Loan_Prediction.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Loan_Prediction.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7ehhEYz_1t8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzoyXb6sBM7H",
        "colab_type": "code",
        "outputId": "eee21099-ff90-4329-9cc7-cbb7c4b6f987",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        }
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Loan_Prediction_train.csv\")\n",
        "df_test = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Loan_prediction_test.csv\")\n",
        "print(df.shape,\"\\n\")\n",
        "print(df.columns,\"\\n\")\n",
        "print(df.info(),\"\\n\")\n",
        "print(df.isnull().sum())\n"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(614, 13) \n",
            "\n",
            "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
            "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
            "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
            "      dtype='object') \n",
            "\n",
            "<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",
            "None \n",
            "\n",
            "Loan_ID               0\n",
            "Gender               13\n",
            "Married               3\n",
            "Dependents           15\n",
            "Education             0\n",
            "Self_Employed        32\n",
            "ApplicantIncome       0\n",
            "CoapplicantIncome     0\n",
            "LoanAmount           22\n",
            "Loan_Amount_Term     14\n",
            "Credit_History       50\n",
            "Property_Area         0\n",
            "Loan_Status           0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBMPSut8CPlN",
        "colab_type": "code",
        "outputId": "01e074e5-e52b-4028-bae0-bee0080654bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "print(df.head())"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    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]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dLR2jIz3D6BM",
        "colab_type": "code",
        "outputId": "afef59b2-f800-41ff-ee27-448101d24b73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "source": [
        "#GENDER\n",
        "print(df['Gender'].value_counts(),\"\\n\")\n",
        "print(df['Gender'].isnull().sum(),\"\\n\")\n",
        "print(df['Gender'].fillna('Male',inplace=True))\n",
        "print(df['Gender'].isnull().sum(),\"\\n\")\n",
        "df['Gender'] = df['Gender'].apply({'Male':1, 'Female':0}.get)\n",
        "df.head()"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Male      489\n",
            "Female    112\n",
            "Name: Gender, dtype: int64 \n",
            "\n",
            "13 \n",
            "\n",
            "None\n",
            "0 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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>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>1</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>1</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>1</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>1</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>1</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       1      No  ...            1.0         Urban           Y\n",
              "1  LP001003       1     Yes  ...            1.0         Rural           N\n",
              "2  LP001005       1     Yes  ...            1.0         Urban           Y\n",
              "3  LP001006       1     Yes  ...            1.0         Urban           Y\n",
              "4  LP001008       1      No  ...            1.0         Urban           Y\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f6ZIYyYbE4Xt",
        "colab_type": "code",
        "outputId": "e35ee6a2-e9c6-45c7-ec44-14245fa3a4d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "#Married\n",
        "print(df['Married'].isnull().sum(),\"\\n\")\n",
        "print(df['Married'].value_counts(),\"\\n\")\n",
        "print(df['Married'].fillna('Yes',inplace = True))\n",
        "print(df['Married'].isnull().sum(),\"\\n\")\n",
        "df['Married'] = df['Married'].apply({'Yes':1, 'No':0}.get)"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3 \n",
            "\n",
            "Yes    398\n",
            "No     213\n",
            "Name: Married, dtype: int64 \n",
            "\n",
            "None\n",
            "0 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZRr9GQYiJYTO",
        "colab_type": "code",
        "outputId": "493a4c25-3ec8-4ec9-bba5-5be8cf9e6eed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "#Dependents\n",
        "print(df['Dependents'].unique())\n",
        "print(df[\"Dependents\"].isnull().sum())\n",
        "print(df['Dependents'].value_counts())\n",
        "df.drop('Dependents',1,inplace=True)\n",
        "print(df.info())\n"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['0' '1' '2' '3+' nan]\n",
            "15\n",
            "0     345\n",
            "1     102\n",
            "2     101\n",
            "3+     51\n",
            "Name: Dependents, dtype: int64\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 614 entries, 0 to 613\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            614 non-null    object \n",
            " 1   Gender             614 non-null    int64  \n",
            " 2   Married            614 non-null    int64  \n",
            " 3   Education          614 non-null    object \n",
            " 4   Self_Employed      582 non-null    object \n",
            " 5   ApplicantIncome    614 non-null    int64  \n",
            " 6   CoapplicantIncome  614 non-null    float64\n",
            " 7   LoanAmount         592 non-null    float64\n",
            " 8   Loan_Amount_Term   600 non-null    float64\n",
            " 9   Credit_History     564 non-null    float64\n",
            " 10  Property_Area      614 non-null    object \n",
            " 11  Loan_Status        614 non-null    object \n",
            "dtypes: float64(4), int64(3), object(5)\n",
            "memory usage: 57.7+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBMm9G0TKoKx",
        "colab_type": "code",
        "outputId": "4073f4d6-425c-40f3-97bc-a46e36f871ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "\n",
        "\n",
        "#Self Employed\n",
        "print(df['Self_Employed'].isnull().sum())\n",
        "print(df['Self_Employed'].value_counts())\n",
        "print(df['Self_Employed'].fillna('No',inplace=True))\n",
        "print(df['Self_Employed'].isnull().sum())\n",
        "df['Self_Employed'] = df['Self_Employed'].apply({'Yes':1, 'No':0}.get)"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32\n",
            "No     500\n",
            "Yes     82\n",
            "Name: Self_Employed, dtype: int64\n",
            "None\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAT36V3vNhFm",
        "colab_type": "code",
        "outputId": "1a786c2e-eb93-44a7-d2b9-35028e9d5d1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "\n",
        "\n",
        "print(df['ApplicantIncome'].isnull().sum())\n",
        "print(df['CoapplicantIncome'].isnull().sum())"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zqx7mVTuOsuy",
        "colab_type": "code",
        "outputId": "798b9483-f745-4e79-ff7c-5e713474f0b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "#LoanAmount\n",
        "print(df['LoanAmount'].isnull().sum())\n",
        "plt.hist('LoanAmount',bins=4,data = df,alpha=0.5)\n",
        "print(df['LoanAmount'].mean(),df['LoanAmount'].median())\n",
        "print(df['LoanAmount'].fillna(df['LoanAmount'].median(),inplace=True))\n",
        "print(df['LoanAmount'].isnull().sum())"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "22\n",
            "146.41216216216216 128.0\n",
            "None\n",
            "0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
            "  keep = (tmp_a >= first_edge)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
            "  keep &= (tmp_a <= last_edge)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T3wbOmPC8pzH4a+z0obmR5/XjBySpQ6vptIwkaZEsd0nqkOUuSR2y3CWpQ5a7JHXIcpekDlnuktSh/wX41dvjiLzQxQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UDfDxosRWMg",
        "colab_type": "code",
        "outputId": "418c6b26-66c0-4cd9-d938-3f0262c0023b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        }
      },
      "source": [
        "#Loan_Amount_term\n",
        "\n",
        "\n",
        "print(df['Loan_Amount_Term'].unique())\n",
        "print(df['Loan_Amount_Term'].describe())\n",
        "print(df['Loan_Amount_Term'].isnull().sum())\n",
        "plt.hist('Loan_Amount_Term',data=df,alpha=0.5)\n",
        "#plt.boxplot('Loan_Amount_Term',data=df)\n",
        "df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].median(),inplace=True)\n",
        "print(df['Loan_Amount_Term'].isnull().sum())"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[360. 120. 240.  nan 180.  60. 300. 480.  36.  84.  12.]\n",
            "count    600.00000\n",
            "mean     342.00000\n",
            "std       65.12041\n",
            "min       12.00000\n",
            "25%      360.00000\n",
            "50%      360.00000\n",
            "75%      360.00000\n",
            "max      480.00000\n",
            "Name: Loan_Amount_Term, dtype: float64\n",
            "14\n",
            "0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
            "  keep = (tmp_a >= first_edge)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
            "  keep &= (tmp_a <= last_edge)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rapbBZ/7+qvo4Hc1BkjclecuxMvAB4DHG+VmY9i8xFvkFx7XAUwzWOf922uOZ4HF+FXgB+G8G63PbGaxT7gOeBv4dOK/1DYOrnX4GPArMTXv8Y5qD9zFY13wEeLg9ru1pHoA/Ax5qc/AY8Het/e3AD4EF4F+Bs1r72a2+0La/fdrHMOb5uBy4r7c5aMf6k/Z4/Fj2jfOz4C0bJKkzq3mpR5I0AQa/JHXG4Jekzhj8ktQZg1+SOmPwS1JnDH5J6sz/ACW7WvElDd/yAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7otlEjGCUcv_",
        "colab_type": "code",
        "outputId": "bc7fc1e9-3274-467f-f68c-84010bf6f90f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "#CreditHistory\n",
        "df['Credit_History'].unique()\n",
        "plt.hist('Credit_History',data = df)\n",
        "df['Credit_History'].fillna(1,inplace = True)\n",
        "print(df['Credit_History'].isnull().sum())"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
            "  keep = (tmp_a >= first_edge)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
            "  keep &= (tmp_a <= last_edge)\n"
          ],
          "name": "stderr"
        },
        {
          "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": "dFJZQicOwjlf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Credit_History'] = df['Credit_History'].apply(np.int64)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2ulFN2aZHMU",
        "colab_type": "code",
        "outputId": "908e345b-6aca-4920-a9a7-a0dbe4a25fe4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        }
      },
      "source": [
        "print(df.isnull().sum())\n",
        "df.head()"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loan_ID              0\n",
            "Gender               0\n",
            "Married              0\n",
            "Education            0\n",
            "Self_Employed        0\n",
            "ApplicantIncome      0\n",
            "CoapplicantIncome    0\n",
            "LoanAmount           0\n",
            "Loan_Amount_Term     0\n",
            "Credit_History       0\n",
            "Property_Area        0\n",
            "Loan_Status          0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "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>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>0</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</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</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</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</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Not Graduate</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</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</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</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       1        0  ...              1          Urban            Y\n",
              "1  LP001003       1        1  ...              1          Rural            N\n",
              "2  LP001005       1        1  ...              1          Urban            Y\n",
              "3  LP001006       1        1  ...              1          Urban            Y\n",
              "4  LP001008       1        0  ...              1          Urban            Y\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jd_tjwXrafS9",
        "colab_type": "code",
        "outputId": "0ddc6973-f6b9-4c70-cf68-08cd1cf42e7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Property_Area\n",
        "print(df['Property_Area'].unique())\n",
        "df['Property_Area'] = df['Property_Area'].apply({'Urban':0, 'Rural':1,'Semiurban':2}.get)\n",
        "df['Property_Area'] = df['Property_Area'].apply(np.int64)\n"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Urban' 'Rural' 'Semiurban']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "akL4zdonpogE",
        "colab_type": "code",
        "outputId": "c478ea3a-6e0c-47f7-de2a-0266c82114e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 147,
      "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>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>0</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</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</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</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</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Not Graduate</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</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</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</td>\n",
              "      <td>0</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       1        0  ...              1              0            Y\n",
              "1  LP001003       1        1  ...              1              1            N\n",
              "2  LP001005       1        1  ...              1              0            Y\n",
              "3  LP001006       1        1  ...              1              0            Y\n",
              "4  LP001008       1        0  ...              1              0            Y\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DC7AyIQNvRuH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Loan_Status'] = df['Loan_Status'].apply({'Y':1,'N':0}.get)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2zx78UjPv_1r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Education'] = df['Education'].apply({'Graduate':1,'Not Graduate':0}.get)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1P5pNnRHwCJV",
        "colab_type": "code",
        "outputId": "ae110342-dfe1-4949-f679-8bd254cb2da1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 150,
      "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>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Loan_ID  Gender  Married  ...  Credit_History  Property_Area  Loan_Status\n",
              "0  LP001002       1        0  ...               1              0            1\n",
              "1  LP001003       1        1  ...               1              1            0\n",
              "2  LP001005       1        1  ...               1              0            1\n",
              "3  LP001006       1        1  ...               1              0            1\n",
              "4  LP001008       1        0  ...               1              0            1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IcUvNxsjwYbD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#CoapplicantIncome\n",
        "df.drop('CoapplicantIncome',1,inplace=True)\n",
        "df.drop('Loan_ID',1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Gh4mNKsw9d7",
        "colab_type": "code",
        "outputId": "e3a12ac9-f36d-4206-d0b0-1d5bb708f036",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 614 entries, 0 to 613\n",
            "Data columns (total 10 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Gender            614 non-null    int64  \n",
            " 1   Married           614 non-null    int64  \n",
            " 2   Education         614 non-null    int64  \n",
            " 3   Self_Employed     614 non-null    int64  \n",
            " 4   ApplicantIncome   614 non-null    int64  \n",
            " 5   LoanAmount        614 non-null    float64\n",
            " 6   Loan_Amount_Term  614 non-null    float64\n",
            " 7   Credit_History    614 non-null    int64  \n",
            " 8   Property_Area     614 non-null    int64  \n",
            " 9   Loan_Status       614 non-null    int64  \n",
            "dtypes: float64(2), int64(8)\n",
            "memory usage: 48.1 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iidaTTHxZPf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "\n",
        "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEylH8S1y-ye",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df.drop(['Loan_Status'],1)\n",
        "y = df['Loan_Status']\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKnK9_tqzLlt",
        "colab_type": "code",
        "outputId": "3aaa26d6-1369-4c97-d66d-d5278af3cd3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "clf = LogisticRegression()\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)\n",
        "clf.fit(X_train,y_train)\n",
        "ypred = clf.predict(X_test)\n",
        "score = accuracy_score(y_test,ypred)\n",
        "cm = confusion_matrix(y_test,ypred)\n",
        "print(\"PRECISION\",metrics.precision_score(y_test,ypred))\n",
        "print(\"RECALL\",metrics.recall_score(y_test,ypred))\n",
        "print(\"ACCURACY SCORE\",score)\n",
        "\n",
        "#confusion(cm,score)\n"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PRECISION 0.7904761904761904\n",
            "RECALL 1.0\n",
            "ACCURACY SCORE 0.8211382113821138\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnoqlTxiGVog",
        "colab_type": "code",
        "outputId": "6d5a469e-9921-4421-d4b9-34f22a2bd81e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "fpr, tpr, _ = metrics.roc_curve(y_test,  ypred)\n",
        "auc = metrics.roc_auc_score(y_test, ypred)\n",
        "plt.plot(fpr,tpr,label=\" auc=\"+str(auc))\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "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": "9UqygLb9GCG3",
        "colab_type": "code",
        "outputId": "2a9e2040-c938-442f-8939-b6e236c83f26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        }
      },
      "source": [
        "sns.heatmap(cm, annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted label')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        },
        {
          "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": {
        "id": "Ea-OfTN20W-_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Decision Tree\n",
        "from sklearn.tree import DecisionTreeClassifier \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60FyjWYZ3gxH",
        "colab_type": "code",
        "outputId": "3a234702-ea9c-4194-8edd-688317edce91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(X_train,y_train)\n",
        "ypred = clf.predict(X_test)\n",
        "score = accuracy_score(y_test,ypred)\n",
        "cm = confusion_matrix(y_test,ypred)\n",
        "print(score)"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7073170731707317\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lw0y6efU3lFw",
        "colab_type": "code",
        "outputId": "3a693f15-d836-42de-e858-b40f2eda649a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=7)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred = knn.predict(X_test)\n",
        "print(\"Accuracy:\",accuracy_score(y_test, y_pred))\n"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.6422764227642277\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fijxb0Rh4uZE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}