0.79      0.94      0.86        85\n",
            "\n",
            "    accuracy                           0.79       123\n",
            "   macro avg       0.78      0.69      0.71       123\n",
            "weighted avg       0.79      0.79      0.77       123\n",
            "\n",
            "Training accuracy: :  0.8370672097759674\n",
            "Testing  accuracy: :  0.7886178861788617\n",
            "Cross validation Log Loss: 0.8044489795918366\n",
            "ROC_AUC-score: 0.6942724458204335\n",
            "**************************************************\n",
            "MAX Depth :  5\n",
            "[[17 21]\n",
            " [ 5 80]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.77      0.45      0.57        38\n",
            "           Y       0.79      0.94      0.86        85\n",
            "\n",
            "    accuracy                           0.79       123\n",
            "   macro avg       0.78      0.69      0.71       123\n",
            "weighted avg       0.79      0.79      0.77       123\n",
            "\n",
            "Training accuracy: :  0.8513238289205702\n",
            "Testing  accuracy: :  0.7886178861788617\n",
            "Cross validation Log Loss: 0.8003673469387756\n",
            "ROC_AUC-score: 0.6942724458204335\n",
            "**************************************************\n",
            "MAX Depth :  6\n",
            "[[17 21]\n",
            " [ 6 79]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.74      0.45      0.56        38\n",
            "           Y       0.79      0.93      0.85        85\n",
            "\n",
            "    accuracy                           0.78       123\n",
            "   macro avg       0.76      0.69      0.71       123\n",
            "weighted avg       0.77      0.78      0.76       123\n",
            "\n",
            "Training accuracy: :  0.8553971486761711\n",
            "Testing  accuracy: :  0.7804878048780488\n",
            "Cross validation Log Loss: 0.7759591836734694\n",
            "ROC_AUC-score: 0.688390092879257\n",
            "**************************************************\n",
            "MAX Depth :  7\n",
            "[[16 22]\n",
            " [ 7 78]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.70      0.42      0.52        38\n",
            "           Y       0.78      0.92      0.84        85\n",
            "\n",
            "    accuracy                           0.76       123\n",
            "   macro avg       0.74      0.67      0.68       123\n",
            "weighted avg       0.75      0.76      0.74       123\n",
            "\n",
            "Training accuracy: :  0.8778004073319755\n",
            "Testing  accuracy: :  0.7642276422764228\n",
            "Cross validation Log Loss: 0.755469387755102\n",
            "ROC_AUC-score: 0.6693498452012383\n",
            "**************************************************\n",
            "MAX Depth :  8\n",
            "[[18 20]\n",
            " [15 70]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.55      0.47      0.51        38\n",
            "           Y       0.78      0.82      0.80        85\n",
            "\n",
            "    accuracy                           0.72       123\n",
            "   macro avg       0.66      0.65      0.65       123\n",
            "weighted avg       0.71      0.72      0.71       123\n",
            "\n",
            "Training accuracy: :  0.9022403258655805\n",
            "Testing  accuracy: :  0.7154471544715447\n",
            "Cross validation Log Loss: 0.7655918367346939\n",
            "ROC_AUC-score: 0.6486068111455108\n",
            "**************************************************\n",
            "MAX Depth :  9\n",
            "[[18 20]\n",
            " [17 68]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.51      0.47      0.49        38\n",
            "           Y       0.77      0.80      0.79        85\n",
            "\n",
            "    accuracy                           0.70       123\n",
            "   macro avg       0.64      0.64      0.64       123\n",
            "weighted avg       0.69      0.70      0.70       123\n",
            "\n",
            "Training accuracy: :  0.9083503054989817\n",
            "Testing  accuracy: :  0.6991869918699187\n",
            "Cross validation Log Loss: 0.7595102040816325\n",
            "ROC_AUC-score: 0.6368421052631579\n",
            "**************************************************\n",
            "MAX Depth :  10\n",
            "[[18 20]\n",
            " [14 71]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.56      0.47      0.51        38\n",
            "           Y       0.78      0.84      0.81        85\n",
            "\n",
            "    accuracy                           0.72       123\n",
            "   macro avg       0.67      0.65      0.66       123\n",
            "weighted avg       0.71      0.72      0.72       123\n",
            "\n",
            "Training accuracy: :  0.9164969450101833\n",
            "Testing  accuracy: :  0.7235772357723578\n",
            "Cross validation Log Loss: 0.7696734693877552\n",
            "ROC_AUC-score: 0.6544891640866873\n",
            "**************************************************\n",
            "MAX Depth :  11\n",
            "[[17 21]\n",
            " [15 70]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.53      0.45      0.49        38\n",
            "           Y       0.77      0.82      0.80        85\n",
            "\n",
            "    accuracy                           0.71       123\n",
            "   macro avg       0.65      0.64      0.64       123\n",
            "weighted avg       0.70      0.71      0.70       123\n",
            "\n",
            "Training accuracy: :  0.9226069246435845\n",
            "Testing  accuracy: :  0.7073170731707317\n",
            "Cross validation Log Loss: 0.7615510204081632\n",
            "ROC_AUC-score: 0.6354489164086687\n",
            "**************************************************\n",
            "MAX Depth :  12\n",
            "[[18 20]\n",
            " [20 65]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.47      0.47      0.47        38\n",
            "           Y       0.76      0.76      0.76        85\n",
            "\n",
            "    accuracy                           0.67       123\n",
            "   macro avg       0.62      0.62      0.62       123\n",
            "weighted avg       0.67      0.67      0.67       123\n",
            "\n",
            "Training accuracy: :  0.9287169042769857\n",
            "Testing  accuracy: :  0.6747967479674797\n",
            "Cross validation Log Loss: 0.7575510204081632\n",
            "ROC_AUC-score: 0.6191950464396284\n",
            "**************************************************\n",
            "MAX Depth :  13\n",
            "[[17 21]\n",
            " [17 68]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.50      0.45      0.47        38\n",
            "           Y       0.76      0.80      0.78        85\n",
            "\n",
            "    accuracy                           0.69       123\n",
            "   macro avg       0.63      0.62      0.63       123\n",
            "weighted avg       0.68      0.69      0.69       123\n",
            "\n",
            "Training accuracy: :  0.9490835030549898\n",
            "Testing  accuracy: :  0.6910569105691057\n",
            "Cross validation Log Loss: 0.7554693877551019\n",
            "ROC_AUC-score: 0.6236842105263158\n",
            "**************************************************\n",
            "MAX Depth :  14\n",
            "[[18 20]\n",
            " [22 63]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.45      0.47      0.46        38\n",
            "           Y       0.76      0.74      0.75        85\n",
            "\n",
            "    accuracy                           0.66       123\n",
            "   macro avg       0.60      0.61      0.61       123\n",
            "weighted avg       0.66      0.66      0.66       123\n",
            "\n",
            "Training accuracy: :  0.9633401221995926\n",
            "Testing  accuracy: :  0.6585365853658537\n",
            "Cross validation Log Loss: 0.765673469387755\n",
            "ROC_AUC-score: 0.6074303405572756\n",
            "**************************************************\n",
            "MAX Depth :  15\n",
            "[[18 20]\n",
            " [18 67]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.50      0.47      0.49        38\n",
            "           Y       0.77      0.79      0.78        85\n",
            "\n",
            "    accuracy                           0.69       123\n",
            "   macro avg       0.64      0.63      0.63       123\n",
            "weighted avg       0.69      0.69      0.69       123\n",
            "\n",
            "Training accuracy: :  0.9694501018329938\n",
            "Testing  accuracy: :  0.6910569105691057\n",
            "Cross validation Log Loss: 0.7554693877551021\n",
            "ROC_AUC-score: 0.6309597523219814\n",
            "**************************************************\n",
            "MAX Depth :  16\n",
            "[[18 20]\n",
            " [18 67]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.50      0.47      0.49        38\n",
            "           Y       0.77      0.79      0.78        85\n",
            "\n",
            "    accuracy                           0.69       123\n",
            "   macro avg       0.64      0.63      0.63       123\n",
            "weighted avg       0.69      0.69      0.69       123\n",
            "\n",
            "Training accuracy: :  0.9857433808553971\n",
            "Testing  accuracy: :  0.6910569105691057\n",
            "Cross validation Log Loss: 0.7555102040816326\n",
            "ROC_AUC-score: 0.6309597523219814\n",
            "**************************************************\n",
            "MAX Depth :  17\n",
            "[[18 20]\n",
            " [22 63]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.45      0.47      0.46        38\n",
            "           Y       0.76      0.74      0.75        85\n",
            "\n",
            "    accuracy                           0.66       123\n",
            "   macro avg       0.60      0.61      0.61       123\n",
            "weighted avg       0.66      0.66      0.66       123\n",
            "\n",
            "Training accuracy: :  0.9877800407331976\n",
            "Testing  accuracy: :  0.6585365853658537\n",
            "Cross validation Log Loss: 0.747265306122449\n",
            "ROC_AUC-score: 0.6074303405572756\n",
            "**************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYfi9xamLfgZ",
        "colab_type": "code",
        "outputId": "8d5e5e9f-6f07-4021-9e84-298eafe4c702",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "dt_max_3=DecisionTreeClassifier(max_depth=3,random_state=42)\n",
        "build_model(dt_max_3,X_train,X_test,y_train,y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[17 21]\n",
            " [ 2 83]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.89      0.45      0.60        38\n",
            "           Y       0.80      0.98      0.88        85\n",
            "\n",
            "    accuracy                           0.81       123\n",
            "   macro avg       0.85      0.71      0.74       123\n",
            "weighted avg       0.83      0.81      0.79       123\n",
            "\n",
            "Training accuracy: :  0.8167006109979633\n",
            "Testing  accuracy: :  0.8130081300813008\n",
            "Cross validation Log Loss: 0.7962857142857144\n",
            "ROC_AUC-score: 0.7119195046439628\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pUjQMzOEPQOG",
        "colab_type": "code",
        "outputId": "b2c8292b-0649-4e15-efc7-c09cf46ab598",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        }
      },
      "source": [
        "dt3_preds=dt_max_3.predict(test_X)\n",
        "dt3_preds"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y',\n",
              "       'Y', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y',\n",
              "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'N', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N',\n",
              "       'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N',\n",
              "       'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y',\n",
              "       'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y',\n",
              "       'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y',\n",
              "       'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
              "       'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s1spW4MiQCed",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub['Loan_Status']=dt3_preds\n",
        "sub.to_csv('dt_md_3.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cMJStN3-QQf8",
        "colab_type": "code",
        "outputId": "026fb009-4339-48dd-f0c6-57d350ee24c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "dt.get_params()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ccp_alpha': 0.0,\n",
              " 'class_weight': None,\n",
              " 'criterion': 'gini',\n",
              " 'max_depth': None,\n",
              " 'max_features': None,\n",
              " 'max_leaf_nodes': None,\n",
              " 'min_impurity_decrease': 0.0,\n",
              " 'min_impurity_split': None,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'min_weight_fraction_leaf': 0.0,\n",
              " 'presort': 'deprecated',\n",
              " 'random_state': None,\n",
              " 'splitter': 'best'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMWja-fQSPcq",
        "colab_type": "code",
        "outputId": "cffa1c31-342a-47e6-ae1e-75163279a4b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i in [0.2,0.4,0.6,0.8,1.0]:\n",
        "    print(\"max_features:\",i)\n",
        "    build_model(DecisionTreeClassifier(max_depth=3,max_features=i,random_state=42),X_train,X_test,y_train,y_test)\n",
        "    print(\"*\"*50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "max_features: 0.2\n",
            "[[16 22]\n",
            " [10 75]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.62      0.42      0.50        38\n",
            "           Y       0.77      0.88      0.82        85\n",
            "\n",
            "    accuracy                           0.74       123\n",
            "   macro avg       0.69      0.65      0.66       123\n",
            "weighted avg       0.72      0.74      0.72       123\n",
            "\n",
            "Training accuracy: :  0.7739307535641547\n",
            "Testing  accuracy: :  0.7398373983739838\n",
            "Cross validation Log Loss: 0.7289795918367347\n",
            "ROC_AUC-score: 0.651702786377709\n",
            "**************************************************\n",
            "max_features: 0.4\n",
            "[[16 22]\n",
            " [ 2 83]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.89      0.42      0.57        38\n",
            "           Y       0.79      0.98      0.87        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.84      0.70      0.72       123\n",
            "weighted avg       0.82      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  0.8105906313645621\n",
            "Testing  accuracy: :  0.8048780487804879\n",
            "Cross validation Log Loss: 0.7881224489795918\n",
            "ROC_AUC-score: 0.6987616099071208\n",
            "**************************************************\n",
            "max_features: 0.6\n",
            "[[18 20]\n",
            " [ 3 82]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.86      0.47      0.61        38\n",
            "           Y       0.80      0.96      0.88        85\n",
            "\n",
            "    accuracy                           0.81       123\n",
            "   macro avg       0.83      0.72      0.74       123\n",
            "weighted avg       0.82      0.81      0.79       123\n",
            "\n",
            "Training accuracy: :  0.814663951120163\n",
            "Testing  accuracy: :  0.8130081300813008\n",
            "Cross validation Log Loss: 0.8003673469387756\n",
            "ROC_AUC-score: 0.7191950464396285\n",
            "**************************************************\n",
            "max_features: 0.8\n",
            "[[17 21]\n",
            " [ 1 84]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.94      0.45      0.61        38\n",
            "           Y       0.80      0.99      0.88        85\n",
            "\n",
            "    accuracy                           0.82       123\n",
            "   macro avg       0.87      0.72      0.75       123\n",
            "weighted avg       0.84      0.82      0.80       123\n",
            "\n",
            "Training accuracy: :  0.8126272912423625\n",
            "Testing  accuracy: :  0.8211382113821138\n",
            "Cross validation Log Loss: 0.7901632653061225\n",
            "ROC_AUC-score: 0.7178018575851393\n",
            "**************************************************\n",
            "max_features: 1.0\n",
            "[[17 21]\n",
            " [ 2 83]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.89      0.45      0.60        38\n",
            "           Y       0.80      0.98      0.88        85\n",
            "\n",
            "    accuracy                           0.81       123\n",
            "   macro avg       0.85      0.71      0.74       123\n",
            "weighted avg       0.83      0.81      0.79       123\n",
            "\n",
            "Training accuracy: :  0.8167006109979633\n",
            "Testing  accuracy: :  0.8130081300813008\n",
            "Cross validation Log Loss: 0.7962857142857144\n",
            "ROC_AUC-score: 0.7119195046439628\n",
            "**************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZg9yuAKS4ol",
        "colab_type": "code",
        "outputId": "c1adab73-9352-49bf-dc67-93c485ef324e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "dtmd_3_mf_06=DecisionTreeClassifier(max_depth=3,max_features=0.6,random_state=42)\n",
        "build_model(dtmd_3_mf_06,X_train,X_test,y_train,y_test)\n",
        "\n",
        "test_preds=dtmd_3_mf_06.predict(test_X)\n",
        "\n",
        "sub['Loan_Status']=test_preds\n",
        "sub.to_csv('dtmd_3_mf_06.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[18 20]\n",
            " [ 3 82]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.86      0.47      0.61        38\n",
            "           Y       0.80      0.96      0.88        85\n",
            "\n",
            "    accuracy                           0.81       123\n",
            "   macro avg       0.83      0.72      0.74       123\n",
            "weighted avg       0.82      0.81      0.79       123\n",
            "\n",
            "Training accuracy: :  0.814663951120163\n",
            "Testing  accuracy: :  0.8130081300813008\n",
            "Cross validation Log Loss: 0.8003673469387756\n",
            "ROC_AUC-score: 0.7191950464396285\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bmw1LzZrUSB4",
        "colab_type": "text"
      },
      "source": [
        "# RandomForestClassifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0OC8HoeWTtnC",
        "colab_type": "code",
        "outputId": "558eb883-36b7-4280-f465-c6b015085e51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "rf=RandomForestClassifier(random_state=42)\n",
        "build_model(rf,X_train, X_test, y_train, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[18 20]\n",
            " [ 4 81]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.82      0.47      0.60        38\n",
            "           Y       0.80      0.95      0.87        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.81      0.71      0.74       123\n",
            "weighted avg       0.81      0.80      0.79       123\n",
            "\n",
            "Training accuracy: :  1.0\n",
            "Testing  accuracy: :  0.8048780487804879\n",
            "Cross validation Log Loss: 0.7718775510204081\n",
            "ROC_AUC-score: 0.7133126934984519\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQ4lt_0KUZfZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_preds=rf.predict(test_X)\n",
        "\n",
        "sub['Loan_Status']=test_preds\n",
        "sub.to_csv('simple_rf.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RraPO3SZa-ac",
        "colab_type": "code",
        "outputId": "b9a574e1-6dd4-43af-a70f-3123c2ec423a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for est in range(100,1000,100):\n",
        "    print(\"Estimators:\",est)\n",
        "    build_model(RandomForestClassifier(random_state=42,max_depth=3,n_estimators=est), X_train, X_test, y_train, y_test)\n",
        "    print(\"*\"*50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Estimators: 100\n",
            "[[14 24]\n",
            " [ 2 83]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.88      0.37      0.52        38\n",
            "           Y       0.78      0.98      0.86        85\n",
            "\n",
            "    accuracy                           0.79       123\n",
            "   macro avg       0.83      0.67      0.69       123\n",
            "weighted avg       0.81      0.79      0.76       123\n",
            "\n",
            "Training accuracy: :  0.7983706720977597\n",
            "Testing  accuracy: :  0.7886178861788617\n",
            "Cross validation Log Loss: 0.7596326530612244\n",
            "ROC_AUC-score: 0.6724458204334366\n",
            "**************************************************\n",
            "Estimators: 200\n",
            "[[13 25]\n",
            " [ 1 84]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.93      0.34      0.50        38\n",
            "           Y       0.77      0.99      0.87        85\n",
            "\n",
            "    accuracy                           0.79       123\n",
            "   macro avg       0.85      0.67      0.68       123\n",
            "weighted avg       0.82      0.79      0.75       123\n",
            "\n",
            "Training accuracy: :  0.8004073319755601\n",
            "Testing  accuracy: :  0.7886178861788617\n",
            "Cross validation Log Loss: 0.7637551020408162\n",
            "ROC_AUC-score: 0.6651702786377709\n",
            "**************************************************\n",
            "Estimators: 300\n",
            "[[15 23]\n",
            " [ 1 84]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.94      0.39      0.56        38\n",
            "           Y       0.79      0.99      0.88        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.86      0.69      0.72       123\n",
            "weighted avg       0.83      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  0.7942973523421588\n",
            "Testing  accuracy: :  0.8048780487804879\n",
            "Cross validation Log Loss: 0.7616734693877552\n",
            "ROC_AUC-score: 0.6914860681114552\n",
            "**************************************************\n",
            "Estimators: 400\n",
            "[[14 24]\n",
            " [ 1 84]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.93      0.37      0.53        38\n",
            "           Y       0.78      0.99      0.87        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.86      0.68      0.70       123\n",
            "weighted avg       0.83      0.80      0.76       123\n",
            "\n",
            "Training accuracy: :  0.7963340122199593\n",
            "Testing  accuracy: :  0.7967479674796748\n",
            "Cross validation Log Loss: 0.765795918367347\n",
            "ROC_AUC-score: 0.678328173374613\n",
            "**************************************************\n",
            "Estimators: 500\n",
            "[[15 23]\n",
            " [ 1 84]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.94      0.39      0.56        38\n",
            "           Y       0.79      0.99      0.88        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.86      0.69      0.72       123\n",
            "weighted avg       0.83      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  0.7963340122199593\n",
            "Testing  accuracy: :  0.8048780487804879\n",
            "Cross validation Log Loss: 0.7657959183673468\n",
            "ROC_AUC-score: 0.6914860681114552\n",
            "**************************************************\n",
            "Estimators: 600\n",
            "[[14 24]\n",
            " [ 1 84]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.93      0.37      0.53        38\n",
            "           Y       0.78      0.99      0.87        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.86      0.68      0.70       123\n",
            "weighted avg       0.83      0.80      0.76       123\n",
            "\n",
            "Training accuracy: :  0.7963340122199593\n",
            "Testing  accuracy: :  0.7967479674796748\n",
            "Cross validation Log Loss: 0.7597142857142857\n",
            "ROC_AUC-score: 0.678328173374613\n",
            "**************************************************\n",
            "Estimators: 700\n",
            "[[13 25]\n",
            " [ 1 84]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.93      0.34      0.50        38\n",
            "           Y       0.77      0.99      0.87        85\n",
            "\n",
            "    accuracy                           0.79       123\n",
            "   macro avg       0.85      0.67      0.68       123\n",
            "weighted avg       0.82      0.79      0.75       123\n",
            "\n",
            "Training accuracy: :  0.7963340122199593\n",
            "Testing  accuracy: :  0.7886178861788617\n",
            "Cross validation Log Loss: 0.7637959183673468\n",
            "ROC_AUC-score: 0.6651702786377709\n",
            "**************************************************\n",
            "Estimators: 800\n",
            "[[15 23]\n",
            " [ 1 84]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.94      0.39      0.56        38\n",
            "           Y       0.79      0.99      0.88        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.86      0.69      0.72       123\n",
            "weighted avg       0.83      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  0.7983706720977597\n",
            "Testing  accuracy: :  0.8048780487804879\n",
            "Cross validation Log Loss: 0.7658367346938775\n",
            "ROC_AUC-score: 0.6914860681114552\n",
            "**************************************************\n",
            "Estimators: 900\n",
            "[[15 23]\n",
            " [ 1 84]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.94      0.39      0.56        38\n",
            "           Y       0.79      0.99      0.88        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.86      0.69      0.72       123\n",
            "weighted avg       0.83      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  0.8024439918533605\n",
            "Testing  accuracy: :  0.8048780487804879\n",
            "Cross validation Log Loss: 0.7637959183673468\n",
            "ROC_AUC-score: 0.6914860681114552\n",
            "**************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0S2wG3wMbWz2",
        "colab_type": "code",
        "outputId": "d55bbf72-864b-4700-e62b-8ad4e1e40e96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for depth in range(1,20):\n",
        "    print(\"max_depth:\",depth)\n",
        "    build_model(RandomForestClassifier(random_state=42,max_depth=depth,n_estimators=300), X_train, X_test, y_train, y_test)\n",
        "    print(\"*\"*50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "max_depth: 1\n",
            "[[ 0 38]\n",
            " [ 0 85]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.00      0.00      0.00        38\n",
            "           Y       0.69      1.00      0.82        85\n",
            "\n",
            "    accuracy                           0.69       123\n",
            "   macro avg       0.35      0.50      0.41       123\n",
            "weighted avg       0.48      0.69      0.56       123\n",
            "\n",
            "Training accuracy: :  0.6863543788187373\n",
            "Testing  accuracy: :  0.6910569105691057\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Cross validation Log Loss: 0.6863673469387754\n",
            "ROC_AUC-score: 0.5\n",
            "**************************************************\n",
            "max_depth: 2\n",
            "[[ 1 37]\n",
            " [ 1 84]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.50      0.03      0.05        38\n",
            "           Y       0.69      0.99      0.82        85\n",
            "\n",
            "    accuracy                           0.69       123\n",
            "   macro avg       0.60      0.51      0.43       123\n",
            "weighted avg       0.63      0.69      0.58       123\n",
            "\n",
            "Training accuracy: :  0.7026476578411406\n",
            "Testing  accuracy: :  0.6910569105691057\n",
            "Cross validation Log Loss: 0.6986122448979593\n",
            "ROC_AUC-score: 0.5072755417956656\n",
            "**************************************************\n",
            "max_depth: 3\n",
            "[[15 23]\n",
            " [ 1 84]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.94      0.39      0.56        38\n",
            "           Y       0.79      0.99      0.88        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.86      0.69      0.72       123\n",
            "weighted avg       0.83      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  0.7942973523421588\n",
            "Testing  accuracy: :  0.8048780487804879\n",
            "Cross validation Log Loss: 0.7616734693877552\n",
            "ROC_AUC-score: 0.6914860681114552\n",
            "**************************************************\n",
            "max_depth: 4\n",
            "[[17 21]\n",
            " [ 2 83]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.89      0.45      0.60        38\n",
            "           Y       0.80      0.98      0.88        85\n",
            "\n",
            "    accuracy                           0.81       123\n",
            "   macro avg       0.85      0.71      0.74       123\n",
            "weighted avg       0.83      0.81      0.79       123\n",
            "\n",
            "Training accuracy: :  0.8228105906313645\n",
            "Testing  accuracy: :  0.8130081300813008\n",
            "Cross validation Log Loss: 0.7861224489795917\n",
            "ROC_AUC-score: 0.7119195046439628\n",
            "**************************************************\n",
            "max_depth: 5\n",
            "[[17 21]\n",
            " [ 2 83]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.89      0.45      0.60        38\n",
            "           Y       0.80      0.98      0.88        85\n",
            "\n",
            "    accuracy                           0.81       123\n",
            "   macro avg       0.85      0.71      0.74       123\n",
            "weighted avg       0.83      0.81      0.79       123\n",
            "\n",
            "Training accuracy: :  0.835030549898167\n",
            "Testing  accuracy: :  0.8130081300813008\n",
            "Cross validation Log Loss: 0.7962857142857143\n",
            "ROC_AUC-score: 0.7119195046439628\n",
            "**************************************************\n",
            "max_depth: 6\n",
            "[[17 21]\n",
            " [ 3 82]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.85      0.45      0.59        38\n",
            "           Y       0.80      0.96      0.87        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.82      0.71      0.73       123\n",
            "weighted avg       0.81      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  0.845213849287169\n",
            "Testing  accuracy: :  0.8048780487804879\n",
            "Cross validation Log Loss: 0.7881224489795919\n",
            "ROC_AUC-score: 0.7060371517027864\n",
            "**************************************************\n",
            "max_depth: 7\n",
            "[[17 21]\n",
            " [ 2 83]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.89      0.45      0.60        38\n",
            "           Y       0.80      0.98      0.88        85\n",
            "\n",
            "    accuracy                           0.81       123\n",
            "   macro avg       0.85      0.71      0.74       123\n",
            "weighted avg       0.83      0.81      0.79       123\n",
            "\n",
            "Training accuracy: :  0.8716904276985743\n",
            "Testing  accuracy: :  0.8130081300813008\n",
            "Cross validation Log Loss: 0.7922040816326531\n",
            "ROC_AUC-score: 0.7119195046439628\n",
            "**************************************************\n",
            "max_depth: 8\n",
            "[[17 21]\n",
            " [ 4 81]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.81      0.45      0.58        38\n",
            "           Y       0.79      0.95      0.87        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.80      0.70      0.72       123\n",
            "weighted avg       0.80      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  0.8920570264765784\n",
            "Testing  accuracy: :  0.7967479674796748\n",
            "Cross validation Log Loss: 0.7881224489795917\n",
            "ROC_AUC-score: 0.7001547987616099\n",
            "**************************************************\n",
            "max_depth: 9\n",
            "[[17 21]\n",
            " [ 4 81]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.81      0.45      0.58        38\n",
            "           Y       0.79      0.95      0.87        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.80      0.70      0.72       123\n",
            "weighted avg       0.80      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  0.9226069246435845\n",
            "Testing  accuracy: :  0.7967479674796748\n",
            "Cross validation Log Loss: 0.7860816326530612\n",
            "ROC_AUC-score: 0.7001547987616099\n",
            "**************************************************\n",
            "max_depth: 10\n",
            "[[18 20]\n",
            " [ 4 81]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.82      0.47      0.60        38\n",
            "           Y       0.80      0.95      0.87        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.81      0.71      0.74       123\n",
            "weighted avg       0.81      0.80      0.79       123\n",
            "\n",
            "Training accuracy: :  0.945010183299389\n",
            "Testing  accuracy: :  0.8048780487804879\n",
            "Cross validation Log Loss: 0.7820816326530611\n",
            "ROC_AUC-score: 0.7133126934984519\n",
            "**************************************************\n",
            "max_depth: 11\n",
            "[[17 21]\n",
            " [ 4 81]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.81      0.45      0.58        38\n",
            "           Y       0.79      0.95      0.87        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.80      0.70      0.72       123\n",
            "weighted avg       0.80      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  0.9592668024439919\n",
            "Testing  accuracy: :  0.7967479674796748\n",
            "Cross validation Log Loss: 0.78\n",
            "ROC_AUC-score: 0.7001547987616099\n",
            "**************************************************\n",
            "max_depth: 12\n",
            "[[18 20]\n",
            " [ 4 81]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.82      0.47      0.60        38\n",
            "           Y       0.80      0.95      0.87        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.81      0.71      0.74       123\n",
            "weighted avg       0.81      0.80      0.79       123\n",
            "\n",
            "Training accuracy: :  0.9714867617107943\n",
            "Testing  accuracy: :  0.8048780487804879\n",
            "Cross validation Log Loss: 0.778\n",
            "ROC_AUC-score: 0.7133126934984519\n",
            "**************************************************\n",
            "max_depth: 13\n",
            "[[19 19]\n",
            " [ 5 80]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.79      0.50      0.61        38\n",
            "           Y       0.81      0.94      0.87        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.80      0.72      0.74       123\n",
            "weighted avg       0.80      0.80      0.79       123\n",
            "\n",
            "Training accuracy: :  0.9938900203665988\n",
            "Testing  accuracy: :  0.8048780487804879\n",
            "Cross validation Log Loss: 0.7779999999999999\n",
            "ROC_AUC-score: 0.7205882352941176\n",
            "**************************************************\n",
            "max_depth: 14\n",
            "[[19 19]\n",
            " [ 6 79]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.76      0.50      0.60        38\n",
            "           Y       0.81      0.93      0.86        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.78      0.71      0.73       123\n",
            "weighted avg       0.79      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  0.9979633401221996\n",
            "Testing  accuracy: :  0.7967479674796748\n",
            "Cross validation Log Loss: 0.7739183673469389\n",
            "ROC_AUC-score: 0.7147058823529412\n",
            "**************************************************\n",
            "max_depth: 15\n",
            "[[18 20]\n",
            " [ 5 80]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.78      0.47      0.59        38\n",
            "           Y       0.80      0.94      0.86        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.79      0.71      0.73       123\n",
            "weighted avg       0.79      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  1.0\n",
            "Testing  accuracy: :  0.7967479674796748\n",
            "Cross validation Log Loss: 0.7718775510204081\n",
            "ROC_AUC-score: 0.7074303405572755\n",
            "**************************************************\n",
            "max_depth: 16\n",
            "[[19 19]\n",
            " [ 3 82]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.86      0.50      0.63        38\n",
            "           Y       0.81      0.96      0.88        85\n",
            "\n",
            "    accuracy                           0.82       123\n",
            "   macro avg       0.84      0.73      0.76       123\n",
            "weighted avg       0.83      0.82      0.80       123\n",
            "\n",
            "Training accuracy: :  1.0\n",
            "Testing  accuracy: :  0.8211382113821138\n",
            "Cross validation Log Loss: 0.7800408163265307\n",
            "ROC_AUC-score: 0.7323529411764707\n",
            "**************************************************\n",
            "max_depth: 17\n",
            "[[19 19]\n",
            " [ 6 79]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.76      0.50      0.60        38\n",
            "           Y       0.81      0.93      0.86        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.78      0.71      0.73       123\n",
            "weighted avg       0.79      0.80      0.78       123\n",
            "\n",
            "Training accuracy: :  1.0\n",
            "Testing  accuracy: :  0.7967479674796748\n",
            "Cross validation Log Loss: 0.7759591836734694\n",
            "ROC_AUC-score: 0.7147058823529412\n",
            "**************************************************\n",
            "max_depth: 18\n",
            "[[18 20]\n",
            " [ 4 81]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.82      0.47      0.60        38\n",
            "           Y       0.80      0.95      0.87        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.81      0.71      0.74       123\n",
            "weighted avg       0.81      0.80      0.79       123\n",
            "\n",
            "Training accuracy: :  1.0\n",
            "Testing  accuracy: :  0.8048780487804879\n",
            "Cross validation Log Loss: 0.7698775510204081\n",
            "ROC_AUC-score: 0.7133126934984519\n",
            "**************************************************\n",
            "max_depth: 19\n",
            "[[19 19]\n",
            " [ 4 81]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.83      0.50      0.62        38\n",
            "           Y       0.81      0.95      0.88        85\n",
            "\n",
            "    accuracy                           0.81       123\n",
            "   macro avg       0.82      0.73      0.75       123\n",
            "weighted avg       0.81      0.81      0.80       123\n",
            "\n",
            "Training accuracy: :  1.0\n",
            "Testing  accuracy: :  0.8130081300813008\n",
            "Cross validation Log Loss: 0.7719183673469387\n",
            "ROC_AUC-score: 0.7264705882352941\n",
            "**************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D4NANz98b7VH",
        "colab_type": "code",
        "outputId": "7863248f-1224-44e6-dc70-5e8ad2c5dac0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "rfmd_5_est_300=RandomForestClassifier(random_state=42,max_depth=5,n_estimators=300)\n",
        "build_model(rfmd_5_est_300, X_train, X_test, y_train, y_test)\n",
        "\n",
        "test_preds=rfmd_5_est_300.predict(test_X)\n",
        "\n",
        "sub['Loan_Status']=test_preds\n",
        "sub.to_csv('rfmd_5_est_300.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[17 21]\n",
            " [ 2 83]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.89      0.45      0.60        38\n",
            "           Y       0.80      0.98      0.88        85\n",
            "\n",
            "    accuracy                           0.81       123\n",
            "   macro avg       0.85      0.71      0.74       123\n",
            "weighted avg       0.83      0.81      0.79       123\n",
            "\n",
            "Training accuracy: :  0.835030549898167\n",
            "Testing  accuracy: :  0.8130081300813008\n",
            "Cross validation Log Loss: 0.7962857142857143\n",
            "ROC_AUC-score: 0.7119195046439628\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgGh0Cl-dBZ7",
        "colab_type": "code",
        "outputId": "a2121787-9cbb-4ad8-9fc4-25d23fab93ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.barh(y=X_train.columns,width=rfmd_5_est_300.feature_importances_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 18 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4KnNoKkPd36R",
        "colab_type": "text"
      },
      "source": [
        "# GradientBoosting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nw_moQnedhan",
        "colab_type": "code",
        "outputId": "f24c6b76-df39-4920-c2db-902c27db1155",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "gb=GradientBoostingClassifier(random_state=42)\n",
        "build_model(gb, X_train, X_test, y_train, y_test)\n",
        "\n",
        "test_preds=gb.predict(test_X)\n",
        "\n",
        "sub['Loan_Status']=test_preds\n",
        "sub.to_csv('gb.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[21 17]\n",
            " [ 8 77]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.72      0.55      0.63        38\n",
            "           Y       0.82      0.91      0.86        85\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.77      0.73      0.74       123\n",
            "weighted avg       0.79      0.80      0.79       123\n",
            "\n",
            "Training accuracy: :  0.9103869653767821\n",
            "Testing  accuracy: :  0.7967479674796748\n",
            "Cross validation Log Loss: 0.7656734693877549\n",
            "ROC_AUC-score: 0.7292569659442724\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYb5gKyIeabz",
        "colab_type": "text"
      },
      "source": [
        "# XGBOOST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJU7dKjceZnc",
        "colab_type": "code",
        "outputId": "2c515715-9258-4398-fef8-895a18a97340",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "xg=XGBClassifier(random_state=42)\n",
        "build_model(xg, X_train, X_test, y_train, y_test)\n",
        "\n",
        "test_preds=xg.predict(test_X)\n",
        "\n",
        "sub['Loan_Status']=test_preds\n",
        "sub.to_csv('xg_simple.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[19 19]\n",
            " [ 7 78]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.73      0.50      0.59        38\n",
            "           Y       0.80      0.92      0.86        85\n",
            "\n",
            "    accuracy                           0.79       123\n",
            "   macro avg       0.77      0.71      0.73       123\n",
            "weighted avg       0.78      0.79      0.78       123\n",
            "\n",
            "Training accuracy: :  0.869653767820774\n",
            "Testing  accuracy: :  0.7886178861788617\n",
            "Cross validation Log Loss: 0.767673469387755\n",
            "ROC_AUC-score: 0.7088235294117646\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykQxuE41d_u-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_check=X_test.copy()\n",
        "test_check['predicted']=xg.predict(X_test)\n",
        "test_check['acutal']=y_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NS596EPgc_z",
        "colab_type": "code",
        "outputId": "0a0bdfc4-db6f-4e14-d563-e97dbd58a576",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        }
      },
      "source": [
        "test_check[(test_check['acutal']=='N') & (test_check['predicted']=='Y')]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Gender_Male</th>\n",
              "      <th>Gender_Missing</th>\n",
              "      <th>Married_No</th>\n",
              "      <th>Married_Yes</th>\n",
              "      <th>Dependents_1</th>\n",
              "      <th>Dependents_2</th>\n",
              "      <th>Dependents_3+</th>\n",
              "      <th>Dependents_Missing</th>\n",
              "      <th>Education_Not Graduate</th>\n",
              "      <th>Self_Employed_No</th>\n",
              "      <th>Self_Employed_Yes</th>\n",
              "      <th>Property_Area_Semiurban</th>\n",
              "      <th>Property_Area_Urban</th>\n",
              "      <th>predicted</th>\n",
              "      <th>acutal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>4230</td>\n",
              "      <td>0.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>3717</td>\n",
              "      <td>2925.0</td>\n",
              "      <td>151.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>10000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>155.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>4887</td>\n",
              "      <td>0.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>2600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>4050</td>\n",
              "      <td>5302.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>469</th>\n",
              "      <td>4333</td>\n",
              "      <td>2451.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>447</th>\n",
              "      <td>3539</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>489</th>\n",
              "      <td>2435</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>12000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>164.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>5726</td>\n",
              "      <td>4595.0</td>\n",
              "      <td>258.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>245</th>\n",
              "      <td>6050</td>\n",
              "      <td>4333.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>3867</td>\n",
              "      <td>0.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>600</th>\n",
              "      <td>416</td>\n",
              "      <td>41667.0</td>\n",
              "      <td>350.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>-999.0</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>585</th>\n",
              "      <td>4283</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>3250</td>\n",
              "      <td>0.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>4200</td>\n",
              "      <td>1430.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>6782</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>286</th>\n",
              "      <td>2600</td>\n",
              "      <td>1717.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>1.0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     ApplicantIncome  CoapplicantIncome  ...  predicted  acutal\n",
              "52              4230                0.0  ...          Y       N\n",
              "24              3717             2925.0  ...          Y       N\n",
              "319            10000                0.0  ...          Y       N\n",
              "18              4887                0.0  ...          Y       N\n",
              "416             2600                0.0  ...          Y       N\n",
              "503             4050             5302.0  ...          Y       N\n",
              "469             4333             2451.0  ...          Y       N\n",
              "447             3539             1376.0  ...          Y       N\n",
              "489             2435                0.0  ...          Y       N\n",
              "191            12000                0.0  ...          Y       N\n",
              "65              5726             4595.0  ...          Y       N\n",
              "245             6050             4333.0  ...          Y       N\n",
              "410             3867                0.0  ...          Y       N\n",
              "600              416            41667.0  ...          Y       N\n",
              "585             4283             3000.0  ...          Y       N\n",
              "225             3250                0.0  ...          Y       N\n",
              "139             4200             1430.0  ...          Y       N\n",
              "95              6782                0.0  ...          Y       N\n",
              "286             2600             1717.0  ...          Y       N\n",
              "\n",
              "[19 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-P5jIwzgjjz",
        "colab_type": "code",
        "outputId": "33fb0f44-571c-4e31-ab9f-0c95a3520566",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "xg.feature_importances_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.06204415, 0.05954689, 0.05607348, 0.060676  , 0.372506  ,\n",
              "       0.043451  , 0.        , 0.07113596, 0.        , 0.0499041 ,\n",
              "       0.03116252, 0.03028117, 0.        , 0.07550925, 0.02132848,\n",
              "       0.        , 0.06366228, 0.00271875], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5-bjYyqiHV2",
        "colab_type": "code",
        "outputId": "bcb965b1-5399-46fe-f211-d90cc8a12312",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "source": [
        "plt.figure(figsize=(12,9))\n",
        "plt.barh(X_train.columns,xg.feature_importances_)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ZFAfaB8liJS",
        "colab_type": "text"
      },
      "source": [
        "## Hyperparameter Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khnw72n2iVE5",
        "colab_type": "code",
        "outputId": "a636c5c3-4db3-4c72-d1e4-d54a7fde5d85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "xg.get_params()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'base_score': 0.5,\n",
              " 'booster': 'gbtree',\n",
              " 'colsample_bylevel': 1,\n",
              " 'colsample_bynode': 1,\n",
              " 'colsample_bytree': 1,\n",
              " 'gamma': 0,\n",
              " 'learning_rate': 0.1,\n",
              " 'max_delta_step': 0,\n",
              " 'max_depth': 3,\n",
              " 'min_child_weight': 1,\n",
              " 'missing': None,\n",
              " 'n_estimators': 100,\n",
              " 'n_jobs': 1,\n",
              " 'nthread': None,\n",
              " 'objective': 'binary:logistic',\n",
              " 'random_state': 42,\n",
              " 'reg_alpha': 0,\n",
              " 'reg_lambda': 1,\n",
              " 'scale_pos_weight': 1,\n",
              " 'seed': None,\n",
              " 'silent': None,\n",
              " 'subsample': 1,\n",
              " 'verbosity': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UM3WqPJCmK-A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xg_params={\n",
        "    \"learning_rate\":[0.01,0.015,0.025,0.05,0.1],\n",
        "    \"gamma\":[0.05,0.1,0.3,0.5,0.7,0.9,1.0],\n",
        "    \"max_depth\":[3,5,7,9,12,15,17],\n",
        "    \"min_child_weight\":[1,3,5,7],\n",
        "    \"subsample\":[0.6,0.7,0.8,0.9,1.0],\n",
        "    \"colsample_bytree\":[0.6,0.7,0.8,0.9,1.0],\n",
        "    \"reg_alpha\":[0,0.01,0.1,1.0],\n",
        "    \"reg_lambda\":[0.01,0.1,1.0]\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaOjrsH1nA_q",
        "colab_type": "code",
        "outputId": "2d6dad4a-5cfa-4db1-f3a7-6dba8c239049",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "xg_random=RandomizedSearchCV(xg,param_distributions=xg_params,n_iter=25,cv=10,n_jobs=-1,random_state=42)\n",
        "xg_random.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=10, error_score=nan,\n",
              "                   estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
              "                                           colsample_bylevel=1,\n",
              "                                           colsample_bynode=1,\n",
              "                                           colsample_bytree=1, gamma=0,\n",
              "                                           learning_rate=0.1, max_delta_step=0,\n",
              "                                           max_depth=3, min_child_weight=1,\n",
              "                                           missing=None, n_estimators=100,\n",
              "                                           n_jobs=1, nthread=None,\n",
              "                                           objective='binary:logistic',\n",
              "                                           random_state=42, reg_alpha=0,\n",
              "                                           reg_lambda=1,...\n",
              "                   param_distributions={'colsample_bytree': [0.6, 0.7, 0.8, 0.9,\n",
              "                                                             1.0],\n",
              "                                        'gamma': [0.05, 0.1, 0.3, 0.5, 0.7, 0.9,\n",
              "                                                  1.0],\n",
              "                                        'learning_rate': [0.01, 0.015, 0.025,\n",
              "                                                          0.05, 0.1],\n",
              "                                        'max_depth': [3, 5, 7, 9, 12, 15, 17],\n",
              "                                        'min_child_weight': [1, 3, 5, 7],\n",
              "                                        'reg_alpha': [0, 0.01, 0.1, 1.0],\n",
              "                                        'reg_lambda': [0.01, 0.1, 1.0],\n",
              "                                        'subsample': [0.6, 0.7, 0.8, 0.9, 1.0]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IEJ3NyznSvO",
        "colab_type": "code",
        "outputId": "9fcc6c4a-f611-4a6a-f162-a1e1bfa39394",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "xg_random.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'colsample_bytree': 0.9,\n",
              " 'gamma': 0.7,\n",
              " 'learning_rate': 0.025,\n",
              " 'max_depth': 9,\n",
              " 'min_child_weight': 3,\n",
              " 'reg_alpha': 0.1,\n",
              " 'reg_lambda': 0.1,\n",
              " 'subsample': 0.7}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_YSzlvmrncNk",
        "colab_type": "code",
        "outputId": "3be0eafe-037c-4f70-cb1e-beb78b8c9b47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "xg_random.best_score_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8104897959183675"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZBgaZgCUnkaY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_preds=xg_random.best_estimator_.predict(test_X)\n",
        "\n",
        "sub['Loan_Status']=test_preds\n",
        "sub.to_csv('xg_randomSearch.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ahlO3FEBn0Jm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}