Loan_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Loan_Prediction.ipynb",
      "provenance": [],
      "mount_file_id": "1j2a566z_0ulphLWUp68j6Z8pjMv-kcoq",
      "authorship_tag": "ABX9TyMQs649cc81O+4PpSm+19Gk",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/stefkim/loan_prediction/blob/main/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": "s5zDkN05cfQM"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import warnings\n",
        "%matplotlib inline\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K7D2nVqpckrZ"
      },
      "source": [
        "train = pd.read_csv('/content/drive/MyDrive/loan_prediction/train.csv')"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "SoHwDIXMfCac",
        "outputId": "c37fc5fc-5543-43c0-d515-1cb050726d11"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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",
              "      <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": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ww7ac7Ae-nW",
        "outputId": "c7b2ee2a-6fe3-49ad-b4ae-a7a289ab4270"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 614 entries, 0 to 613\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            614 non-null    object \n",
            " 1   Gender             601 non-null    object \n",
            " 2   Married            611 non-null    object \n",
            " 3   Dependents         599 non-null    object \n",
            " 4   Education          614 non-null    object \n",
            " 5   Self_Employed      582 non-null    object \n",
            " 6   ApplicantIncome    614 non-null    int64  \n",
            " 7   CoapplicantIncome  614 non-null    float64\n",
            " 8   LoanAmount         592 non-null    float64\n",
            " 9   Loan_Amount_Term   600 non-null    float64\n",
            " 10  Credit_History     564 non-null    float64\n",
            " 11  Property_Area      614 non-null    object \n",
            " 12  Loan_Status        614 non-null    object \n",
            "dtypes: float64(4), int64(1), object(8)\n",
            "memory usage: 62.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "RgcBCxd8dbvs",
        "outputId": "3aa5055f-7064-41f9-cfbb-928297fe5a3a"
      },
      "source": [
        "missing_values_total = train.isnull().sum().sort_values(ascending=False)\n",
        "missing_values_pct = (train.isnull().mean()*100).sort_values(ascending=False)\n",
        "\n",
        "missing_values = pd.concat([missing_values_total, missing_values_pct, train.dtypes],\n",
        "                           keys=['Total','Percentage','Dtype'],\n",
        "                           axis=1)\n",
        "missing_values.head(10)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total</th>\n",
              "      <th>Percentage</th>\n",
              "      <th>Dtype</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Credit_History</th>\n",
              "      <td>50</td>\n",
              "      <td>8.143322</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Self_Employed</th>\n",
              "      <td>32</td>\n",
              "      <td>5.211726</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LoanAmount</th>\n",
              "      <td>22</td>\n",
              "      <td>3.583062</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents</th>\n",
              "      <td>15</td>\n",
              "      <td>2.442997</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <td>14</td>\n",
              "      <td>2.280130</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>13</td>\n",
              "      <td>2.117264</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Married</th>\n",
              "      <td>3</td>\n",
              "      <td>0.488599</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan_Status</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Property_Area</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Total  Percentage    Dtype\n",
              "Credit_History        50    8.143322  float64\n",
              "Self_Employed         32    5.211726   object\n",
              "LoanAmount            22    3.583062  float64\n",
              "Dependents            15    2.442997   object\n",
              "Loan_Amount_Term      14    2.280130  float64\n",
              "Gender                13    2.117264   object\n",
              "Married                3    0.488599   object\n",
              "Loan_Status            0    0.000000   object\n",
              "Property_Area          0    0.000000   object\n",
              "CoapplicantIncome      0    0.000000  float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4T7kE_YKjIZW",
        "outputId": "c169dab3-9027-4496-b104-15f013650ffc"
      },
      "source": [
        "train.describe()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>614.000000</td>\n",
              "      <td>614.000000</td>\n",
              "      <td>592.000000</td>\n",
              "      <td>600.00000</td>\n",
              "      <td>564.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5403.459283</td>\n",
              "      <td>1621.245798</td>\n",
              "      <td>146.412162</td>\n",
              "      <td>342.00000</td>\n",
              "      <td>0.842199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6109.041673</td>\n",
              "      <td>2926.248369</td>\n",
              "      <td>85.587325</td>\n",
              "      <td>65.12041</td>\n",
              "      <td>0.364878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>12.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2877.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3812.500000</td>\n",
              "      <td>1188.500000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5795.000000</td>\n",
              "      <td>2297.250000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>81000.000000</td>\n",
              "      <td>41667.000000</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>480.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       ApplicantIncome  CoapplicantIncome  ...  Loan_Amount_Term  Credit_History\n",
              "count       614.000000         614.000000  ...         600.00000      564.000000\n",
              "mean       5403.459283        1621.245798  ...         342.00000        0.842199\n",
              "std        6109.041673        2926.248369  ...          65.12041        0.364878\n",
              "min         150.000000           0.000000  ...          12.00000        0.000000\n",
              "25%        2877.500000           0.000000  ...         360.00000        1.000000\n",
              "50%        3812.500000        1188.500000  ...         360.00000        1.000000\n",
              "75%        5795.000000        2297.250000  ...         360.00000        1.000000\n",
              "max       81000.000000       41667.000000  ...         480.00000        1.000000\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Wgu8JwDtmGHH",
        "outputId": "7d3bc59a-754a-4e45-c00a-ff221459bd06"
      },
      "source": [
        "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(train[['ApplicantIncome','CoapplicantIncome']]))"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f40ad3c85d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "NMUFNaJNhbgP",
        "outputId": "a3a53b92-63b5-45dd-fb9e-7b796931fb00"
      },
      "source": [
        "matrix = np.triu(train.corr())\n",
        "sns.heatmap(train.corr(), annot=True, mask=matrix)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f40b316d950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "5nBSj23BoJKo",
        "outputId": "ef714de9-e18a-426a-a1a5-8a116b43a626"
      },
      "source": [
        "sns.countplot(train['Loan_Status'])"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f40ad535190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        },
        {
          "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": "code",
      "metadata": {
        "id": "oOuoRYXjkMJw"
      },
      "source": [
        "df = train.copy()\n",
        "df.drop(columns='Loan_ID',inplace=True)"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CkWoGU5Ljb4N"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "np.random.seed(99)\n",
        "df[df.select_dtypes(['object']).columns] = df.select_dtypes(['object']).apply(lambda x: le.fit_transform(x.astype(str)))"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Mv8UIkUpjk9o",
        "outputId": "1de0f653-b929-4534-e4c2-af1378dd3dc7"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 83,
      "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>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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Married  Dependents  ...  Credit_History  Property_Area  Loan_Status\n",
              "0       1        0           0  ...             1.0              2            1\n",
              "1       1        1           1  ...             1.0              0            0\n",
              "2       1        1           0  ...             1.0              2            1\n",
              "3       1        1           0  ...             1.0              2            1\n",
              "4       1        0           0  ...             1.0              2            1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgQytj7tkYNU"
      },
      "source": [
        "y = df['Loan_Status']\n",
        "X = df.drop(columns='Loan_Status')"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "JxA2X6zMkgks",
        "outputId": "94fb7cf8-ffb5-4a9e-8944-e18aea0d3a2b"
      },
      "source": [
        "from sklearn.experimental import enable_iterative_imputer  \n",
        "from sklearn.impute import SimpleImputer, IterativeImputer\n",
        "\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "from sklearn.linear_model import BayesianRidge\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "\n",
        "df_y = df['Loan_Status']\n",
        "df_X = df.drop(columns='Loan_Status')\n",
        "\n",
        "N_SPLITS = 5\n",
        "br_estimator = BayesianRidge()\n",
        "\n",
        "np.random.seed(1772023)\n",
        "\n",
        "# Estimate the score after imputation (mean and median strategies)\n",
        "score_simple_imputer = pd.DataFrame()\n",
        "for strategy in ('mean', 'median'):\n",
        "    estimator = make_pipeline(\n",
        "        StandardScaler(),\n",
        "        SimpleImputer(missing_values=np.nan, strategy=strategy),\n",
        "        br_estimator\n",
        "    )\n",
        "    score_simple_imputer[strategy] = cross_val_score(\n",
        "        estimator, df_X, df_y, scoring='neg_mean_squared_error',\n",
        "        cv=N_SPLITS\n",
        "    )\n",
        "\n",
        "# Neigbors\n",
        "nn = round(np.sqrt(12))\n",
        "\n",
        "# Estimate the score after iterative imputation of the missing values\n",
        "# with different estimators\n",
        "estimators = [\n",
        "    BayesianRidge(),\n",
        "    DecisionTreeRegressor(max_features='sqrt', random_state=0),\n",
        "    ExtraTreesRegressor(n_estimators=10, random_state=0),\n",
        "    KNeighborsRegressor(n_neighbors=nn)\n",
        "]\n",
        "\n",
        "score_iterative_imputer = pd.DataFrame()\n",
        "for impute_estimator in estimators:\n",
        "    estimator = make_pipeline(\n",
        "        StandardScaler(),\n",
        "        IterativeImputer(random_state=0, estimator=impute_estimator),\n",
        "        br_estimator\n",
        "    )\n",
        "    score_iterative_imputer[impute_estimator.__class__.__name__] = \\\n",
        "        cross_val_score(\n",
        "            estimator, df_X, df_y, scoring='neg_mean_squared_error',\n",
        "            cv=N_SPLITS\n",
        "    )\n",
        "\n",
        "scores = pd.concat(\n",
        "    [score_simple_imputer, score_iterative_imputer],\n",
        "    keys=['SimpleImputer', 'IterativeImputer'], axis=1\n",
        ")\n",
        "\n",
        "# plot results\n",
        "fig, ax = plt.subplots(figsize=(13, 6))\n",
        "means = -scores.mean().sort_values()\n",
        "errors = scores.std().sort_values()\n",
        "means.plot.barh(xerr=errors, ax=ax)\n",
        "ax.set_title('Imputation Method Comparison using Various Models')\n",
        "ax.set_xlabel('MSE (smaller is better)')\n",
        "ax.set_yticks(np.arange(means.shape[0]))\n",
        "ax.set_yticklabels([\" - \".join(label) for label in means.index.tolist()])\n",
        "plt.tight_layout(pad=1)\n",
        "plt.show()"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6uyqMjzLktV9",
        "outputId": "a26ba524-d112-4659-b565-98fddbbedca4"
      },
      "source": [
        "-scores.mean().sort_values(ascending=False)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IterativeImputer  KNeighborsRegressor      0.156014\n",
              "SimpleImputer     mean                     0.156093\n",
              "IterativeImputer  BayesianRidge            0.156171\n",
              "SimpleImputer     median                   0.156250\n",
              "IterativeImputer  ExtraTreesRegressor      0.157984\n",
              "                  DecisionTreeRegressor    0.159727\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5S7jJIeam9nn"
      },
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
        "\n",
        "np.random.seed(1772023)\n",
        "X_imp = imputer.fit_transform(X)"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9SxSndkNnQW-",
        "outputId": "6ab19fcc-996a-4d8d-e865-38343ec65aa6"
      },
      "source": [
        "X_imp"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  1.,   0.,   0., ..., 360.,   1.,   2.],\n",
              "       [  1.,   1.,   1., ..., 360.,   1.,   0.],\n",
              "       [  1.,   1.,   0., ..., 360.,   1.,   2.],\n",
              "       ...,\n",
              "       [  1.,   1.,   1., ..., 360.,   1.,   2.],\n",
              "       [  1.,   1.,   2., ..., 360.,   1.,   2.],\n",
              "       [  0.,   0.,   0., ..., 360.,   0.,   1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KeV0_9yMn4V9"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "np.random.seed(1772023)\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X_imp)"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AaoY5Zs9n7O4"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, \n",
        "                                                    y, \n",
        "                                                    test_size=0.25, \n",
        "                                                    random_state=1772023)"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9KDc_8-1oASG"
      },
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5, random_state=1772023, shuffle=True)\n",
        "rf = RandomForestClassifier()\n",
        "lr = LogisticRegression()"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zuJ5jWJuoCpo",
        "outputId": "0c430dfd-6430-4fd0-d52a-0128f64d0cb7"
      },
      "source": [
        "scores = pd.DataFrame()\n",
        "np.random.seed(1772023)\n",
        "for scoring in ['accuracy','f1','roc_auc']:\n",
        "    scores[scoring] = cross_val_score(rf,\n",
        "                                      X_train, \n",
        "                                      y_train,\n",
        "                                      scoring=scoring,\n",
        "                                      cv=cv, n_jobs=-1)\n",
        "scores.mean()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "accuracy    0.784783\n",
              "f1          0.866636\n",
              "roc_auc     0.724983\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbGlfK3goZjp",
        "outputId": "512dc855-5d6f-4113-dd54-89b3eacdb7b3"
      },
      "source": [
        "scores = pd.DataFrame()\n",
        "np.random.seed(1772023)\n",
        "for scoring in ['accuracy','f1','roc_auc']:\n",
        "    scores[scoring] = cross_val_score(lr,\n",
        "                                      X_train,\n",
        "                                      y_train,\n",
        "                                      scoring=scoring,\n",
        "                                      cv=cv, n_jobs=-1)\n",
        "scores.mean()"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "accuracy    0.802174\n",
              "f1          0.872024\n",
              "roc_auc     0.719812\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o1o9Bvc8ofyC"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE \n",
        "\n",
        "smote = SMOTE(k_neighbors=2)\n",
        "np.random.seed(1772023)\n",
        "X_smote, y_smote = smote.fit_resample(X_scaled, y)"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "jcHZnUXxoxs7",
        "outputId": "cb8590e8-6a4c-43dd-f90c-8c2fb6980a83"
      },
      "source": [
        "sns.countplot(y_smote)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f40acfad890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        },
        {
          "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": "code",
      "metadata": {
        "id": "F-YKvE1bo3t9"
      },
      "source": [
        "X_train_sm, X_test_sm, y_train_sm, y_test_sm = train_test_split(X_smote, \n",
        "                                                                y_smote, \n",
        "                                                                test_size=0.25, \n",
        "                                                                random_state=1772023)"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1sweHlL9o7Rd",
        "outputId": "b089cbe2-61d9-45d5-d351-deee23f2129a"
      },
      "source": [
        "scores = pd.DataFrame()\n",
        "np.random.seed(1772023)\n",
        "for scoring in ['accuracy','f1','roc_auc']:\n",
        "    scores[scoring] = cross_val_score(rf, \n",
        "                                      X_train_sm, \n",
        "                                      y_train_sm, \n",
        "                                      scoring=scoring, \n",
        "                                      cv=cv, n_jobs=-1)\n",
        "scores.mean()"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "accuracy    0.815086\n",
              "f1          0.804528\n",
              "roc_auc     0.886482\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v5KqZ9oYo7O5",
        "outputId": "23b31d95-4704-4ae1-a2ac-8aac611c7fc9"
      },
      "source": [
        "scores = pd.DataFrame()\n",
        "np.random.seed(1772023)\n",
        "for scoring in ['accuracy','f1','roc_auc']:\n",
        "    scores[scoring] = cross_val_score(lr,\n",
        "                                      X_train_sm,\n",
        "                                      y_train_sm, \n",
        "                                      scoring=scoring, \n",
        "                                      cv=cv, n_jobs=-1)\n",
        "scores.mean()"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "accuracy    0.673003\n",
              "f1          0.725512\n",
              "roc_auc     0.706720\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HF856rPju5zl",
        "outputId": "7cbd59c3-615e-423f-a28c-ab853c8ff0fa"
      },
      "source": [
        "! cwd /content/drive/MyDrive/loan_prediction"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/bin/bash: cwd: command not found\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cuL1EsgQu-yn",
        "outputId": "748e5876-8dfc-4c37-b2a9-49316ee2f75e"
      },
      "source": [
        "!pwd"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}