Loan_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    },
    "colab": {
      "name": "Loan_Prediction.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Amangiri99/Loan-Prediction/blob/master/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": "CCQXivtutSVo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import style\n",
        "plt.style.use(['dark_background'])\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set(color_codes = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WNQIea2tSV5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('train.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6beke9htSWG",
        "colab_type": "code",
        "colab": {},
        "outputId": "08c54819-72e3-45df-9585-a2a14adb3b09"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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>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",
              "      <td>0</td>\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",
              "      <td>1</td>\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",
              "      <td>2</td>\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",
              "      <td>3</td>\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",
              "      <td>4</td>\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 Dependents     Education Self_Employed  \\\n",
              "0  LP001002   Male      No          0      Graduate            No   \n",
              "1  LP001003   Male     Yes          1      Graduate            No   \n",
              "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
              "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
              "4  LP001008   Male      No          0      Graduate            No   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0             5849                0.0         NaN             360.0   \n",
              "1             4583             1508.0       128.0             360.0   \n",
              "2             3000                0.0        66.0             360.0   \n",
              "3             2583             2358.0       120.0             360.0   \n",
              "4             6000                0.0       141.0             360.0   \n",
              "\n",
              "   Credit_History Property_Area Loan_Status  \n",
              "0             1.0         Urban           Y  \n",
              "1             1.0         Rural           N  \n",
              "2             1.0         Urban           Y  \n",
              "3             1.0         Urban           Y  \n",
              "4             1.0         Urban           Y  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4In2hRYtSWU",
        "colab_type": "code",
        "colab": {},
        "outputId": "cbd0a683-e77f-4aa7-9d47-44434f705205"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 614 entries, 0 to 613\n",
            "Data columns (total 13 columns):\n",
            "Loan_ID              614 non-null object\n",
            "Gender               601 non-null object\n",
            "Married              611 non-null object\n",
            "Dependents           599 non-null object\n",
            "Education            614 non-null object\n",
            "Self_Employed        582 non-null object\n",
            "ApplicantIncome      614 non-null int64\n",
            "CoapplicantIncome    614 non-null float64\n",
            "LoanAmount           592 non-null float64\n",
            "Loan_Amount_Term     600 non-null float64\n",
            "Credit_History       564 non-null float64\n",
            "Property_Area        614 non-null object\n",
            "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": "markdown",
      "metadata": {
        "id": "GC2shrJ-tSWf",
        "colab_type": "text"
      },
      "source": [
        "# Data Cleaning and EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TzJUYXiltSWj",
        "colab_type": "text"
      },
      "source": [
        "## Working with Gender "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2YKVL61tSWn",
        "colab_type": "code",
        "colab": {},
        "outputId": "ca755a9a-be33-4a2a-96f6-eddf933433cc"
      },
      "source": [
        "print(\"Uniques in the data set {} \".format(df['Gender'].unique()))\n",
        "\n",
        "#Filling the missing values in Gender\n",
        "df['Gender'].fillna(method = 'ffill',inplace = True)\n",
        "print(\"After filling the nan values {}\".format(df['Gender'].unique()))\n",
        "\n",
        "\n",
        "#Checking the count of each Loan_Status\n",
        "p = df.groupby('Gender')['Gender'].count()\n",
        "sns.barplot(x = p.index,y = p.values);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uniques in the data set ['Male' 'Female' nan] \n",
            "After filling the nan values ['Male' 'Female']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vbPa6XdEtSW0",
        "colab_type": "text"
      },
      "source": [
        "Most of the applicants are male as compared to Female"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cDNiAKsntSW2",
        "colab_type": "text"
      },
      "source": [
        "## Working with Married Status"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnJYCZDStSW4",
        "colab_type": "code",
        "colab": {},
        "outputId": "7fe81118-8ba7-45fe-9c76-83fb03dffcff"
      },
      "source": [
        "print(\"Uniques in the dataset {}\" .format(df['Married'].unique()))\n",
        "\n",
        "#Filling the missing values in Married\n",
        "df['Married'].fillna(method = 'ffill',inplace = True)\n",
        "print(\"After replacing the nan values {}\".format(df['Married'].unique()))\n",
        "\n",
        "\n",
        "#Checking the count of Marraige Status\n",
        "p = df.groupby('Married')['Married'].count()\n",
        "sns.barplot(x = p.index,y = p.values);\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uniques in the dataset ['No' 'Yes' nan]\n",
            "After replacing the nan values ['No' 'Yes']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lTlVlP11tSXF",
        "colab_type": "text"
      },
      "source": [
        "Most of the applicants are Married"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kkqldCB-tSXI",
        "colab_type": "text"
      },
      "source": [
        "## Working with the dependents on the applicant"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ff8M7yZCtSXK",
        "colab_type": "code",
        "colab": {},
        "outputId": "0e6015dd-5b8e-474f-8406-57b728479fff"
      },
      "source": [
        "print(\"Unique values in dataset for dependents {}\".format(df['Dependents'].unique()))\n",
        "\n",
        "#Filling the missing values in Married\n",
        "df['Dependents'].fillna(method = 'ffill',inplace = True)\n",
        "print(\"After removing nan values {}\".format(train['Dependents'].unique()))\n",
        "\n",
        "p = df.groupby('Dependents')['Dependents'].count()\n",
        "\n",
        "plt.pie(p , labels = p.index , autopct = \"%.2f\" , wedgeprops = dict(width = 0.25))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unique values in dataset for dependents ['0' '1' '2' '3+' nan]\n",
            "After removing nan values ['1' '0' '2' '3+']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jx2U6SaqtSXe",
        "colab_type": "text"
      },
      "source": [
        "There are 57.6% of applicants who have no  dependents.\n",
        "\n",
        "There are 16.9% of applicants have 1 dependent.\n",
        "\n",
        "There are 17.1% of applicants have 2 dependents.\n",
        "\n",
        "There are 8.3% of applicants have more tha 3+ dependents.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAKYaHKntSXh",
        "colab_type": "text"
      },
      "source": [
        "## Educational Qualification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ya-csmVMtSXj",
        "colab_type": "code",
        "colab": {},
        "outputId": "2d62a834-2c6d-43b2-8049-aab648573f55"
      },
      "source": [
        "print(\"Uniques in the dataset depending upon education {}\".format(df['Education'].unique()))\n",
        "p = df.groupby('Education')['Education'].count()\n",
        "sns.barplot(x = p.index,y = p.values);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uniques in the dataset depending upon education ['Graduate' 'Not Graduate']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0L4R7NvtSXw",
        "colab_type": "text"
      },
      "source": [
        "Most of the applicants are Graduate as compared to others"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kSXQLwi_tSXy",
        "colab_type": "text"
      },
      "source": [
        "## Applicant is Self Employed or not"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTXj7rlZtSX0",
        "colab_type": "code",
        "colab": {},
        "outputId": "36d2edb6-276b-4f0a-f8f9-15236e5423f5"
      },
      "source": [
        "print(\"Number of uniques in the dataset depending upon Self_Employed or not {}\".format(df['Self_Employed'].unique()))\n",
        "\n",
        "df['Self_Employed'].fillna(method = 'bfill',inplace = True)\n",
        "print(\"After filling the nan values {}\".format(df['Self_Employed'].unique()))\n",
        "\n",
        "p = df.groupby('Self_Employed')['Self_Employed'].count()\n",
        "sns.barplot(x = p.index,y = p.values);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of uniques in the dataset depending upon Self_Employed or not ['No' 'Yes' nan]\n",
            "After filling the nan values ['No' 'Yes']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ShDyOGatSX_",
        "colab_type": "text"
      },
      "source": [
        "Most of the people are not self employed. Exactly the same happens in real world most of the people are not self employed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2RKZ1JpqtSYA",
        "colab_type": "text"
      },
      "source": [
        "## Working with Applicants Income"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UL26eMOjtSYG",
        "colab_type": "code",
        "colab": {},
        "outputId": "bde154ab-916b-44f0-f73e-c3f9525c7a26"
      },
      "source": [
        "sns.distplot(df['ApplicantIncome'],kde = True);"
      ],
      "execution_count": null,
      "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": "markdown",
      "metadata": {
        "id": "vCo7jGLQtSYS",
        "colab_type": "text"
      },
      "source": [
        "Right-Skewed DataSet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EhXluSFVtSYU",
        "colab_type": "code",
        "colab": {},
        "outputId": "a565634a-74aa-4a57-a079-9475183364e4"
      },
      "source": [
        "sns.boxplot(df['ApplicantIncome']);"
      ],
      "execution_count": null,
      "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": "markdown",
      "metadata": {
        "id": "XuEJhkUftSYf",
        "colab_type": "text"
      },
      "source": [
        "There are a lot of outliers in the data set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSoHMwnwtSYh",
        "colab_type": "code",
        "colab": {},
        "outputId": "81a625a0-b717-4bee-a21c-b98ace5cff43"
      },
      "source": [
        "df['ApplicantIncome'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      614.000000\n",
              "mean      5403.459283\n",
              "std       6109.041673\n",
              "min        150.000000\n",
              "25%       2877.500000\n",
              "50%       3812.500000\n",
              "75%       5795.000000\n",
              "max      81000.000000\n",
              "Name: ApplicantIncome, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwdpv_IFtSYt",
        "colab_type": "code",
        "colab": {},
        "outputId": "548b363f-c388-4d78-dced-7fb7cd9e4990"
      },
      "source": [
        "sns.boxplot(np.log(df['ApplicantIncome']));"
      ],
      "execution_count": null,
      "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": "markdown",
      "metadata": {
        "id": "f1t0W4W8tSY6",
        "colab_type": "text"
      },
      "source": [
        "Now Dropping the outliers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mja2OWBtSY8",
        "colab_type": "code",
        "colab": {},
        "outputId": "7fdcacce-a11b-40bc-9070-282869423407"
      },
      "source": [
        "print(df['ApplicantIncome'].dtype)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZbhEvhxWtSZF",
        "colab_type": "code",
        "colab": {},
        "outputId": "5be54b57-f327-4a3b-9e54-052e7ee9cda9"
      },
      "source": [
        "#Removing Outliers Dependin upon quantile\n",
        "max_threshold = df['ApplicantIncome'].quantile(0.85)\n",
        "min_threshold = df['ApplicantIncome'].quantile(0.01)\n",
        "df = df[(df['ApplicantIncome'] < max_threshold) & (df['ApplicantIncome'] > min_threshold)]\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(514, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kl0q8KTytSZQ",
        "colab_type": "code",
        "colab": {},
        "outputId": "1c6a6267-4c3a-4d09-ad29-a16aeb40efe0"
      },
      "source": [
        "sns.boxplot(df['ApplicantIncome']);"
      ],
      "execution_count": null,
      "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": "LggZAeaotSZZ",
        "colab_type": "code",
        "colab": {},
        "outputId": "a29872f2-1a3e-4873-bd72-2533fa71d144"
      },
      "source": [
        "sns.distplot(df['ApplicantIncome'],kde = True);"
      ],
      "execution_count": null,
      "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": "markdown",
      "metadata": {
        "id": "8UAtpR8GtSZi",
        "colab_type": "text"
      },
      "source": [
        "## Co apllicant income"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aG78hvljtSZk",
        "colab_type": "code",
        "colab": {},
        "outputId": "f448865d-0da6-4915-e0c9-ff4832ecdd21"
      },
      "source": [
        "df['CoapplicantIncome'].isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H45bTgNetSZv",
        "colab_type": "code",
        "colab": {},
        "outputId": "1509025b-e7dc-48c7-f102-0e8cb6b0ed2f"
      },
      "source": [
        "df.groupby(train['CoapplicantIncome'])['CoapplicantIncome'].count()[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "152"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVR_eMWEtSZ4",
        "colab_type": "text"
      },
      "source": [
        "In real case also most of the co-applicants are housewives \n",
        "As most of the Co-Applicant Income is 0 so that leads to Outliers in the dataset. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZV34J2PYtSZ6",
        "colab_type": "text"
      },
      "source": [
        "## Amount of Loan Required"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLWyLrf3tSZ8",
        "colab_type": "code",
        "colab": {},
        "outputId": "a26fb974-60ff-496d-bc50-84074a0d1faa"
      },
      "source": [
        "df['LoanAmount'].isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ogU5tO7vtSaG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df[df['LoanAmount'] > 0 ]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cT_8Z9-XtSaP",
        "colab_type": "code",
        "colab": {},
        "outputId": "77a56b62-af5a-494e-8b05-f6a8d7d9d462"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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>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",
              "      <td>1</td>\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",
              "      <td>2</td>\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",
              "      <td>3</td>\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",
              "      <td>4</td>\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",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>LP001011</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</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 Dependents     Education Self_Employed  \\\n",
              "1  LP001003   Male     Yes          1      Graduate            No   \n",
              "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
              "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
              "4  LP001008   Male      No          0      Graduate            No   \n",
              "5  LP001011   Male     Yes          2      Graduate           Yes   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "1             4583             1508.0       128.0             360.0   \n",
              "2             3000                0.0        66.0             360.0   \n",
              "3             2583             2358.0       120.0             360.0   \n",
              "4             6000                0.0       141.0             360.0   \n",
              "5             5417             4196.0       267.0             360.0   \n",
              "\n",
              "   Credit_History Property_Area Loan_Status  \n",
              "1             1.0         Rural           N  \n",
              "2             1.0         Urban           Y  \n",
              "3             1.0         Urban           Y  \n",
              "4             1.0         Urban           Y  \n",
              "5             1.0         Urban           Y  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-3nfEP6tSaV",
        "colab_type": "code",
        "colab": {},
        "outputId": "71f1b171-c91c-4328-ec74-64feb3722492"
      },
      "source": [
        "sns.kdeplot(df['LoanAmount'],shade = True);"
      ],
      "execution_count": null,
      "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": "markdown",
      "metadata": {
        "id": "4Ip9XMlptSaa",
        "colab_type": "text"
      },
      "source": [
        "## Loan tenure "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tA1Bu1N1tSab",
        "colab_type": "code",
        "colab": {},
        "outputId": "932afebd-a8c5-4aed-9414-774e371254ee"
      },
      "source": [
        "print(\"Number of uniques in the data {}\".format(df['Loan_Amount_Term'].unique()))\n",
        "\n",
        "df['Loan_Amount_Term'].fillna(value = df['Loan_Amount_Term'].median(),inplace = True)\n",
        "print(\"After removing nan values {}\".format(df['Loan_Amount_Term'].unique()))\n",
        "\n",
        "#Converting Loan_AMOUNT_Term from months to years\n",
        "df['Loan_Amount_Term'] = df['Loan_Amount_Term']//12 \n",
        "\n",
        "\n",
        "p = df.groupby('Loan_Amount_Term')['Loan_Amount_Term'].count()\n",
        "sns.barplot(x= p.index , y = p.values);\n",
        "print(p)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of uniques in the data [360. 120. 240.  nan 180.  60. 300. 480.  36.  84.  12.]\n",
            "After removing nan values [360. 120. 240. 180.  60. 300. 480.  36.  84.  12.]\n",
            "Loan_Amount_Term\n",
            "1.0       1\n",
            "3.0       2\n",
            "5.0       2\n",
            "7.0       4\n",
            "10.0      3\n",
            "15.0     32\n",
            "20.0      3\n",
            "25.0     10\n",
            "30.0    425\n",
            "40.0     13\n",
            "Name: Loan_Amount_Term, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V2cZkUyItSag",
        "colab_type": "text"
      },
      "source": [
        "Most of the people like to take loan with large tenure."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkJExsU9tSah",
        "colab_type": "text"
      },
      "source": [
        "## Credit History Of Applicant"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4p_-fP1btSah",
        "colab_type": "code",
        "colab": {},
        "outputId": "be688c5c-f602-48e2-e111-3451fd7e6ff6"
      },
      "source": [
        "df['Credit_History'].unique()\n",
        "\n",
        "\n",
        "df['Credit_History'].fillna(method = 'ffill',inplace = True)\n",
        "df['Credit_History'].unique()\n",
        "\n",
        "\n",
        "p = df.groupby('Credit_History')['Credit_History'].count()\n",
        "sns.barplot(x= p.index , y = p.values);\n",
        "print(p)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Credit_History\n",
            "0.0     81\n",
            "1.0    414\n",
            "Name: Credit_History, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgUZ0rhJtSal",
        "colab_type": "text"
      },
      "source": [
        "A lot of people have a Good Credit History"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SkVQ-S9wtSal",
        "colab_type": "text"
      },
      "source": [
        "## The Type of area for which the loan has been taken "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8R-UkCHgtSam",
        "colab_type": "code",
        "colab": {},
        "outputId": "468517e7-fb72-4fb1-c22c-8b83698e8fcb"
      },
      "source": [
        "df['Property_Area'].unique()\n",
        "\n",
        "\n",
        "p = df.groupby('Property_Area')['Property_Area'].count()\n",
        "sns.barplot(x= p.index , y = p.values);"
      ],
      "execution_count": null,
      "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": "gJ3YBKVWtSaq",
        "colab_type": "code",
        "colab": {},
        "outputId": "cb9886f5-525a-4603-baa0-96f4595ff5f7"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 495 entries, 1 to 613\n",
            "Data columns (total 13 columns):\n",
            "Loan_ID              495 non-null object\n",
            "Gender               495 non-null object\n",
            "Married              495 non-null object\n",
            "Dependents           495 non-null object\n",
            "Education            495 non-null object\n",
            "Self_Employed        495 non-null object\n",
            "ApplicantIncome      495 non-null int64\n",
            "CoapplicantIncome    495 non-null float64\n",
            "LoanAmount           495 non-null float64\n",
            "Loan_Amount_Term     495 non-null float64\n",
            "Credit_History       495 non-null float64\n",
            "Property_Area        495 non-null object\n",
            "Loan_Status          495 non-null object\n",
            "dtypes: float64(4), int64(1), object(8)\n",
            "memory usage: 54.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h7z9_SlitSau",
        "colab_type": "text"
      },
      "source": [
        "## Loan Status of applicants"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIBwYrJitSav",
        "colab_type": "code",
        "colab": {},
        "outputId": "043ef43a-ae3b-4601-e2b1-65c8b6e3fc35"
      },
      "source": [
        "print(\"Unique values in the column for Loan_Status {}\".format(df['Loan_Status'].unique()))\n",
        "\n",
        "p = df.groupby('Loan_Status')['Loan_Status'].count()\n",
        "sns.barplot(x = p.index , y = p.values);\n",
        "\n",
        "p_total = sum(p.values)\n",
        "for value in p.values:\n",
        "    print(value * 100 / p_total)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unique values in the column for Loan_Status ['N' 'Y']\n",
            "30.505050505050505\n",
            "69.4949494949495\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trTSlTEStSay",
        "colab_type": "text"
      },
      "source": [
        "There is roughly 70:30 ratio of getting the loan approved. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JD0K68PBtSaz",
        "colab_type": "text"
      },
      "source": [
        "## Income of Male as compared to Female"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfQy6N07tSaz",
        "colab_type": "code",
        "colab": {},
        "outputId": "9b497861-c43f-4588-ff32-d54104217d06"
      },
      "source": [
        "df.groupby('Gender')['ApplicantIncome'].median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender\n",
              "Female    3422.5\n",
              "Male      3547.0\n",
              "Name: ApplicantIncome, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "66BkEoaZtSa2",
        "colab_type": "code",
        "colab": {},
        "outputId": "533e0fc3-130e-4c81-b710-61b7daa83145"
      },
      "source": [
        "df.groupby('Gender')['ApplicantIncome'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender\n",
              "Female    3559.106383\n",
              "Male      3844.381546\n",
              "Name: ApplicantIncome, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-XR3Z2ItSa5",
        "colab_type": "text"
      },
      "source": [
        "Male Applicants have an higher income as compared to female apllicants"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ESo-uv7ctSa6",
        "colab_type": "text"
      },
      "source": [
        "## Depending upon gender who has higher ratio of getting loan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBLaVOvFtSa6",
        "colab_type": "code",
        "colab": {},
        "outputId": "e3024b32-064b-4f1f-c42e-8ce8ed5195b1"
      },
      "source": [
        "df.groupby(['Gender', 'Loan_Status'])['Loan_Status'].count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender  Loan_Status\n",
              "Female  N               32\n",
              "        Y               62\n",
              "Male    N              119\n",
              "        Y              282\n",
              "Name: Loan_Status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BIpVaA6jtSa-",
        "colab_type": "text"
      },
      "source": [
        "We can clearly see Male ratio of getting loan approved is approx 1.75:1\n",
        "\n",
        "where as female ratio of getting loan approved if approx 2.25:1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cL-LJ6fctSa-",
        "colab_type": "text"
      },
      "source": [
        "## Credit history depending upon gender"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TcpTMQvAtSa_",
        "colab_type": "code",
        "colab": {},
        "outputId": "0f5e1442-997d-4e06-ee97-168523ff72c4"
      },
      "source": [
        "df.groupby(['Gender', 'Credit_History'])['Credit_History'].count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender  Credit_History\n",
              "Female  0.0                16\n",
              "        1.0                78\n",
              "Male    0.0                65\n",
              "        1.0               336\n",
              "Name: Credit_History, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pswxKbttSbB",
        "colab_type": "text"
      },
      "source": [
        "Both of them seems to have equal credit history"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FsJYaVDxtSbC",
        "colab_type": "code",
        "colab": {},
        "outputId": "bc9a3ca4-a257-4ae4-d876-f1e7f3a372ed"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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>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",
              "      <td>1</td>\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>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\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>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\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>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\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>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>LP001011</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>30.0</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 Dependents     Education Self_Employed  \\\n",
              "1  LP001003   Male     Yes          1      Graduate            No   \n",
              "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
              "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
              "4  LP001008   Male      No          0      Graduate            No   \n",
              "5  LP001011   Male     Yes          2      Graduate           Yes   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "1             4583             1508.0       128.0              30.0   \n",
              "2             3000                0.0        66.0              30.0   \n",
              "3             2583             2358.0       120.0              30.0   \n",
              "4             6000                0.0       141.0              30.0   \n",
              "5             5417             4196.0       267.0              30.0   \n",
              "\n",
              "   Credit_History Property_Area Loan_Status  \n",
              "1             1.0         Rural           N  \n",
              "2             1.0         Urban           Y  \n",
              "3             1.0         Urban           Y  \n",
              "4             1.0         Urban           Y  \n",
              "5             1.0         Urban           Y  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NXXBNfMtSbF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.Loan_Status.replace({'Y': 0, 'N': 1}, inplace= True)\n",
        "df.Education.replace({'Graduate': 0, 'Not Graduate': 1}, inplace= True)\n",
        "df.Self_Employed.replace({'Yes': 0, 'No': 1}, inplace= True)\n",
        "df.Gender.replace({'Male': 0, 'Female': 1}, inplace= True)\n",
        "df.Married.replace({'Yes': 0, 'No': 1}, inplace= True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p9gUZKoLtSbN",
        "colab_type": "code",
        "colab": {},
        "outputId": "05fead77-4a72-4a7b-82fd-eb145402d2a4"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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>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",
              "      <td>1</td>\n",
              "      <td>LP001003</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>LP001005</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>LP001006</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>LP001008</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>LP001011</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Loan_ID  Gender  Married Dependents  Education  Self_Employed  \\\n",
              "1  LP001003       0        0          1          0              1   \n",
              "2  LP001005       0        0          0          0              0   \n",
              "3  LP001006       0        0          0          1              1   \n",
              "4  LP001008       0        1          0          0              1   \n",
              "5  LP001011       0        0          2          0              0   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "1             4583             1508.0       128.0              30.0   \n",
              "2             3000                0.0        66.0              30.0   \n",
              "3             2583             2358.0       120.0              30.0   \n",
              "4             6000                0.0       141.0              30.0   \n",
              "5             5417             4196.0       267.0              30.0   \n",
              "\n",
              "   Credit_History Property_Area  Loan_Status  \n",
              "1             1.0         Rural            1  \n",
              "2             1.0         Urban            0  \n",
              "3             1.0         Urban            0  \n",
              "4             1.0         Urban            0  \n",
              "5             1.0         Urban            0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gigtisrctSbT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.drop('Loan_ID',axis = 1,inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9f6Tu-yLtSbZ",
        "colab_type": "code",
        "colab": {},
        "outputId": "58241e9a-58a7-418b-b555-d2cb71004b75"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 495 entries, 1 to 613\n",
            "Data columns (total 12 columns):\n",
            "Gender               495 non-null int64\n",
            "Married              495 non-null int64\n",
            "Dependents           495 non-null object\n",
            "Education            495 non-null int64\n",
            "Self_Employed        495 non-null int64\n",
            "ApplicantIncome      495 non-null int64\n",
            "CoapplicantIncome    495 non-null float64\n",
            "LoanAmount           495 non-null float64\n",
            "Loan_Amount_Term     495 non-null float64\n",
            "Credit_History       495 non-null float64\n",
            "Property_Area        495 non-null object\n",
            "Loan_Status          495 non-null int64\n",
            "dtypes: float64(4), int64(6), object(2)\n",
            "memory usage: 50.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJssi1WLtSbf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dep = pd.get_dummies(df['Dependents'])\n",
        "lat = pd.get_dummies(df['Loan_Amount_Term'])\n",
        "pa = pd.get_dummies(df['Property_Area'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayqlchCVtSbi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.concat([df ,dep ,lat ,pa],axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01wzc2ShtSbm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.drop(['Dependents' ,'Loan_Amount_Term','Property_Area'],axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1l8LZlLmtSbs",
        "colab_type": "code",
        "colab": {},
        "outputId": "50572c3d-faa6-4797-fbc6-de2947bc02a0"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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",
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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>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>Credit_History</th>\n",
              "      <th>Loan_Status</th>\n",
              "      <th>0</th>\n",
              "      <th>...</th>\n",
              "      <th>7.0</th>\n",
              "      <th>10.0</th>\n",
              "      <th>15.0</th>\n",
              "      <th>20.0</th>\n",
              "      <th>25.0</th>\n",
              "      <th>30.0</th>\n",
              "      <th>40.0</th>\n",
              "      <th>Rural</th>\n",
              "      <th>Semiurban</th>\n",
              "      <th>Urban</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Married  Education  Self_Employed  ApplicantIncome  \\\n",
              "1       0        0          0              1             4583   \n",
              "2       0        0          0              0             3000   \n",
              "3       0        0          1              1             2583   \n",
              "4       0        1          0              1             6000   \n",
              "5       0        0          0              0             5417   \n",
              "\n",
              "   CoapplicantIncome  LoanAmount  Credit_History  Loan_Status  0  ...  7.0  \\\n",
              "1             1508.0       128.0             1.0            1  0  ...    0   \n",
              "2                0.0        66.0             1.0            0  1  ...    0   \n",
              "3             2358.0       120.0             1.0            0  1  ...    0   \n",
              "4                0.0       141.0             1.0            0  1  ...    0   \n",
              "5             4196.0       267.0             1.0            0  0  ...    0   \n",
              "\n",
              "   10.0  15.0  20.0  25.0  30.0  40.0  Rural  Semiurban  Urban  \n",
              "1     0     0     0     0     1     0      1          0      0  \n",
              "2     0     0     0     0     1     0      0          0      1  \n",
              "3     0     0     0     0     1     0      0          0      1  \n",
              "4     0     0     0     0     1     0      0          0      1  \n",
              "5     0     0     0     0     1     0      0          0      1  \n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJykQYrhtSbx",
        "colab_type": "code",
        "colab": {},
        "outputId": "ed7e0a3a-6315-4fd4-f4d7-f79ae40d85a3"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 495 entries, 1 to 613\n",
            "Data columns (total 26 columns):\n",
            "Gender               495 non-null int64\n",
            "Married              495 non-null int64\n",
            "Education            495 non-null int64\n",
            "Self_Employed        495 non-null int64\n",
            "ApplicantIncome      495 non-null int64\n",
            "CoapplicantIncome    495 non-null float64\n",
            "LoanAmount           495 non-null float64\n",
            "Credit_History       495 non-null float64\n",
            "Loan_Status          495 non-null int64\n",
            "0                    495 non-null uint8\n",
            "1                    495 non-null uint8\n",
            "2                    495 non-null uint8\n",
            "3+                   495 non-null uint8\n",
            "1.0                  495 non-null uint8\n",
            "3.0                  495 non-null uint8\n",
            "5.0                  495 non-null uint8\n",
            "7.0                  495 non-null uint8\n",
            "10.0                 495 non-null uint8\n",
            "15.0                 495 non-null uint8\n",
            "20.0                 495 non-null uint8\n",
            "25.0                 495 non-null uint8\n",
            "30.0                 495 non-null uint8\n",
            "40.0                 495 non-null uint8\n",
            "Rural                495 non-null uint8\n",
            "Semiurban            495 non-null uint8\n",
            "Urban                495 non-null uint8\n",
            "dtypes: float64(3), int64(6), uint8(17)\n",
            "memory usage: 46.9 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sIrgGoh3tSb0",
        "colab_type": "text"
      },
      "source": [
        "# Modelling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8W8m27ftSb1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn import model_selection\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jw-ilyrctSb4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[['ApplicantIncome', 'CoapplicantIncome' ,'LoanAmount']] = Standardisation.fit_transform(dff[['ApplicantIncome', 'CoapplicantIncome' ,'LoanAmount']])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBy4BlG_tSb_",
        "colab_type": "code",
        "colab": {},
        "outputId": "525072db-28c4-4e88-c469-46c9873c0c57"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Loan_Status</th>\n",
              "      <th>0</th>\n",
              "      <th>...</th>\n",
              "      <th>7.0</th>\n",
              "      <th>10.0</th>\n",
              "      <th>15.0</th>\n",
              "      <th>20.0</th>\n",
              "      <th>25.0</th>\n",
              "      <th>30.0</th>\n",
              "      <th>40.0</th>\n",
              "      <th>Rural</th>\n",
              "      <th>Semiurban</th>\n",
              "      <th>Urban</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.580707</td>\n",
              "      <td>-0.059731</td>\n",
              "      <td>0.026080</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.578814</td>\n",
              "      <td>-0.658963</td>\n",
              "      <td>-1.182978</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.884259</td>\n",
              "      <td>0.278033</td>\n",
              "      <td>-0.129927</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.618635</td>\n",
              "      <td>-0.658963</td>\n",
              "      <td>0.279592</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.191597</td>\n",
              "      <td>1.008398</td>\n",
              "      <td>2.736710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Married  Education  Self_Employed  ApplicantIncome  \\\n",
              "1       0        0          0              1         0.580707   \n",
              "2       0        0          0              0        -0.578814   \n",
              "3       0        0          1              1        -0.884259   \n",
              "4       0        1          0              1         1.618635   \n",
              "5       0        0          0              0         1.191597   \n",
              "\n",
              "   CoapplicantIncome  LoanAmount  Credit_History  Loan_Status  0  ...  7.0  \\\n",
              "1          -0.059731    0.026080             1.0            1  0  ...    0   \n",
              "2          -0.658963   -1.182978             1.0            0  1  ...    0   \n",
              "3           0.278033   -0.129927             1.0            0  1  ...    0   \n",
              "4          -0.658963    0.279592             1.0            0  1  ...    0   \n",
              "5           1.008398    2.736710             1.0            0  0  ...    0   \n",
              "\n",
              "   10.0  15.0  20.0  25.0  30.0  40.0  Rural  Semiurban  Urban  \n",
              "1     0     0     0     0     1     0      1          0      0  \n",
              "2     0     0     0     0     1     0      0          0      1  \n",
              "3     0     0     0     0     1     0      0          0      1  \n",
              "4     0     0     0     0     1     0      0          0      1  \n",
              "5     0     0     0     0     1     0      0          0      1  \n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 316
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZSV-XnstScB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = df['Loan_Status']\n",
        "X = df.drop('Loan_Status',axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51665izOtScG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train,X_test,y_train,y_test = model_selection.train_test_split(X,y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RHNmbdSKtScQ",
        "colab_type": "code",
        "colab": {},
        "outputId": "91ce21d7-ab3b-43cf-e335-01430a44b6bb"
      },
      "source": [
        "print(\"X_TRAIN {} , Y_TRAIN {} , X_TEST {} , Y_TEST {}\".format(X_train.shape,y_train.shape,X_test.shape,y_test.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_TRAIN (371, 25) , Y_TRAIN (371,) , X_TEST (124, 25) , Y_TEST (124,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p6PtEkuMtScU",
        "colab_type": "text"
      },
      "source": [
        "## Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hxp9j5HXtScU",
        "colab_type": "code",
        "colab": {},
        "outputId": "3fb7eb54-8e25-4b92-b675-0e5be302bdc4"
      },
      "source": [
        "logreg = LogisticRegression()\n",
        "\n",
        "logreg.fit(X_train,y_train)\n",
        "\n",
        "y_pred = logreg.predict(X_test)\n",
        "\n",
        "\n",
        "print(confusion_matrix(y_test , y_pred))\n",
        "print(accuracy_score(y_test , y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[84  1]\n",
            " [16 23]]\n",
            "0.8629032258064516\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "D:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WqLAwbnatScX",
        "colab_type": "text"
      },
      "source": [
        "## Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IxGEpcaLtScX",
        "colab_type": "code",
        "colab": {},
        "outputId": "c2824230-a1b2-44f6-d0df-a9f9c1446700"
      },
      "source": [
        "clf = DecisionTreeClassifier()\n",
        "\n",
        "clf.fit(X_train,y_train)\n",
        "\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "print(accuracy_score(y_test , y_pred))\n",
        "print(confusion_matrix(y_test , y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7903225806451613\n",
            "[[68 17]\n",
            " [ 9 30]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFmqJOEdtSce",
        "colab_type": "text"
      },
      "source": [
        "## RandomForest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELr0R9PKtScf",
        "colab_type": "code",
        "colab": {},
        "outputId": "eb72252b-3564-43a9-ab5f-dca2686c1e24"
      },
      "source": [
        "clf = RandomForestClassifier(n_estimators=100)\n",
        "\n",
        "clf.fit(X_train,y_train)\n",
        "\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "print(accuracy_score(y_test , y_pred))\n",
        "print(confusion_matrix(y_test , y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8548387096774194\n",
            "[[82  3]\n",
            " [15 24]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bYNtu6dstScj",
        "colab_type": "text"
      },
      "source": [
        "Logistic Regression Helped us to achieve the best Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yxx8qsIqtScj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}