Loan_Eligibility (1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Loan_Eligibility",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TpjPGOW4VMvH"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predict Loan Eligibility for Dream Housing Finance company"
      ],
      "metadata": {
        "id": "YUi-N02cVtoH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dream Housing Finance company deals in all kinds of home loans. They have presence across all urban, semi urban and rural areas. Customer first applies for home loan and after that company validates the customer eligibility for loan.\n",
        "\n",
        "Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have provided a dataset to identify the customers segments that are eligible for loan amount so that they can specifically target these customers. "
      ],
      "metadata": {
        "id": "0BtxAEqeXE1T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Dictionary**\n",
        "Train file: CSV containing the customers for whom loan eligibility is known as 'Loan_Status'"
      ],
      "metadata": {
        "id": "mcbJnzFqXKuv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "mEbgFk8iXUYb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Test file: CSV containing the customer information for whom loan eligibility is to be predicted"
      ],
      "metadata": {
        "id": "39uP98_gXYzt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "qdOEDheeXdvW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# What is hypothesis generation?\n",
        "This is a very important stage in any data science/machine learning pipeline. It involves\n",
        "understanding the problem in detail by brainstorming as many factors as possible which can\n",
        "impact the outcome. \n",
        "\n",
        "It is done by understanding the problem statement thoroughly and before\n",
        "looking at the data.\n",
        "\n",
        "Below are some of the factors which I think can affect the Loan Approval (dependent variable for\n",
        "this loan prediction problem):\n",
        "\n",
        "* Salary: Applicants with high income should have more chances of loan approval.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "* Previous history: Applicants who have repayed their previous debts should have higher\n",
        "chances of loan approval.\n",
        "\n",
        "* Loan amount: Loan approval should also depend on the loan amount. If the loan amount is\n",
        "less, chances of loan approval should be high.\n",
        "\n",
        "* Loan term: Loan for less time period and less amount should have higher chances of\n",
        "approval.\n",
        "\n",
        "* EMI: Lesser the amount to be paid monthly to repay the loan, higher the chances of loan\n",
        "approval.\n",
        "These are some of the factors which i think can affect the target variable, you can come up with\n",
        "many more factors.\n"
      ],
      "metadata": {
        "id": "_qS_dsq0YXzC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "MH7K1b-XXBer"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.read_csv(\"train_ctrUa4K.csv\")\n",
        "test = pd.read_csv(\"test_lAUu6dG.csv\")"
      ],
      "metadata": {
        "id": "-IdW9DyTZTs6"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets make a copy of train and test data so that even if we have to make any changes in these datasets we would not lose the original datasets."
      ],
      "metadata": {
        "id": "eDRZjh7QZn9d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_original = train.copy()\n",
        "test_original = test.copy()"
      ],
      "metadata": {
        "id": "4a9WDMbGZetF"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Structure of train & Test Datasets"
      ],
      "metadata": {
        "id": "tKkuBKv4aAbR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9qAr7N45ZmtS",
        "outputId": "2813383b-77c9-45b2-a2d4-9353b89522a1"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have 12 independent variables and 1 target variable."
      ],
      "metadata": {
        "id": "ZxKAdcGCaGA6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hhBO8oKiZ4Bd",
        "outputId": "b197d30d-4520-4779-dd25-45228f5ce5e0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
              "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
              "       'Loan_Amount_Term', 'Credit_History', 'Property_Area'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eThWc8F9aEwl",
        "outputId": "e3daf818-5ffb-494a-f8a7-de32e9a74d4f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               object\n",
              "Gender                object\n",
              "Married               object\n",
              "Dependents            object\n",
              "Education             object\n",
              "Self_Employed         object\n",
              "ApplicantIncome        int64\n",
              "CoapplicantIncome    float64\n",
              "LoanAmount           float64\n",
              "Loan_Amount_Term     float64\n",
              "Credit_History       float64\n",
              "Property_Area         object\n",
              "Loan_Status           object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9nfbc_hB62iV",
        "outputId": "9f10fa85-2bbe-4dc0-e572-fc651a6404b2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               object\n",
              "Gender                object\n",
              "Married               object\n",
              "Dependents            object\n",
              "Education             object\n",
              "Self_Employed         object\n",
              "ApplicantIncome        int64\n",
              "CoapplicantIncome      int64\n",
              "LoanAmount           float64\n",
              "Loan_Amount_Term     float64\n",
              "Credit_History       float64\n",
              "Property_Area         object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see there are three format of data types:\n",
        "* object: Object format means variables are categorical. Categorical variables in our dataset\n",
        "are: Loan_ID, Gender, Married, Dependents, Education, Self_Employed, Property_Area,\n",
        "Loan Status\n",
        "* int64: It represents the integer variables. ApplicantIncome is of this format.\n",
        "\n",
        "* float64: It represents the variable which have some decimal values involved. They are alsobnumerical variables. \n",
        "Numerical variables in our dataset are: Coapplicantincome,\n",
        "LoanAmount, Loan _Amount_Term, and Credit _History\n",
        "Let's look at the shape of the dataset."
      ],
      "metadata": {
        "id": "DP0dOkeYaurw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hefbDEdHbA8D",
        "outputId": "d4f610e4-bc7d-4e96-f25f-0e58e4e7e0c9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(614, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYKrM0N6adQY",
        "outputId": "e5017d5a-37e4-407d-ca73-31b24e1cdbb6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(367, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**In this section, we will do univariate analysis.** It is the simplest form of analyzing data where we\n",
        "examine each variable individually. \n",
        "\n",
        "* For categorical features we can use frequency table or bar\n",
        "plots which will calculate the number of each category in a particular variable. \n",
        "\n",
        "* For numerical\n",
        "features, probability density plots can be used to look at the distribution of the variable.\n",
        "\n",
        "\n",
        "* Target Variable\n",
        "We will first look at the target variable, i.e., Loan _Status. As it is a categorical variable, let us look\n",
        "at its frequency table, percentage distribution and bar plot.\n",
        "Frequency table of a variable will give us the count of each category in that variable."
      ],
      "metadata": {
        "id": "sluinqUtbUfA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['Loan_Status'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kf2GjN5xbEsl",
        "outputId": "8c271b1d-f3b0-4ba0-8428-042eef0322c6"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y    422\n",
              "N    192\n",
              "Name: Loan_Status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ratio of proportion of Categories"
      ],
      "metadata": {
        "id": "bXuIIHgUb0M_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['Loan_Status'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_ziVZ5Rbpt-",
        "outputId": "10e3b6c8-d90c-46a8-9246-e439b57a4a8d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y    0.687296\n",
              "N    0.312704\n",
              "Name: Loan_Status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['Loan_Status'].value_counts().plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "B8YnWSKebw3F",
        "outputId": "4e40e7fe-2d82-4615-e541-b91b41413474"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e64ef4fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The loan of 422 (around 69%) people out of 614 was approved.\n",
        "\n",
        "* Now lets visualize each variable separately. Different types of variables are Categorical, ordinal\n",
        "and numerical.\n",
        "1. Categorical features: These features have categories (Gender, Married, Self_Employed,\n",
        "Credit History, Loan_Status)\n",
        "2. Ordinal features: Variables in categorical features having some order involved\n",
        "(Dependents, Education, Property Area)\n",
        "3. Numerical features: These features have numerical values (Applicantincome,\n",
        "CoapplicantIncome, LoanAmount, Loan Amount Term)\n",
        "* Let's visualize the categorical and ordinal features first"
      ],
      "metadata": {
        "id": "3_3PvieFcJ58"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['Gender'].value_counts(normalize=True).plot.bar(title = \"gender\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "XIcjpoDmb7NV",
        "outputId": "efa9ee53-f7d6-453e-80ce-19ee3ab17014"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e64df1bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['Married'].value_counts(normalize=True).plot.bar(title = \"Married\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "VuLNdUL4coiC",
        "outputId": "37529340-5793-46e4-b7da-b14851642bcd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e64920290>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['Self_Employed'].value_counts(normalize=True).plot.bar(title = \"Self_Employed\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "CwGk_Do0ct4y",
        "outputId": "afe511d7-62f9-4914-8d90-a06380ca7f53"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e64920850>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['Credit_History'].value_counts(normalize=True).plot.bar(title = \"Credit_History\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "RokuhqUXc0WA",
        "outputId": "82e295c2-447b-4113-c7a1-b9622d48d1e7"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e64868190>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**It can be inferred from the above bar plots that:**\n",
        "1. 80% applicants in the dataset are male.\n",
        "2. Around 65% of the applicants in the dataset are married.\n",
        "3. Around 15% applicants in the dataset are self employed.\n",
        "4. Around 85% applicants have repaid their debts.\n",
        "Now let's visualize the ordinal variables.\n",
        "\n",
        "* Independent Variable (Ordinal) "
      ],
      "metadata": {
        "id": "-ayaNNkUdHEB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['Dependents'].value_counts(normalize=True).plot.bar(title = \"Dependent\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "fnoJdeqNc7kz",
        "outputId": "7538b4ca-f6bc-41a4-e4cc-fab7f2302137"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e64928b10>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['Education'].value_counts(normalize=True).plot.bar(title = \"Education\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "CNobzFIWdVVu",
        "outputId": "e4570c3e-1b27-4763-bb36-4804f0a475b0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e6476f310>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['Property_Area'].value_counts(normalize=True).plot.bar(title = \"Property Area\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "XUWqNZdcddve",
        "outputId": "980a1a9f-ba2e-4dec-9684-ad97c936ead1"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e646e5250>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Following inferences can be made from the above bar plots:\n",
        "1. Most of the applicants don't have an dependents.\n",
        "2. Around 80% of the applicants are Graduate.\n",
        "3. Most of the applicants are from Semiurban area.\n",
        "\n",
        "\n",
        "* **Independent Variable (Numerical)**\n",
        "\n",
        "Till now we have seen the categorical and ordinal variables and now lets visualize the numerical\n",
        "variables. Lets look at the distribution of Applicant income first."
      ],
      "metadata": {
        "id": "iOg_l0vhdtaB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(train['ApplicantIncome'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "ZKxjc4QgdjRN",
        "outputId": "ed281367-9bf2-42e0-b806-325965d8ed03"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e6461c990>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"ApplicantIncome\"].plot.box()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "UTq7YnXQeEMm",
        "outputId": "cd413f6f-491d-465a-f0e5-01d1ff87156b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e6253ed50>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* It can be inferred that most of the data in the distribution of applicant income is towards left\n",
        "which means it is not normally distributed. \n",
        "* We will try to make it normal in later sections as\n",
        "algorithms works better if the data is normally distributed.\n",
        "\n",
        "* The boxplot confirms the presence of a lot of outliers/extreme values. \n",
        "* This can be attributed to\n",
        "the income disparity in the society. \n",
        "\n",
        "Part of this can be driven by the fact that we are looking at\n",
        "people with different education levels. \n",
        "\n",
        "Let us segregate them by Education:"
      ],
      "metadata": {
        "id": "do4avxX-eZXG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.boxplot(column=\"ApplicantIncome\", by = \"Education\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "q2YocFWleOXd",
        "outputId": "e5dd578d-154b-4528-9ca8-486d61656209"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e624ba850>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that there are a higher number of graduates with very high incomes, which are appearing to be the outliers.\n",
        "\n",
        "Let's look at coapplicant income distribution."
      ],
      "metadata": {
        "id": "54ThNc6oe6gV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(train['CoapplicantIncome'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "VQw8cHcve0Td",
        "outputId": "e5ad3d56-05b1-46e6-b4ad-cffdb579fcbf"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f1e6243c310>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYBklEQVR4nO3de7SddX3n8ffXELlIMImcHmIuBiG0QU0RA4PCmoXijJHaCc44iGMhy6FNB2OBqW0XaJe1rmV1bKuOlcYVlQUsEaQKS3AY5CJFnQohIBLIkUO4h4YkgEqKigl854/nd2DncHKyE84+v33Ofr/W2uv8nt9z2d/9JOeTJ7/nsiMzkSSNv5fVLkCSepUBLEmVGMCSVIkBLEmVGMCSVMletQt4KZYsWZLXXHNN7TIkaVdipM4JfQT8+OOP1y5BkvbYhA5gSZrIDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqmRCPw1tT2zbto2BgYHnpxcuXMjUqVMrViSpV/VcAA8MDHDGeVcxrX8eWzc9zMoVsGjRotplSepBPRfAANP65zF9zqG1y5DU4xwDlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKDGBJqqRjARwRcyPixohYFxF3R8RZpf/jEfFoRNxRXie2rHNuRKyPiHsi4h2dqk2SusFeHdz2duDDmXl7REwDbouI68q8z2Xm37UuHBGHA6cArwNeDVwfEYdl5rMdrFGSqunYEXBmbszM20t7KzAAzB5llaXApZn5TGY+AKwHju5UfZJU27iMAUfEfOCNwC2l60MRcWdEnB8RM0rfbOCRltU2MEJgR8TyiFgTEWu2bNnSwaolqbM6HsARsT/wLeDszHwKWAkcAhwBbAT+fne2l5mrMnNxZi7u6+sb83olabx0NIAjYipN+F6cmZcDZOamzHw2M58DvswLwwyPAnNbVp9T+iRpUurkVRABfBUYyMzPtvTPalns3cBdpX0lcEpE7B0RBwMLgNWdqk+SauvkVRDHAqcCayPijtL3EeB9EXEEkMCDwB8DZObdEXEZsI7mCooVXgEhaTLrWABn5g+BGGHW1aOs80ngk52qSZK6iXfCSVIlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlBrAkVWIAS1IlHQvgiJgbETdGxLqIuDsizir9MyPiuoi4t/ycUfojIr4QEesj4s6IOLJTtUlSN+jkEfB24MOZeThwDLAiIg4HzgFuyMwFwA1lGuCdwILyWg6s7GBtklRdxwI4Mzdm5u2lvRUYAGYDS4ELy2IXAieV9lLgomzcDEyPiFmdqk+SahuXMeCImA+8EbgF6M/MjWXWY0B/ac8GHmlZbUPpk6RJqeMBHBH7A98Czs7Mp1rnZWYCuZvbWx4RayJizZYtW8awUkkaXx0N4IiYShO+F2fm5aV709DQQvm5ufQ/CsxtWX1O6dtBZq7KzMWZubivr69zxUtSh3XyKogAvgoMZOZnW2ZdCSwr7WXAt1v6TytXQxwD/KJlqEKSJp29OrjtY4FTgbURcUfp+wjwaeCyiDgdeAg4ucy7GjgRWA/8EvhAB2uTpOo6FsCZ+UMgdjL7hBGWT2BFp+qRpG7jnXCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVtBXAEXFsO32SpPa1ewT8D232SZLatNdoMyPizcBbgL6I+NOWWQcAUzpZmCRNdqMGMPByYP+y3LSW/qeA93SqKEnqBaMGcGbeBNwUERdk5kPjVJMk9YRdHQEP2TsiVgHzW9fJzLd1oihJ6gXtBvA/AV8CvgI8284KEXE+8C5gc2a+vvR9HPgjYEtZ7COZeXWZdy5wetn+mZn53TZrk6QJqd0A3p6ZK3dz2xcAXwQuGtb/ucz8u9aOiDgcOAV4HfBq4PqIOCwz2wp7SZqI2r0M7aqI+GBEzIqImUOv0VbIzO8DT7a5/aXApZn5TGY+AKwHjm5zXUmakNo9Al5Wfv55S18Cr92D9/xQRJwGrAE+nJk/A2YDN7css6H0vUhELAeWA8ybN28P3l6SukNbR8CZefAIrz0J35XAIcARwEbg73d3A5m5KjMXZ+bivr6+PShBkrpDW0fA5Yj1RTJz+PjuqDJzU8s2vwx8p0w+CsxtWXRO6ZOkSavdIYijWtr7ACcAt/PiE2yjiohZmbmxTL4buKu0rwS+HhGfpTkJtwBYvTvblqSJpq0Azsw/aZ2OiOnApaOtExGXAMcDB0bEBuCvgOMj4gia8eMHgT8u2787Ii4D1gHbgRVeASFpsmv3CHi4p4GDR1sgM983QvdXR1n+k8An97AeSZpw2h0DvormqBWah/AsBC7rVFGS1AvaPQJuvXFiO/BQZm7oQD2S1DPavQztJuCnNE9EmwH8ppNFSVIvaPcbMU6muSrhvwInA7dEhI+jlKSXoN0hiI8CR2XmZoCI6AOuB77ZqcIkabJr91kQLxsK3+KJ3VhXkjSCdo+Ar4mI7wKXlOn3Ald3piRJ6g27+k64Q4H+zPzziPjPwHFl1o+AiztdnCRNZrs6Av48cC5AZl4OXA4QEW8o836/o9VJ0iS2q3Hc/sxcO7yz9M3vSEWS1CN2FcDTR5m371gWIkm9ZlcBvCYi/mh4Z0T8IXBbZ0qSpN6wqzHgs4ErIuL9vBC4i4GX0zxOUpK0h0YN4PIA9bdExFuB15fu/5OZ3+t4ZZI0ybX7POAbgRs7XIsk9RTvZpOkSgxgSarEAJakSgxgSarEAJakSgxgSarEAJakSgxgSarEAJakSgxgSarEAJakSgxgSarEAJakSgxgSarEAJakSgxgSarEAJakSgxgSarEAJakSgxgSarEAJakSgxgSarEAJakSgxgSarEAJakSgxgSarEAJakSgxgSaqkYwEcEedHxOaIuKulb2ZEXBcR95afM0p/RMQXImJ9RNwZEUd2qi5J6hadPAK+AFgyrO8c4IbMXADcUKYB3gksKK/lwMoO1iVJXaFjAZyZ3weeHNa9FLiwtC8ETmrpvygbNwPTI2JWp2qTpG4w3mPA/Zm5sbQfA/pLezbwSMtyG0rfi0TE8ohYExFrtmzZ0rlKJanDqp2Ey8wEcg/WW5WZizNzcV9fXwcqk6TxMd4BvGloaKH83Fz6HwXmtiw3p/RJ0qQ13gF8JbCstJcB327pP61cDXEM8IuWoQpJmpT26tSGI+IS4HjgwIjYAPwV8Gngsog4HXgIOLksfjVwIrAe+CXwgU7VJUndomMBnJnv28msE0ZYNoEVnapFkrqRd8JJUiUGsCRVYgBLUiUGsCRVYgBLUiUduwpiotu2bRsDAwM79C1cuJCpU6dWqkjSZGMA78TAwABnnHcV0/rnAbB108OsXAGLFi2qXJmkycIAHsW0/nlMn3No7TIkTVKOAUtSJQawJFViAEtSJQawJFViAEtSJQawJFViAEtSJQawJFViAEtSJQawJFViAEtSJQawJFViAEtSJQawJFViAEtSJQawJFViAEtSJQawJFViAEtSJQawJFViAEtSJQawJFViAEtSJQawJFViAEtSJQawJFViAEtSJQawJFWyV+0CJornnt3O4ODg89MLFy5k6tSpFSuSNNEZwG16+vF/5VNXPUPfa37F1k0Ps3IFLFq0qHZZkiYwA3g37N83h+lzDq1dhqRJwjFgSarEAJakSgxgSarEAJakSqqchIuIB4GtwLPA9sxcHBEzgW8A84EHgZMz82c16pOk8VDzCPitmXlEZi4u0+cAN2TmAuCGMi1Jk1Y3DUEsBS4s7QuBkyrWIkkdVyuAE7g2Im6LiOWlrz8zN5b2Y0B/ndIkaXzUuhHjuMx8NCJ+C7guIn7aOjMzMyJypBVLYC8HmDdv3pgWtW3bNgYGBgAYHBwkc8QSJGlMVAngzHy0/NwcEVcARwObImJWZm6MiFnA5p2suwpYBbB48eIxTciBgQHOOO8qpvXP47F1q3nl/DeM5eYlaQfjPgQREa+IiGlDbeA/AncBVwLLymLLgG+Pd20A0/rnMX3OobziVQfVeHtJPaTGEXA/cEVEDL3/1zPzmoi4FbgsIk4HHgJOrlCbJI2bcQ/gzLwf+N0R+p8AThjveiSplm66DE2SeooBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmVGMCSVIkBLEmV1PpKogntuWe3Mzg4uEPfwoULmTp1aqWKJE1EBvAeePrxf+VTVz1D32t+BcDWTQ+zcgUsWrSocmWSJhIDeA/t3zeH6XMOrV2GpAnMMWBJqsQAlqRKDGBJqsQAlqRKDGBJqsQAlqRKvAxtDAy/McObMiS1o6cDeHhwDg4Okpm7vZ3WGzO8KUNSu3o6gIff0fbYutW8cv4b9mhb3pghaXf1dADDjsG5ddPDlauR1Es8CSdJlfT8EfBY80lpktplAI8xn5QmqV0GcAd4Qk5SOwzgDvMaYUk7YwB3mNcIS9oZA3gcOCQhaSRehiZJlRjAklSJASxJlTgGPI68SUNSKwN4HA2/SeMXGx/gw+8Y5LDDDgMMY6nXGMDjbPjDfz511Z1eoib1KAO4Mi9Rk3qXJ+EkqRKPgCeAbdu2MTAw8Py0Y8XS5GAATwADAwOccd5VTOuf51ixNIl0XQBHxBLgfwNTgK9k5qcrlzTuhh/xDg4Osv9vzWX6nENfdCnbtm3bAJ4/IvboeHTD9y24z1RPVwVwREwBzgP+A7ABuDUirszMdXUr67zWYB0cHOSz197DAQfNA3b8rrqRvsduyitm0PeaBS+6rA3aC5fhoTSZQ731fxPg85o7aTIMnXX6M3RVAANHA+sz836AiLgUWAqMaQAPfffb0088xpRfP8PP99v3RdPjPW/zPbfzlz9+mukHreWJB9ZxwLyFO9T8b1s2vLCNV8wY8XP96mdb+Mvzr2b6QWsB+OWTm/jYH7x9h0AeyeDgIJ/42vXsN7MfgCceWMeUfacx/aC5bW9johh+I8zO+vTStf69mqh/j4Z/hq994oNj+o917MnXsHdKRLwHWJKZf1imTwX+XWZ+qGWZ5cDyMvnbwD178FYHAo+/xHInOveB+wDcBzA+++DxzFwyvLPbjoB3KTNXAateyjYiYk1mLh6jkiYk94H7ANwHUHcfdNt1wI8Cc1um55Q+SZp0ui2AbwUWRMTBEfFy4BTgyso1SVJHdNUQRGZuj4gPAd+luQzt/My8uwNv9ZKGMCYJ94H7ANwHUHEfdNVJOEnqJd02BCFJPcMAlqRKeiqAI2JJRNwTEesj4pza9bxUEXF+RGyOiLta+mZGxHURcW/5OaP0R0R8oXz2OyPiyJZ1lpXl742IZS39b4qItWWdL0REjO8n3LWImBsRN0bEuoi4OyLOKv09sx8iYp+IWB0RPyn74K9L/8ERcUup+xvlxDYRsXeZXl/mz2/Z1rml/56IeEdL/4T43YmIKRHx44j4Tpnu7n2QmT3xojmpdx/wWuDlwE+Aw2vX9RI/078HjgTuaun7DHBOaZ8D/K/SPhH4v0AAxwC3lP6ZwP3l54zSnlHmrS7LRln3nbU/8wj7YBZwZGlPAwaBw3tpP5S69i/tqcAtpd7LgFNK/5eAM0r7g8CXSvsU4BulfXj5vdgbOLj8vkyZSL87wJ8CXwe+U6a7eh/00hHw87c5Z+ZvgKHbnCeszPw+8OSw7qXAhaV9IXBSS/9F2bgZmB4Rs4B3ANdl5pOZ+TPgOmBJmXdAZt6czd/Mi1q21TUyc2Nm3l7aW4EBYDY9tB/KZ/m3Mjm1vBJ4G/DN0j98Hwztm28CJ5Sj+qXApZn5TGY+AKyn+b2ZEL87ETEH+D3gK2U66PJ90EsBPBt4pGV6Q+mbbPozc2NpPwb0l/bOPv9o/RtG6O9a5b+Rb6Q5Auyp/VD+630HsJnmH4/7gJ9n5vaySGvdz3/WMv8XwKvY/X3TbT4P/AXwXJl+FV2+D3opgHtOOWLriesMI2J/4FvA2Zn5VOu8XtgPmflsZh5Bc/fo0cDvVC5pXEXEu4DNmXlb7Vp2Ry8FcK/c5ryp/LeZ8nNz6d/Z5x+tf84I/V0nIqbShO/FmXl56e65/QCQmT8HbgTeTDO8MnSzVWvdz3/WMv+VwBPs/r7pJscC/ykiHqQZHngbzXPFu3sf1B40H68XzV1/99MMrA8Nor+udl1j8Lnms+NJuL9lx5NPnynt32PHk0+rS/9M4AGaE08zSntmmTf85NOJtT/vCJ8/aMZlPz+sv2f2A9AHTC/tfYEfAO8C/okdT0B9sLRXsOMJqMtK+3XseALqfpqTTxPqdwc4nhdOwnX1Pqi+s8b5D+ZEmrPk9wEfrV3PGHyeS4CNwDaaManTacaxbgDuBa5vCZGgedj9fcBaYHHLdv47zcmG9cAHWvoXA3eVdb5IuXOym17AcTTDC3cCd5TXib20H4BFwI/LPrgL+Fjpfy3NPx7rSxDtXfr3KdPry/zXtmzro+Vz3kPL1R4T6XdnWAB39T7wVmRJqqSXxoAlqasYwJJUiQEsSZUYwJJUiQEsSZUYwOqIiDgoIi6NiPsi4raIuDoiOv6d5BHx8Yj4s9L+RES8fYy3f3ZE7Ncy/WBEHDiW76HeYQBrzJWHmlwB/HNmHpKZbwLO5YXnMYyLzPxYZl4/xps9G9hvl0tJbTCA1QlvBbZl5peGOjLzJ8API+JvI+Ku8nzd90LzHIeIuCEibi/9S0v//Ij4aURcHBEDEfHNoaPPcuT5mbL86og4dHgREXFBRLyntI+KiH8pz8xdHRHTyvZ/UN739oh4S1n2+Ij45/J+Q+8fEXEm8Grgxoi4cdh7zS81fjmaZ/JeGxH7lnmHRsT15b1vj4hDyvZG2hfHR8RNEfHtiLg/Ij4dEe8vNa+NiEPKcn0R8a2IuLW8jh3rP0SNg9p3rfiafC/gTOBzI/T/F5ondU2hORp+mOZ5vnvRPPIR4ECau5OC5jbrBI4t884H/qy0H6TcjQScxgt3Pn28ZZkLgPfQ3Dp6P3BU6T+gvOd+wD6lbwGwprSPp3k61hyag5QfAce1vO+BLZ/pwVLzfGA7cETpvwz4g9K+BXh3ae9T3ndn++J44OelvTfN8wb+uqx7FuWWa5pn3g7VNA8YqP3n7mv3Xx4BazwdB1ySzZO7NgE3AUfRhO3fRMSdNLcNz+aF4YpHMvP/lfbXyjaGXNLy882jvO9vAxsz81aAzHwqm0cQTgW+HBFraW5LPbxlndWZuSEzn6O5vXl+G5/vgcy8o7RvA+ZHxDRgdmZeUd7715n5y1H2BcCt2Tzn+Bma216vLf1rW+p4O/DF8gjKK4EDyhPhNIF01dfSa9K4m+bIs13vp3mgzJsyc1t5otU+Zd7we+WzjXa7/iewCfhdmiPdX7fMe6al/Szt/a4MX2ffPahp+Haea5l+rqWOlwHHZGZrzZpgPAJWJ3wP2Dsilg91RMQimv9av7c8PLyP5iuVVtM8CnBzCd+3Aq9p2da8iBg6uv1vwA9b5r235eePRqnnHmBWRBxVapnW8gjCjeUo91Sa4YBd2Urz1UdtyeZbOjZExEnlvfcu49g/YOR90a5rgT8ZmoiII3ZjXXUJA1hjLjMTeDfw9nIZ2t3Ap2jGLe+keZTf94C/yMzHgIuBxWUo4DTgpy2buwdYEREDNI+JXNkyb0YZtjiL5mh2Z/X8hiak/yEifkIz9roP8I/AstL3O8DTbXy8VcA1w0/C7cKpwJml1n8BDqK5SmSkfdGuM2n22Z0RsQ74H7uxrrqET0NT14rmK4a+k5mvH2HegzSPknx8nMuSxoxHwJJUiUfAklSJR8CSVIkBLEmVGMCSVIkBLEmVGMCSVMn/BwlkoxwmJF6pAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"CoapplicantIncome\"].plot.box()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ivHGtCoNfNeR",
        "outputId": "2789108f-1f34-4111-ea12-4e2da4eac9a5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e646dc490>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see a similar distribution as that of the applicant income. \n",
        "* Majority of coapplicant's income\n",
        "ranges from 0 to 5000. We also see a lot of outliers in the coapplicant income and it is not\n",
        "normally distributed.\n",
        "\n",
        "Let's look at the distribution of LoanAmount variable."
      ],
      "metadata": {
        "id": "daOmxOw5fgaC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(train[\"LoanAmount\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "l3ZCrazTfVnK",
        "outputId": "3a2e514e-9227-4fb0-da0a-be397faef7df"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e625284d0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['LoanAmount'].plot.box()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "wvLm4dOSfqVe",
        "outputId": "0591ae15-aeb3-4f3c-b218-0e491f695909"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e59999e10>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see a lot of outliers in this variable and the distributuion is failrly normal. We will treat the outliers later."
      ],
      "metadata": {
        "id": "sM_CTTt_fzge"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets recall some of the hypotheses that we generated earlier:\n",
        "* Applicants with high income should have more chances of loan approval.\n",
        "* Applicants who have repaid their previous debts should have higher chances of loan\n",
        "approval.\n",
        "* Loan approval should also depend on the loan amount. If the loan amount is less, chances\n",
        "of loan approval should be high.\n",
        "* Lesser the amount to be paid monthly to repay the loan, higher the chances of loan\n",
        "approval.\n",
        "* Lets try to test the above mentioned hypotheses using bivariate analysis\n",
        "\n",
        "After looking at every variable individually in univariate analysis, we will now explore them again\n",
        "with respect to the target variable.\n",
        "\n",
        "1. Categorical Independent Variable vs Target Variable\n",
        "\n",
        "First of all we will find the relation between target variable and categorical independent\n",
        "variables. Let us look at the stacked bar plot now which will give us the proportion of approved\n",
        "and unapproved loans.\n"
      ],
      "metadata": {
        "id": "1Ze9fNaDgQOp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Gender = pd.crosstab(train['Gender'], train['Loan_Status'])"
      ],
      "metadata": {
        "id": "0MNfoxZtfxi7"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Gender"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "miVrRnX-gpBg",
        "outputId": "be8ba416-e850-4b87-c504-ef6963c2314f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d1ff2f52-5dd8-40e8-bedf-636994935ba5\">\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Loan_Status</th>\n",
              "      <th>N</th>\n",
              "      <th>Y</th>\n",
              "    </tr>\n",
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              "      <th>Gender</th>\n",
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              "    <tr>\n",
              "      <th>Female</th>\n",
              "      <td>37</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Male</th>\n",
              "      <td>150</td>\n",
              "      <td>339</td>\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d1ff2f52-5dd8-40e8-bedf-636994935ba5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d1ff2f52-5dd8-40e8-bedf-636994935ba5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Loan_Status    N    Y\n",
              "Gender               \n",
              "Female        37   75\n",
              "Male         150  339"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.core.reshape.pivot import crosstab\n",
        "help(crosstab)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mn4p8EYSgqQ1",
        "outputId": "26d17010-0a7d-4612-a607-086d1354ed27"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on function crosstab in module pandas.core.reshape.pivot:\n",
            "\n",
            "crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name: str = 'All', dropna: bool = True, normalize=False) -> 'DataFrame'\n",
            "    Compute a simple cross tabulation of two (or more) factors. By default\n",
            "    computes a frequency table of the factors unless an array of values and an\n",
            "    aggregation function are passed.\n",
            "    \n",
            "    Parameters\n",
            "    ----------\n",
            "    index : array-like, Series, or list of arrays/Series\n",
            "        Values to group by in the rows.\n",
            "    columns : array-like, Series, or list of arrays/Series\n",
            "        Values to group by in the columns.\n",
            "    values : array-like, optional\n",
            "        Array of values to aggregate according to the factors.\n",
            "        Requires `aggfunc` be specified.\n",
            "    rownames : sequence, default None\n",
            "        If passed, must match number of row arrays passed.\n",
            "    colnames : sequence, default None\n",
            "        If passed, must match number of column arrays passed.\n",
            "    aggfunc : function, optional\n",
            "        If specified, requires `values` be specified as well.\n",
            "    margins : bool, default False\n",
            "        Add row/column margins (subtotals).\n",
            "    margins_name : str, default 'All'\n",
            "        Name of the row/column that will contain the totals\n",
            "        when margins is True.\n",
            "    dropna : bool, default True\n",
            "        Do not include columns whose entries are all NaN.\n",
            "    normalize : bool, {'all', 'index', 'columns'}, or {0,1}, default False\n",
            "        Normalize by dividing all values by the sum of values.\n",
            "    \n",
            "        - If passed 'all' or `True`, will normalize over all values.\n",
            "        - If passed 'index' will normalize over each row.\n",
            "        - If passed 'columns' will normalize over each column.\n",
            "        - If margins is `True`, will also normalize margin values.\n",
            "    \n",
            "    Returns\n",
            "    -------\n",
            "    DataFrame\n",
            "        Cross tabulation of the data.\n",
            "    \n",
            "    See Also\n",
            "    --------\n",
            "    DataFrame.pivot : Reshape data based on column values.\n",
            "    pivot_table : Create a pivot table as a DataFrame.\n",
            "    \n",
            "    Notes\n",
            "    -----\n",
            "    Any Series passed will have their name attributes used unless row or column\n",
            "    names for the cross-tabulation are specified.\n",
            "    \n",
            "    Any input passed containing Categorical data will have **all** of its\n",
            "    categories included in the cross-tabulation, even if the actual data does\n",
            "    not contain any instances of a particular category.\n",
            "    \n",
            "    In the event that there aren't overlapping indexes an empty DataFrame will\n",
            "    be returned.\n",
            "    \n",
            "    Examples\n",
            "    --------\n",
            "    >>> a = np.array([\"foo\", \"foo\", \"foo\", \"foo\", \"bar\", \"bar\",\n",
            "    ...               \"bar\", \"bar\", \"foo\", \"foo\", \"foo\"], dtype=object)\n",
            "    >>> b = np.array([\"one\", \"one\", \"one\", \"two\", \"one\", \"one\",\n",
            "    ...               \"one\", \"two\", \"two\", \"two\", \"one\"], dtype=object)\n",
            "    >>> c = np.array([\"dull\", \"dull\", \"shiny\", \"dull\", \"dull\", \"shiny\",\n",
            "    ...               \"shiny\", \"dull\", \"shiny\", \"shiny\", \"shiny\"],\n",
            "    ...              dtype=object)\n",
            "    >>> pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])\n",
            "    b   one        two\n",
            "    c   dull shiny dull shiny\n",
            "    a\n",
            "    bar    1     2    1     0\n",
            "    foo    2     2    1     2\n",
            "    \n",
            "    Here 'c' and 'f' are not represented in the data and will not be\n",
            "    shown in the output because dropna is True by default. Set\n",
            "    dropna=False to preserve categories with no data.\n",
            "    \n",
            "    >>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])\n",
            "    >>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])\n",
            "    >>> pd.crosstab(foo, bar)\n",
            "    col_0  d  e\n",
            "    row_0\n",
            "    a      1  0\n",
            "    b      0  1\n",
            "    >>> pd.crosstab(foo, bar, dropna=False)\n",
            "    col_0  d  e  f\n",
            "    row_0\n",
            "    a      1  0  0\n",
            "    b      0  1  0\n",
            "    c      0  0  0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Gender.sum(1).astype(float)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ir_NGTAyheaI",
        "outputId": "db6a2ba8-6ff7-4e1d-852a-78594063d480"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender\n",
              "Female    112.0\n",
              "Male      489.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Gender.div(Gender.sum(1).astype(float), axis=0).plot(kind = 'bar', stacked=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "WD50W27UhBEH",
        "outputId": "1b27996b-4511-4a12-b451-8156bacc112a"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e598e6d10>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Gender.div(Gender.sum(1).astype(float), axis=0).plot(kind = 'bar', stacked=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "9QqVJgRshJiX",
        "outputId": "b2cd1a9a-f3ee-476c-8b74-ccdc75e589e0"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e5980e810>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be inffered that the proportion of male and female applicants is more or less same for both approved and unapproved loans.\n",
        "\n",
        "Now let's visulaize remaning categorical varibles VS Target Variables"
      ],
      "metadata": {
        "id": "mq4lHzfah4_e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Married = pd.crosstab(train['Married'],train['Loan_Status'])\n",
        "\n",
        "Dependents = pd.crosstab(train['Dependents'], train[ 'Loan_Status' ])\n",
        "\n",
        "Education = pd.crosstab(train['Education'],train['Loan_Status'])\n",
        "\n",
        "Self_Employed = pd.crosstab(train['Self_Employed'],train['Loan_Status'])\n",
        "\n",
        "Married.div(Married.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize= (4,4))\n",
        "plt.show()\n",
        "\n",
        "Dependents.div(Dependents.sum (1).astype(float),axis=0).plot(kind=\"bar\", stacked=True) \n",
        "plt.show()\n",
        "\n",
        "Education.div(Education.sum(1).astype(float),axis=0).plot(kind=\"bar\",stacked=True, figsize= (4,4)) \n",
        "plt. show()\n",
        "\n",
        "Self_Employed.div(Self_Employed.sum(1). astype(float),axis=0).plot(kind=\"bar\",stacked=True, figsize=(4,4)) \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4Kqkix6Oh1A4",
        "outputId": "f9e083b4-a28d-4981-c3b3-868d14e6824d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BY4GPdrB+EjAJYPjw4Y3ctCRt9+o5Qn8e2K1muaXa9hYRcQTwJeCYzHyjvYEyc1pmjs3MsUOGDNmceiVJHagn0OcCoyJiZET0ASYCM2s7RMQBwDephPnSxpcpSepMp4GemeuAc4DZwCLg1sxcGBGXRMQx1W5fA3YEvhcRT0TEzA6GkyRtJXXNoWfmLGBWm7YLa14f0eC6JEld5JWiklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUiLoCPSLGR8RTEbE4Ii5oZ33fiLiluv6RiBjR6EIlSZvWaaBHRE/gamACMBo4OSJGt+l2JvBKZu4B/CtweaMLlSRtWj1H6OOAxZn5bGauAWYAx7bpcyxwffX1bcDhERGNK1OS1JledfQZBiypWW4FDuqoT2aui4gVwCDgpdpOETEJmFRdXBkRT21O0dvYYNr8Hlsqtu/PL+7PxnFfNlZ32Z/v62hFPYHeMJk5DZi2Lbe5pSJiXmaObXYdpXB/No77srFK2J/1TLk8D+xWs9xSbWu3T0T0AgYAyxtRoCSpPvUE+lxgVESMjIg+wERgZps+M4FPVl+fAPwoM7NxZUqSOtPplEt1TvwcYDbQE5iemQsj4hJgXmbOBK4FboiIxcDLVEK/FN1qiqgbcH82jvuysbr9/gwPpCWpDF4pKkmFMNAlqRAGuiQVYpueh94dRMReVK58HVZteh6YmZmLmleVtPFvcxjwSGaurGkfn5k/aF5leqfwCL1GRPwjlVsbBPDz6k8AN7d3UzJtvoj462bX0J1ExGTgv4BzgV9ERO3tN77anKrKERFfaHYNjeBZLjUi4lfAPpm5tk17H2BhZo5qTmXliYjfZObwZtfRXUTEAuDDmbmyejfT24AbMvMbEfF4Zh7Q1AK7uYh4LDMPbHYdW8opl7d6E9gV+J827btU16kLImJ+R6uAoduylgL02DDNkpnPRcShwG0R8T4q+1My0Nv4LHB/RDzNH25INhzYAzinaVV1X0OBPwNeadMewM+2fTnd2osRsX9mPgFQPVI/CpgO7Nfc0rqniPg1kFT+HneJiGerrzMzd29qcZvJKZc2IqIHlVsG134pOjcz1zevqu4pIq4F/iMzf9rOuu9m5ilNKKtbiogWYF1m/q6ddYdk5oNNKKsYpUxbGeiStnulBLpnuUgSFPEJxyN0SduliBhHZb58bkTsQ+X7nl9m5qwml7bZDHRJ252IuIjKc5J7AfdS+d7sAeBIYHZmXtq86jafgS5pu1M9r39/oC/wO6AlM38fEf2pXIk7pqkFbibn0CVtj9Zl5vrMXAU8k5m/B8jM1+nG15wY6JK2R2si4l3V1x/c0BgRA+jGge6Ui6TtTkT0zcw32mkfDOySmQuaUNYWM9AlqRBOuUhSIQx0SSqEga53tIhYHxFPRMTCiPjviDi/er+dZtXzXHWedXPee1xEjG50TdIGBrre6V7PzP0zcx8qF31MAC5qck2b6zjAQNdWY6Cr28jMpcAk4Jyo6BkRX4uIuRExPyI+DRARh0bEnIi4OyKeiohrNhzVR8THI+KhiHgsIr4XETtW25+LiIur7Quqj3sjIgZFxA+rnxC+Tc29xyPitIj4efUTxDcjome1fWVEXFr9RPFwRAyNiI8AxwBfq/Z/f0RMjognq7XP2KY7U0Uy0NWtZOazQE/gvcCZwIrM/BDwIeCsiBhZ7TqOyuPaRgPvB46vTpV8GTii+nSaecB5NcO/VG3/d+Bz1baLgJ9WPyHcTuX++ETE3sBJwCGZuT+wHji1+p4dgIcz8wPAHOCszPwZMBP4fPUTxzPABcAB1asSz27YTtJ2ywdcqDv7ODAmIk6oLg8ARgFrgJ9Xw5+IuBn4Y2A1lYB/MCIA+gAP1Yz3/ep/HwWOr77+0w2vM/PuiNjwsI7DqVyQMrc6Vn9gaXXdGuCumrGO7KD++cBNEXEHcEdXfnGpPQa6upWI2J3K0fBSKtMf52bm7DZ9DqXyJJpaG55Mc29mntzB8BsuNFlP5/9vBHB9Zrb3cOG1+YcLPDY11v+h8g/G0cCXImK/zFzXyXalDjnlom4jIoYA1wBXVQNzNvC3EdG7un7PiNih2n1cRIyszp2fBPwUeBg4JCL2qPbfISL27GSzc4BTqv0nAAOr7fcDJ0TEe6vr3lN9vuemvAbsVO3fA9gtM38M/COVTxc71rMfpI54hK53uv4R8QTQG1gH3ABcWV33bWAE8FhU5j2WUTmTBGAucBWV58H+GLg9M9+MiDOAmyOib7Xfl4FfbWL7F1f7L6TyHNTfAGTmkxHxZeCH1XBeC3yGtz9gvNYM4FsRMRmYCFxbvXdIAFMz89U69ofUIS/9V3GqUy6fy8yjml2LtC055SJJhfAIXZIK4RG6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKsT/B/IJ4ElqkXvEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Proportion of married applicants is higher for the approved loans.\n",
        "* Distribution of applicants with 1 or 3+ dependents is similar across both the categories of\n",
        "Loan_Status.\n",
        "\n",
        "* There is nothing significant we can infer from Self_Employed vs Loan_Status plot.\n",
        "\n",
        "Now we will look at the relationship between remaining categorical independent variables and\n",
        "Loan _Status."
      ],
      "metadata": {
        "id": "Sj0K1IGOjm9p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Credit_History = pd.crosstab(train['Credit_History'],train['Loan_Status'])\n",
        "\n",
        "Property_Area=pd.crosstab(train[ 'Property_Area'], train[ 'Loan_Status' ])\n",
        "\n",
        "Credit_History.div(Credit_History.sum (1).astype(float),axis=0).plot(kind=\"bar\", stacked=True, figsize= (4,4)) \n",
        "plt.show()\n",
        "\n",
        "Property_Area.div (Property_Area.sum(1) .astype(float), axis=0). plot (kind=\"bar\", stacked=True) \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "X8IA6WE6iwEx",
        "outputId": "033ecc50-e678-46c3-a893-f954edc8e16e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* It seems people with credit history as 1 are more likely to get their loans approved.\n",
        "* Proportion of loans getting approved in semiurban area is higher as compared to that in\n",
        "rural or urban areas.\n",
        "\n",
        "Now let's visualize numerical independent variables with respect to target variable.\n",
        "\n",
        "Numerical Independent Variable vs Target Variable\n",
        "\n",
        "\n",
        "We will try to find the mean income of people for which the loan has been approved vs the mean\n",
        "income of people for which the loan has not been approved.\n"
      ],
      "metadata": {
        "id": "xxgSH2r2kPRk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.groupby('Loan_Status')['ApplicantIncome'].mean().plot.bar ()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "aX6ykj5DkBVx",
        "outputId": "d28c4d5d-c89f-4b65-cb89-5bf809ba7079"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e595f5b90>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here the y-axis represents the mean applicant income.\n",
        " *  We don't see any change in the mean\n",
        "income. \n",
        "* So, let's make bins for the applicant income variable based on the values in it and analyze\n",
        "the corresponding loan status for each bin.\n"
      ],
      "metadata": {
        "id": "INWU-XMgkrjP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bins = [0,2500,4000,6000,81000] \n",
        "group = [ 'Low', 'Average', 'High','Very high']\n",
        "\n",
        "train[\"Income_bin\"] = pd.cut(train['ApplicantIncome'],bins,labels=group)\n",
        "\n",
        "Income_bin = pd.crosstab(train['Income_bin'],train['Loan_Status'] )\n",
        "\n",
        "Income_bin.div(Income_bin.sum(1).astype(float),axis=0).plot(kind=\"bar\",stacked=True) \n",
        "\n",
        "plt.xlabel ('ApplicantIncome') \n",
        "P = plt.ylabel ('Percentage')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "AZXxSpaTkgVv",
        "outputId": "6584a995-4cf9-4f2e-f587-97b15cdcc43b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"Income_bin\"].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tNd_7RblW8G",
        "outputId": "6a4ccdc6-118f-4ca3-e7eb-cab1f16c125a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         High\n",
              "1         High\n",
              "2      Average\n",
              "3      Average\n",
              "4         High\n",
              "5         High\n",
              "6          Low\n",
              "7      Average\n",
              "8         High\n",
              "9    Very high\n",
              "Name: Income_bin, dtype: category\n",
              "Categories (4, object): ['Low' < 'Average' < 'High' < 'Very high']"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_original[\"ApplicantIncome\"].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W3jWl88pluYB",
        "outputId": "86aba317-6f83-4c77-8c6e-82d90b00ae1e"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     5849\n",
              "1     4583\n",
              "2     3000\n",
              "3     2583\n",
              "4     6000\n",
              "5     5417\n",
              "6     2333\n",
              "7     3036\n",
              "8     4006\n",
              "9    12841\n",
              "Name: ApplicantIncome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* It can be inferred that Applicant income does not affect the chances of loan approval which\n",
        "contradicts our hypothesis in which we assumed that if the applicant income is high the chances\n",
        "of loan approval will also be high. \n",
        "\n",
        "We will analyze the coapplicant income and loan amount variable in similar manner.\n"
      ],
      "metadata": {
        "id": "s_nM5Q_bm-Ty"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bins=[0,1000,3000,42000] \n",
        "group=[' Low','Average','High' ]\n",
        "train['Coapplicant_Income_bin'] = pd.cut(train['CoapplicantIncome'], bins, labels=group)\n",
        "\n",
        "Coapplicant_Income_bin = pd.crosstab(train['Coapplicant_Income_bin'],train['Loan_Status'])\n",
        "\n",
        "Coapplicant_Income_bin.div(Coapplicant_Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
        "plt.xlabel ('CoapplicantIncome') \n",
        "\n",
        "p = plt.ylabel('Percentage')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "O_iiDlH4mHJj",
        "outputId": "40534728-b027-4b75-f814-27839230b983"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['ApplicantIncome'][train[\"CoapplicantIncome\"] == 0].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zy7hFbDboWJP",
        "outputId": "c97ca582-43ad-44a1-d522-785c88857c4e"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "273"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* It shows that if coapplicant's income is less the chances of loan approval are high. \n",
        "* But this does\n",
        "not look right. \n",
        "* The possible reason behind this may be that most of the applicants don't have any\n",
        "coapplicant so the coapplicant income for such applicants is O and hence the loan approval is not\n",
        "dependent on it. \n",
        "* So we can make a new variable in which we will combine the applicant's and\n",
        "coapplicant's income to visualize the combined effect of income on loan approval.\n",
        "\n",
        "* Let us combine the Applicant Income and Coapplicant Income and see the combined effect of\n",
        "Total Income on the Loan Status."
      ],
      "metadata": {
        "id": "L7JdEWQ-n_uv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['Total_Income'] = train['ApplicantIncome'] + train['CoapplicantIncome']\n",
        "\n",
        "bins=[0,2500,4000,6000,81000] \n",
        "group=[ 'Low', 'Average', 'High','Very high' ]\n",
        "\n",
        "train['Total_Income_bin'] = pd.cut(train['Total_Income'],bins,labels=group)\n",
        "\n",
        "Total_Income_bin = pd.crosstab(train['Total_Income_bin'],train['Loan_Status']) \n",
        "\n",
        "Total_Income_bin.div(Total_Income_bin.sum(1).astype(float),axis=0).plot(kind=\"bar\",stacked=True) \n",
        "plt.xlabel ( 'Total_Income') \n",
        "P =plt.ylabel ( 'Percentage' )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "jwOC925YnqjM",
        "outputId": "8b8372ee-bd10-4526-ac81-6a11653e7d22"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We can see that Proportion of loans getting approved for applicants having low Total_Income is\n",
        "very less as compared to that of applicants with Average, High and Very High Income.\n",
        "\n",
        "* Let's visualize the Loan amount variable.\n"
      ],
      "metadata": {
        "id": "VEPS6cTap0V6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bins=[0,100,200,700] \n",
        "group=['Low', 'Average', 'Nigh' ]\n",
        "train['LoanAmount_bin'] = pd.cut(train['LoanAmount'], bins, labels=group)\n",
        "\n",
        "LoanAmount_bin = pd.crosstab(train['LoanAmount_bin'],train['Loan_Status'])\n",
        "\n",
        "LoanAmount_bin.div(LoanAmount_bin.sum(1).astype(float),axis=0).plot (kind=\"bar\",stacked=True) \n",
        "plt.xlabel('LoanAmount') \n",
        "P = plt.ylabel('Percentage')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "67USwBudpmG7",
        "outputId": "30a99e93-6227-4c46-a689-011b1c66c555"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* It can be seen that the proportion of approved loans is higher for Low and Average Loan Amount\n",
        "as compared to that of High Loan Amount which supports our hypothesis in which we\n",
        "considered that the chances of loan approval will be high when the loan amount is less.\n",
        "\n",
        "Let's drop the bins which we created for the exploration part. We will change the 3+ in\n",
        "dependents variable to 3 to make it a numerical variable.\n",
        "\n",
        "We will also convert the target\n",
        "variable's categories into 0 and 1 so that we can find its correlation with numerical variables.\n",
        "\n",
        "One more reason to do so is few models like logistic regression takes only numeric values as\n",
        "input. We will replace N with O and Y with 1.\n"
      ],
      "metadata": {
        "id": "o84ZJCYEql-6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ahAKAdxysLXQ",
        "outputId": "679574fb-f33b-49d2-fa86-4836f3000486"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
              "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
              "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status',\n",
              "       'Income_bin', 'Coapplicant_Income_bin', 'Total_Income',\n",
              "       'Total_Income_bin', 'LoanAmount_bin'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train=train.drop (['Income_bin','Coapplicant_Income_bin','Total_Income','Total_Income_bin',\"LoanAmount_bin\"],axis=1)\n",
        "\n",
        "train['Dependents'].replace ('3+',3, inplace=True)\n",
        "test['Dependents'].replace('3+', 3, inplace=True)\n",
        "train['Loan_Status' ].replace('N',0, inplace=True)\n",
        "train['Loan_Status' ].replace ('Y', 1, inplace=True)\n"
      ],
      "metadata": {
        "id": "AgvB_bHEqRhR"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets look at the correlation between all the numerical variables. We will use the heat map to\n",
        "visualize the correlation. Heatmaps visualize data through variations in coloring. The variables\n",
        "with darker color means their correlation is more."
      ],
      "metadata": {
        "id": "hdRIjZ07suvB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matrix  = train.corr()\n",
        "f, ax = plt.subplots(figsize = (9,6))\n",
        "sns.heatmap(matrix, vmax = .8, square = True, cmap = \"BuPu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "o8oBFu_crfaD",
        "outputId": "4e288434-bf1a-403e-9b15-f3cb9116c704"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e59717a90>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hn+GbmZl1AD/DNzMz6wDlzPd+hm9mZtYJXMM3MzPLw8/wzczMOkBJ28ad8M3MzPJwDd/MzKz6VNKEX9KGCTMzM8vDNXwzM7M8SlpVdsI3MzPLo6RN+k74ZmZmeTjhm5mZdYCSNumXNGwzMzPLwzV8MzOzPNykb2Zm1gGc8M3MzDpASR+GlzRsMzMzy8M1fDMzszzcpG9mZtYBnPDNzMw6QEkfhpcmbEkv9cM9jpX0mqTV2n2vJnF8pcj7m5lZD6S+vwpQmoTfTw4EJgP7FhyHE76ZmbVUqRO+pNGS7pE0XdK1ktZI5UdKmixpmqSrJQ1N5RdLOkvSXZLmSNqv7lqbAKsAXyNL/LXywyRdJ+lGSU9IOkrScZIeTPd+W5NYbpW0XdpeS9ITdde9RtLvJT0m6bup/DRgJUlTJV3eDz9GMzPLwzX8QvwUOD4iRgEzgG+k8msiYkxEbA08DBxR9551gV2AjwOn1ZWPB64A7gA2l7RO3bEtyWr9Y4D/AV6JiG2Au4FDm8TSk9HAOGArYJyk4RFxAvBqRIyOiIN680MwM7N+NKgFrwKUNuGn5+yrR8RtqegS4P1pe0tJd0iaARwEbFH31usiYnFEzALqk/qBwBURsRi4Gti/7tgtETE/Ip4F5gG/TuUzgBFNYunJTRExLyJeA2YBG/XiPWZmViTX8AeUi4GjImIr4JvAkLpjC+q2BSBpK2BT4MbU5D6eumb9Lu9ZXLe/mOYjHRay5Oc8pMux+usu6sW1kDRB0hRJU+79x23NTjczs1ZTC14FKG3Cj4h5wPOSdk1FhwC1DDgMeErS8mQ1/GYOBE6OiBHptR6wnqRe1bibxPIEsG3a3o/eeSPF3uheEyNiu4jY7n3rfaCXlzMzs05XpnH4QyXNrdv/AfBp4LzUKW8OcHg69nXgXuDZ9OewJtceD3ysS9m1qfzpXsbXXSzfA66UNAH4bS+vNRGYLukBP8c3MxtgBnninbaKiO5aI3ZocO6PgR83KD+sy/4q6c93Njj3uLrdi+vKR9RtX1w7FhFTu4nlEWBUXdHXur437X+8bvt44Piu1zIzswGgH57BS9oDOBMYDFwQEad1c94ngV8CYyJiSk/XLG2TvpmZWSHa/Axf0mDgR8BHgZHAgZJGNjhvGHAMWUt2U074ZmZmA8v2wOyImBMRr5MNGd+rwXnfAr4DvNabizrhm5mZ5TFIfX7Vj7hKrwl1d1gfeLJuf24qe5Ok9wLDI6K3fcPK8wzfzMxsQGjBM/yImEjWQXsZbq9BZB3XD8vzPtfwzczM8mj/OPy/A8Pr9jdIZTXDyGaAvTXNHbMDMKk2jXt3XMM3MzPLo/3D8iYDm0ramCzRjwc+VTuY5n5Zq7Yv6Vbgy+6lb2ZmViIRsRA4CriBbD2YKyNipqRTJO25rNd1Dd/MzCyPfhiHHxHXA9d3KTupm3N36801nfDNzMzyKOdEe074ZmZmuXhqXTMzsw5Q0PK2feVOe2ZmZh3ANXwzM7M8SlpVdsI3MzPLo6RN+k74ZmZmeZQ04Ze0YcLMzMzycA3fzMwsj5JWlZ3wzczM8ihpk74TvpmZWR5O+GZmZh2gpE36JQ3bzMzM8nAN38zMLA836Vt/O3zSwUWH0BaDS7owRTNbPHlZ0SG0zYnDq/n/4tn/+kXRIbTNosVRdAjl5YRvZmbWAUr6MNwJ38zMLAeVtIZf0u8pZmZmlodr+GZmZnmUtIbvhG9mZpZDSfO9E76ZmVkefoZvZmZmA5Zr+GZmZnmUtKrshG9mZpZDWZv0nfDNzMzycMI3MzOrvpLm+7I+iTAzM7M8XMM3MzPLo6RVfCd8MzOzHFTSFT2d8M3MzPIoZ773M3wzM7NO4Bq+mZlZDh6Hb2Zm1gFKmu+d8M3MzHIpacZ3wjczM8uhrE367rRnZmbWAVzDNzMzy6OkVWUnfDMzsxwq26Qv6aX+CKTBfY+V9Jqk1Yq4f10cX+nh2JqSpqbXPyX9vW5/hf6M08zM+onU91cBBnLDxIHAZGDfguPoNuFHxHMRMToiRgPnAWfU9iPi9Z4uKsmtK2ZmJVTSfL9sCV/SaEn3SJou6VpJa6TyIyVNljRN0tWShqbyiyWdJekuSXMk7dfk+psAqwBfI0v8tfLDJF0n6UZJT0g6StJxkh5M8bytSXy3Stouba8l6Ym6614j6feSHpP03VR+GrBSqrFfnuPns62k2yTdL+kGSevW3f+HkqYAx6T9MyRNkfSwpDEpjsck/X+9vZ+ZmVkzy1rD/ylwfESMAmYA30jl10TEmIjYGngYOKLuPesCuwAfB05rcv3xwBXAHcDmktapO7YlWa1/DPA/wCsRsQ1wN3Bok/h6MhoYB2wFjJM0PCJOAF5NNfaDenENJC0PnA3sFxHbAhemOGtWiIjtIuL7af/1iNiOrIXgV8Dn02c8TNKavbmnmZn1H0l9fhUhd8JPz9RXj4jbUtElwPvT9paS7pA0AzgI2KLurddFxOKImAXUJ/BGDgSuiIjFwNXA/nXHbomI+RHxLDAP+HUqnwGMaBJfT26KiHkR8RowC9ioF+9pZHOyhH2jpKlkrRQb1B3/RZfzJ9XFPzMinoqIBcAcYHjXi0uakFoEpvz0oguWMUQzM1tmg1rwKkCrnyNfDOwdEdMkHQbsVndsQd12t19vJG0FbEqWMAFWAB4HzmlwncV1+4tp/nkWsuRHPaTLsfrrLurFtbojssS9YzfHX+7mvvWfpbb/lhgiYiIwEeCZ+QtiGWM0M7NlVNle+l1FxDzgeUm7pqJDgFptehjwVGrW7lUTeAMHAidHxIj0Wg9YT1KvatxN4nsC2DZt99iPoM4b6fP01qPA2pJ2hKyJX9IWTd5jZmbWVr2pxQ6VNLdu/wfAp4HzUqe8OcDh6djXgXuBZ9Ofw5YhpvHAx7qUXZvKn+7lNbqL73vAlZImAL/t5bUmAtMlPdCb5/gR8XrqlHhWerywHPBDYGYv72dmZgNZSWv4inCrcFlVtUl/8KBy/mNq5p/Pv1J0CG1z4vCDiw6hLc7+V9cuN9Wx3OCBPCp72a2/+kpt/wVy/m9m9fl375EfH9nvv+g8FtzMzCyPktbwC0v4qXPepV2KF0TE+4qIp5k0RO6mBoc+FBHP9Xc8ZmZWDJW0FbKwhB8RM8jGvpdCSuqlidfMzKyem/TNzMxyKGmLvhO+mZlZLiXN+E74ZmZmOZR14h0nfDMzszzKme8H9PK4ZmZm1iKu4ZuZmeXgYXlmZmYdoJzp3gnfzMwsl7J22vMzfDMzsw7gGr6ZmVkOJa3gO+GbmZnl4YRvZmbWAVTSbnt+hm9mZpaD1PdX83toD0mPSpot6YQGx4+TNEvSdEk3Sdqo2TWd8M3MzAYQSYOBHwEfBUYCB0oa2eW0B4HtImIU8Evgu82u64RvZmaWQz/U8LcHZkfEnIh4HbgC2Kv+hIi4JSJeSbv3ABs0u6gTvpmZWQ6SWvGaIGlK3WtC3S3WB56s25+byrpzBPC7ZnG7056ZmVkOreiyFxETgYl9jkU6GNgO+ECzc53wzczMcuiHmfb+Dgyv298glXWNYyzwVeADEbGg2UXdpG9mZjawTAY2lbSxpBWA8cCk+hMkbQP8BNgzIp7pzUVdwy+x1xcuKjqEtli8OIoOoS2GDV2h6BDa5ux//aLoENri6LXGFR1C2+w89pNFh9AWx994eNvv0e4KfkQslHQUcAMwGLgwImZKOgWYEhGTgNOBVYCrUovD3yJiz56u64RvZmaWQ39MuxMR1wPXdyk7qW57bN5rOuGbmZnl4NXyzMzMbMByDd/MzCyHklbwnfDNzMzyKGuTvhO+mZlZDuVM9074ZmZmuZS0gu9Oe2ZmZp3ANXwzM7Mc/AzfzMysA5Qz3Tvhm5mZ5VLSCr4TvpmZWR5lbdJ3pz0zM7MO4Bq+mZlZDiWt4Dvhm5mZ5aGSdttzwjczM8uhrDV8P8M3MzPrAK7hm5mZ5VDWGr4TvpmZWQ6D/AzfzMys+lzDNzMz6wBlTfjutGdmZtYBBlTCl/QOSVdI+ouk+yVdL2mzZbzWxZL2S9sXSBqZtr/Si/e+1GX/MEnnpO3PSTq0h/fuJmmnZYnZzMwGPkl9fhVhwDTpK/sJXAtcEhHjU9nWwDrAn9P+chGxMO+1I+KzdbtfAb69rHFGxHlNTtkNeAm4q7fXXNbPZWZm/a+kLfoDqoa/O/BGfUKNiGnAYEl3SJoEzJI0WNLpkiZLmi7pPyH7wiDpHEmPSvoj8PbadSTdKmk7SacBK0maKunyZQlS0smSvpy2vyBpVorjCkkjgM8BX0z32FXSCEk3p3NukrRheu/Fks6TdC/wXUmPSVo7HRskaXZt38zMBg7X8PtuS+D+bo69F9gyIh6XNAGYFxFjJK0I/EnSH4BtgM2BkWStArOAC+svEhEnSDoqIkY3iWUlSVPr9t8GTGpw3gnAxhGxQNLqEfGCpPOAlyLiewCSfk3WanGJpM8AZwF7p/dvAOwUEYskzQMOAn4IjAWmRcSzTeI0M7N+5k577XVfRDyetj8CHJoS8r3AmsCmwPuBn0fEooj4B3BzH+73akSMrr2Ak7o5bzpwuaSDge6a5HcEfpa2LwV2qTt2VUQsStsXArW+AZ8BLmp0MUkTJE2RNOXyiy9sdIqZmdlbDKQa/kxgv26OvVy3LeDoiLih/gRJH2tXYD34P2RfND4BfFXSVjnf/+bniognJT0t6YPA9mS1/beIiInARIC5z78SyxS1mZktM9fw++5mYMXUZA+ApFHArl3OuwH4L0nLp3M2k7QycDswLj3jX5esT0Ajb9Te2xeSBgHDI+IW4HhgNWAVYD4wrO7Uu4Dxafsg4I4eLnsBcBlL1/zNzGwAUQv+K8KASfgREcA+wNg0LG8mcCrwzy6nXkD2fP4BSQ8BPyFrqbgWeCwd+ylwdze3mghMX9ZOe3UGA5dJmgE8CJwVES8Avwb2qXXaA44GDpc0HTgEOKaHa04i+9LQsDnfzMyKJ/X9VUjcWZ61gUDSdsAZEdG1VaOhqjbpL15cyY9V3nbAXqjq75Gj1xpXdAhts/PYTxYdQlscf+Phbf+Hdsesf/b5f/hdR76j338hDKRn+B1N0gnAf9HNs3szMxsYihpW11cdm/AlrQnc1ODQhyLiuf6OJyJOA07r7/uamVk+Jc33nZvwU1JvNh7fzMxsKa7hm5mZdYBypvsB1EvfzMzM2sc1fDMzsxxK2qLvhG9mZpaHn+GbmZl1gJLmeyd8MzOzPIqaGrev3GnPzMysA7iGb2ZmloOb9M3MzDqAO+2ZmZl1gJLmez/DNzMz6wSu4ZuZmeVQ1hq+E76ZmVkOZR2W54RvZmaWg2v4ZmZmHaCsvfTdac/MzKwDuIZvZmaWQ0kr+E74ZmZmebjTnpmZWQdwDd/63eLFUXQIbTF0SDX/t1y4qJp/XwCLKvr/4s5jP1l0CG3zpz9eXXQIbXJ40QEMWNX8zWpmZtYmg0paxXfCNzMzy6Gk+d4J38zMLA8nfDMzsw5Q1l76nnjHzMysA7iGb2ZmloOb9M3MzDpAWefSd8I3MzPLoaT53gnfzMwsj7LW8N1pz8zMbICRtIekRyXNlnRCg+MrSvpFOn6vpBHNrumEb2ZmloNa8Orx+tJg4EfAR4GRwIGSRnY57Qjg+Yh4F3AG8J1mcTvhm5mZ5SCpz68mtgdmR8SciHgduALYq8s5ewGXpO1fAh9Skws74ZuZmeUgteKlCZKm1L0m1N1ifeDJuv25qYxG50TEQmAesGZPcbvTnpmZWT+LiInAxP68p2v4ZmZmObT7GT7wd2B43f4GqazhOZKWA1YDnuvpok74ZmZmebSiTb9nk4FNJW0saQVgPDCpyzmTgE+n7f2AmyMierqom/TNzMxyaPco/IhYKOko4AZgMHBhRMyUdAowJSImAf8LXCppNvBvsi8FPXLCNzMzy6E/5t2JiOuB67uUnVS3/Rqwf55ruknfzMysA7iGb2ZmlkM5J9Z1wjczM8unpHPpO+GbmZnlUM5074RvZmaWS0kr+OXstCfppX6+3yBJZ0l6SNIMSZMlbZyOfaWX1+jVeWZmZu1QyoRfgHHAesCoiNgK2Ad4IR3rbSJ3wjczq4R+mGuvDSqT8CWNlnSPpAlP6asAABt+SURBVOmSrpW0Rio/MtXIp0m6WtLQVH5xqrXfJWmOpP16uPy6wFMRsRggIuZGxPOSTgNWkjRV0uXputdJul/SzNpiCF3PkzRC0kN1sX9Z0slp+wuSZqXPcUUbflRmZtYH7Z9orz0qk/CBnwLHR8QoYAbwjVR+TUSMiYitgYfJ1hCuWRfYBfg4cFoP174S+ERK2N+XtA1ARJwAvBoRoyPioHTuZyJiW2A74AuS1uzmvO6cAGyTPsfnevvhzcysf5Szfl+RhC9pNWD1iLgtFV0CvD9tbynpDkkzgIOALereel1ELI6IWcA63V0/IuYCmwMnAouBmyR9qJvTvyBpGnAP2cIGm+b8ONOByyUdDCzserB+ScWfXXJhzkubmVmn6oRe+hcDe0fENEmHAbvVHVtQt93jl66IWAD8DvidpKeBvYGb6s+RtBswFtgxIl6RdCswpMHlFrL0l636c/4P2ZeVTwBflbRVWuu4FsebSyr+7bmXe1wowczMWs+99AsUEfOA5yXtmooOAWq1/WHAU5KWJ6vh5ybpvZLWS9uDgFHAX9PhN9K1IVue8PmU7N8N7FB3mfrzngbeLmlNSSuSPVKoXXt4RNwCHJ+ut8qyxGxmZu1Szkb9stbwh0qaW7f/A7JlAs9LnfLmAIenY18H7gWeTX8OW4b7vR04PyVngPuAc9L2RGC6pAeAzwCfk/Qw8ChZsz5dz4uIg9KqR/eRrWn8SDpnMHBZekQh4KyIeAEzMxswylrDV5Plc20Aq2qT/tAhZf0e2rOFiyr51wXAosXV/GyX7V/dgTJ/+uPVRYfQFpPiN21Px/96aUGf/4dfa5UV+/1rQyWa9M3MzKxn1axKLSNJWwGXdileEBHvKyIeMzMbgErapO+EXyciZgCji47DzMwGLpU04zvhm5mZ5VDWTntO+GZmZjmUNN+7056ZmVkncA3fzMwsj5K26Tvhm5mZ5VDOdO+Eb2ZmlktJK/h+hm9mZtYJXMM3MzPLoaQVfCd8MzOzXErapu+Eb2ZmlkM5070TvpmZWS4lreC7056ZmVkncA3fzMwsl3JW8Z3wzczMcihrk74TvpmZWQ4lzfdO+GZmZnmUtYbvTntmZmYdwDV8MzOzXMpZxXfCNzMzy6GsTfqKiKJjsBKQNCEiJhYdRztU9bP5c5VPVT9bVT9X2fgZvvXWhKIDaKOqfjZ/rvKp6mer6ucqFSd8MzOzDuCEb2Zm1gGc8K23qvz8raqfzZ+rfKr62ar6uUrFnfbMzMw6gGv4ZmZmHcAJ38zMrAM44ZuZmXUAJ3zrWJKGFh1Dq0m6tDdlZSVpDUmjJL239io6pr6StGbRMfQHSYMkrVp0HJ3MCd8akrSZpJskPZT2R0n6WtFxtYKknSTNAh5J+1tLOrfgsFpli/odSYOBbQuKpaUkfQuYDpwFfD+9vldoUK1xj6SrJH1MKuukrY1J+pmkVSWtDDwEzJL030XH1amc8K075wMnAm8ARMR0YHyhEbXOGcB/AM8BRMQ04P2FRtRHkk6UNB8YJenF9JoPPAP8quDwWuUAYJOI2C0idk+vDxYdVAtsRjZs7RDgMUnflrRZwTG1ysiIeBHYG/gdsDHZ57QCOOFbd4ZGxH1dyhYWEkkbRMSTXYoWFRJIi0TEqRExDDg9IlZNr2ERsWZEnFh0fC3yELB60UG0WmRujIgDgSOBTwP3SbpN0o4Fh9dXy0tanizhT4qINwCPBS+IV8uz7vxL0iakf5yS9gOeKjaklnlS0k5ApF9GxwAPFxxTS0TEiZLWBzai7t93RNxeXFQtcyrwYHrMtKBWGBF7FhdS36Vn+AeT1XyfBo4GJgGjgavIasVl9RPgCWAacLukjYAXC42og3niHWtI0jvJmhl3Ap4HHgcOjognioyrFSStBZwJjCVb2PoPwDER8VyhgbWApNPIHr3MYkmrRZQ9KQJImkmWQGYAi2vlEXFbYUG1gKQ/A5cCF0XE3C7Hjo+I7xQTWXtIWi4iKtNaWCZO+Naj1NlmUETMLzoWa07So8CoiFjQ9OSSkTQ5IsYUHUcrpU6V342ILxUdSztIOqlReUSc0t+xmJv0rRuSVgcOBUYAy9U6D0fEFwoMqyUkbUzWbDqCpZu9S18LBuYAy1PX5F0hd0g6lay5u75J/4HiQuqbiFiUHi9V1ct120OAj1ORx2dl5Bq+NSTpLuAe3tp8eklhQbWIpGnA/1KxpmEASVcDWwM3sXRSrMIXtVsaFEfZe+pL+jGwPtnz+jcTZERcU1hQbSJpReCGiNit6Fg6kWv41p0hEXFc0UG0yWsRcVbRQbTJpPSqlNT0PSkizig6ljYYQjZEtP6LSwCVS/jAUGCDooPoVK7hW0OSvgi8BPyGpWuK/y4sqBaR9ClgU7LOepVoGu4Eku6LiO2LjsN6T9IMlgzDGwysDXwrIs4uLqrO5YRvDUn6PPA/wAss+QcbEfHO4qJqjfQc+BDgLyxp0i990zCApMdpMM65In9vZ5D1T/gFSzd9l/qLmqQNgLOBnVPRHWSjRuZ2/65ySMPwahYCT7uHfnGc8K0hSXOA7SPiX0XH0mqSZpPNAPZ60bG0Wpd52YcA+wNvi4iGvaXLpMLP8G8EfkY2NA+yMfkHRcSHi4uqNSRdGhGHNCuz/uGEbw1J+gOwd0S8UnQsrSbpOmBCRDxTdCz9QdL9EVGJ+fSrSNLUiBjdrKyMJD0QEe+t218OmB4RIwsMq2O5055152VgaqpVVaq3N9n0rI9ImkyFZmwD6LJ63CBgOyry71zSOsC3gfUi4qOSRgI7RsT/FhxaXz0n6WDg52n/QNI6D2Ul6UTgK8BKkmoz6wl4nWxCLyuAa/jWkKRPNyqvyLC8DzQqr8iwvPpm74Vk05p+LyIeLSai1pH0O+Ai4KsRsXWqLT4YEVsVHFqfpOfcZwM7kvW/uAs4usF6D6Uj6dQKreVQek741i1JK5Ct5AXwaFr4ohJSbbE2a9t9ndK8X2a1mfYkPRgR26Sy0jd9S9o5Iv7UrKysJK1BNipmSK2sIms7lI5Xy7OGJO0GPAb8CDgX+LOkUi8hWyPpAOA+sg5tBwD3psWBSk/SapJ+IGlKen1f0mpFx9UXqSYP8HLqlFhb0GkHYF5hgbVOoyFqlRi2JumzwO3ADcA3058nFxlTJ6vEsz1ri+8DH6k1Baf1uX8OVKHz11eBMbVavaS1gT8Cvyw0qta4kGwZ2QPS/iFkzeD7FhZR390HvBf4EtmkQptI+hPZmO7SflFLS9/uBKwtqX6Sq1XJxqxXwTFkLWn3RMTukt5N1g/DCuCEb91Zvv65b0T8OS0lWwWDujThP0d1Wrs2iYhP1u1/U9LUwqJpDQFExP2p/8Xmqazsj5lWAFYh+z08rK78RUr8RaaL1yLiNUlIWjEiHpG0edFBdSonfOvOFEkXAJel/YOAKQXG00q/l3QDS3pFjwN+V2A8rfSqpF0i4k7IngUDrxYcU191rQHXfEQSEfGDfo+oBVIn0dskXRwRfwWQNAhYJSKqsmb83LQQ13XAjZKeB/5acEwdy532rKG0yMXngV1S0R3AuVVZdlXSvtR9toi4tsh4WkXSaOASYDWyWvC/gcMiYlqhgfWBpKeAH5Nq+l1FxDf7N6LWkvQz4HPAImAyWZP+mRFxeqGBtVhqnVkN+F3JW2ZKywnfGpK0Mllz3KK0PxhYsQoT8aTlcZ+KiNfS/krAOhHxRKGBtZCkVQGqUFPsOnlL1dRGGkg6iKyvwgnA/RExquDQ+swz7Q0sbtK37twEjCVbQAdgJbLFZqqwdvdVLP05FqWyMY1PL4/UfHooMAJYTsoqxSWfMKlhzf4tJ0lrRMTz7Q6mDZZP/WP2Bs6JiDckVaUmtkX9Tqo4VKHjbylVpaOStd6QiKgle9L20ALjaaXl6ufRT9srFBhPK11PluxnAPfXvcrsQ70876a2RtE+PyGbIGll4PY0EU+pW2YknShpPjBK0ovpNR94BvhVweF1LNfwrTsvS3pvbSUySdtS/s5fNc9K2jMiJgFI2guoyiJBQyKiUQe30sqxJHOvWgIGmog4CzirruivknYvKp5WiIhTgVM9097A4mf41pCkMcAVwD/IfpG+AxgXEWWvLSJpE+ByYD2yz/YkcGhEzC40sBaQ9EWyxzC/Yel1AnqbNEurbM/6JR0cEZd1MwKhtKMP4M3pgl+IiHlpf3eyRxZPAD+q4kqVZeAavjUUEZPTJBm1MbNlH/P8poj4C7CDpFXS/ktN3lImrwOnk00uVPs2H8A7C4vIurNy+nNYj2eV05XAPsC8NHLkKuBUYDTZzJ2fLTC2juUavnVL0k6kzl+1soj4aWEBtUgacvhJ3vrZTikqplaRNAfYPiKq8oii1+rn2LdiSZpeG2Ug6XvA4oj4f2megalVGIFQRq7hW0OSLgU2AaaS9WKHrKZY+oRP1mloHllntkrMK1BnNlD6oZON9GKIV2879w0Iks7q6XiFRlZ8EDgRICIW10aOWP9zwrfubAeMjGo2AW0QEXsUHUSbvAxMTcvk1j/DL3PyqOlxiFcJ+ynU94f5JvCNogJpg5slXQk8BawB3AwgaV2yx05WACd8685DZB31nio6kDa4S9JWETGj6EDa4Lr0qlfqL22STgS+AqwkqTZcTWSJY2JhgfVRRFxS25Z0bP1+BRxLNmX1usAudf1/3kHWv8QK4Gf41lCqIY4mW6msvqa4Z2FBtYikWcC7gMfJPpuAqOJzRUnDgfFVmKa1ykO8yjbCoFUk3R0ROxYdR6dwDd+6c3LRAbTRR4sOoJ3Scr/7AweSDT2sxDoBEXGipPWBjVi6s+XtxUVlfTSk6AA6iRO+NZRW8qoUSW9Lm/MLDaQNJA0jW/P+U8BmwDXAxhGxQaGBtZCk04DxwCyW7khayoSfZp6rNbEO7fK4IiJi1WIi61duYu5HTvi2lLpfQmLpf4xV+CV0P0s+W1dlH6v+DNnjl68Bd0ZESNqn4JhabR9g86qs2BgRvRp/X+I1AmyAccK3pfT2l1AZRcTGRcfQRieS1X7PBX4u6RcFx9MOc4Dlqd5QymZuIltFr4o8Rq8fefEcayiNw29aVkaS3rLISqOyMomIH0bEDsBeqeg6YD1Jx0varMDQWukVsiGHP5F0Vu1VdFD9oMpJ0cvk9iPX8K07Xcc8L0fJl7WUNIRsOtO1JK3Bkl+kqwLrFxZYC0XEHODbwLclbUn2TP96slEJZTcpvTpNaZ9zS9oX+A7wdrJ/b0s9GoyIhwoMr+N4WJ4tpX7MM0tmbHtzzHOZh0VJOoZsfPB6wN9ZkvBfBM6PiHOKis2sO2UesidpNvCJiHi46FjMCd+6UfExz0dHxNlFx9EOzWpUZSbpcRrUdiOizJ0tmyrzGgGS/hQROxcdh2Wc8K1bVR7zXOGFgSpbo5K0Zt3uELK5Bt4WEScVFFJLNFsjQNLbSjhtMACSziSbXe86lp7A65rCgupgTvjWUHdjnisy017DhYGqMN98p9WoJN0fEWXvW7JUk31aI2BGRIwsMKyWkHRRg+KIiM/0ezDmTnvWrUqNee6iygsDTUlD8ipXo5JU/xx7ENnfY2l/h1V1jYB6EXF40THYEqX9x2JtV+Uxz1VeGGhVss6WH6krC7KZ98ru+3XbC4EngAOKCaXvIuJU4NSK95cZAhxBNurnzWl0XcMvhhO+dac25vkmqrfM6lrALEmVWxioyjWqiNi96BhaSdK7I+IR4KourRcARMQDBYTVapcCjwD/AZwCHARUrn9JWfgZvjUk6dONyquwhKekDzQqr8L6AVWuUUlajWzN+PenotuAUyJiXnFRLTtJ50fEkWllyq4iIj7Y70G1WG2EgaTpETFK0vLAHWmSKOtnruFbQ1VI7N2pQmLvQZVrVBeSPY6pNeMfAlxEtmhQ6UTEkenPSrVcdPFG+vOFNBHUP8mGjFoBXMO3hiRtCpwKjGTpmmLpxzxL2gE4G3gPsAIwGHi5ImPVK1ujkjQ1IkY3KyuLNGdCtyrS0fKzwNXAKLIvZ6sAX4+InxQaWIdyDd+6cxFZ8+kZwO7A4VRn7YVzyIYcXkXW0/tQsiVlq6DKNapXJe0SEXcCSNoZeLXgmPriE+nPtwM7ATen/d2Bu6hAR8uIuCBt3ka5V6OsBCd8685KEXGTJEXEX4GTJd0PlHqSk5qImC1pcEQsAi6S9CDZinNlNzGtE/B1snnnV0nbVfBfwCXpWb6AfwOHFRpRH9Q6WEr6A9kw0afS/rrAxQWG1jLp7+pkYNdUdCvwrbL2uyg7J3zrzgJJg4DHJB1FNvf8KgXH1CqvSFqBbBTCd8mG51Wi9aLKNaqImApsLam28MqLTd5SFsNryT55GtiwqGBarFL9LsrOz/CtIUljyDp7rQ58i2x89+kRcU+hgbWApI3IfqmuAHwRWA04NyJmFxpYC1S5RiVpdbLHLyNYekrkUg8VlXQOsCnw81Q0DpgdEUcXF1VrVK3fRdk54VvHkbQy8GpELE77g4EVI+KVnt858Em6mqxGVRtlcQiwdUSUvkYl6S7gHmAGsLhWXoURJZL2Yclww9sj4toi42kVSXcD/92l38X3ImLHYiPrTE741pCkG4H9I+KFtL8GcEVE/EexkfWdpHuAsRHxUtpfBfhDROxUbGR9V+UaVZmXiW0mtTptGhF/lDQUGBwR84uOq68kbQ38lKwVDeB54NMRMb24qDpXJZ5bWlusVUv2ABHxPNXp7T2kluwB0vbQAuNppVcl7VLbqUBP9nqXSjpS0rqS3lZ7FR1UX0k6EvglUBuqtj7ZWgilFxHTImJrsmF5o9Iyv6WfUKisnPCtO4slvdlxKNVAqtIc9HL9VKaStqU6SfFzwI8kPSHpCbIhiP9ZbEgt8zpwOnA3cH96TSk0otb4PLAz8CJARDxGdb5cA1kHy7pOlscVGkwHcy99685XgTsl3UY2BGpXYEKxIbXMsWTzl/+D7LO9g6yjVOlFxDS69GSXdCxQhSbULwHvioh/FR1Iiy2IiNclASBpOarz5boRFR1Ap/IzfOuWpLWA2gxt91TpF22agW7ztPtoRLzR0/llJulvEVH6YV5pvPreVehcWS8NDX2BbATC0cD/BWZFxFcLDaxNqvL/Yxk54dtSait4NVq9C8q9gpekD0bEzd1NaVqFqUwbkfRkRAwvOo6+knQt2aJAt1ChFRyVVe0/S7aksYAbgAuixL+cJc2ncSuFyCb1cutyAfxDt66+BBzJ0muP1wTl7nDzAbLpSz/R4FhV1oxvpLSJo4vreGtntlJ/tjQkdGZEvBs4v+h4WiUihhUdg72Va/hmFdCJNSpJw4HxEXF60bH0haRfAUdHxN+KjsWqrXK/BKxvqryCl6QeewdHxA/6K5ZW65QalaS1gf2BA4H1gCpMULMGMFPSfcDLtcKI2LO4kKyKnPCtq0bN3TVlb/buiKRYNZKGkc29/imyVQ2vATaOiA0KDayPJL0LWIe3Lm60K9n6DmYt5SZ9MxvQJL0K3Ad8DbgzIkLSnIgo9eJAkn4DnBgRM7qUbwV8OyJ6+vJtlpsn3rGGJK0p6SxJD0i6X9KZktYsOq5WkPROSb+W9KykZyT9SlKpk0fFnQisCJwLnChpk4LjaZV1uiZ7gFQ2ov/DsapzwrfuXAE8C3wS2C9t/6LQiFrnZ8CVwLpkz4GvYslKZTbARMQPI2IHYK9UdB2wnqTjJW1WYGh9tXoPx1bqtyisY7hJ3xqS9FBEbNmlbEZEbFVUTK0iaXpEjOpSVpvz20pA0pZkHffGRcS7io5nWUj6OXBzRJzfpfyzwIcjohKzP9rA4YRvDUn6Adlz0ytT0X7A9hHx5eKiag1J3yFbtesKso6I48h6Sp8OEBH/Li46W1aS7i7TsquS1iEbZfA62boAANsBKwD7RMQ/i4rNqskJ3xpK47pXZsm644NYMmQoImLVQgJrAUmP93A4yt4ZrFNJejCtxlYqknYHaq1pMyPi5iLjsepywjezSpD0QEQ0nBLazDwO33qQJuHZhazZ+46IqMQa3ZKGkC1Q8uZnA86LiNcKDczMrI1cw7eGJJ0LvIslvdfHAX+JiM8XF1VrSLoSmA9cloo+BaweEfsXF5X1VVmb9M36ixO+NSTpEeA9tRW7JA0ie774nmIj6ztJsyJiZLMyKxdJW0bEQ0XHYTZQeRy+dWc2UL9m9fBUVgUPSNqhtiPpfcCUAuOxXpC0r6THJM2T9KKk+ZJerB13sjfrmWv41pCk24AxZEPzSNuTgReh3At7SHoY2ByorU62IfAosJCsl/6o7t5rxZE0G/hERDxcdCxmZeROe9adk+q2Rbagx3jgG8WE01J7FB2ALZOnnezNlp1r+NYtSduQdWjbH3gcuCYizi42qtaR9HZgSG3f65EPbJLOBN5BNrXuglp5mZdsNutPruHbUtLc5Aem17/I5s9XROxeaGAtJGlP4Ptk8+g/A2wEPAxsUWRc1tSqwCvAR+rKyr5ks1m/cQ3fliJpMdm49CMiYnYqK/1SpPUkTQM+CPwxIrZJM50dHBFHFByamVnbuIZvXe1L9qz+Fkm/J5tvXsWG1HJvRMRzkgZJGhQRt0j6YdFBWc/ShElHkLXE1D+K+UxhQZmViIfl2VIi4rqIGA+8G7gFOBZ4u6QfS/pIz+8ujRckrULWknF5ejb8cpP3WPEuJXuG/x/AbcAGZBMomVkvuEnfmpK0BlnHvXER8aGi4+krSSsDr5G1XBwErAZcHhHPFRqY9ag2k15teWNJy5NN+bxD0zebmWv41lxEPB8RE6uQ7AEi4mVgLbLhec8BVzjZl8Ib6c8XJG1J9kXt7QXGY1YqTvjWcSQdQDah0P7AAcC9kvYrNirrhYmptenrwCRgFvCdYkMyKw836VvHSb30PxwRz6T9tcl67G9dbGRmZu3jGr51okG1ZJ88h/8tDHiSVpN0hqQp6fU9SasVHZdZWfiXnHWi30u6QdJhkg4DfgtcX3BM1tyFZGs5HJBe84GLCo3IrETcpG8dQ9K7gHUi4k+S9gV2SYdeIOul/5fiorNmJE2NiNHNysysMdfwrZP8kCWr/V0TEcdFxHHAtemYDWyvSqp9SUPSzsCrBcZjViqeac86yToRMaNrYUTMkDSi/8OxnD4H/LTuuf3zwKcLjMesVJzwrZOs3sOxlfotClsmETEN2FrSqmn/RUnHAtOLjcysHNykb51kiqQjuxZK+ixwfwHx2DKIiBcj4sW0e1yhwZiViDvtWceQtA7Z8/rXWZLgtwNWAPaJiH8WFZstG0lPRsTwouMwKwMnfOs4aTncLdPuzIi4uch4bNlJ+ltEbFh0HGZl4IRvZgOapPlAo19UAlaKCPdFMusFJ3wzM7MO4E57ZmZmHcAJ38zMrAM44ZuZmXUAJ3wzM7MO4IRvZmbWAf5/aqJBoIjRYGoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that the most correlated variables are ( ApplicantIncome - LoanAmmount) and ( Credit_Histroy - Loan_Status) Loan Ammount is also correlated with CoapplicantIncome"
      ],
      "metadata": {
        "id": "-ln84k0GtOzp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After exploring all the variables in our data, we can now impute the missing values and treat the\n",
        "outliers because missing data and outliers can have adverse effect on the model performance.\n",
        "\n",
        "## Missing value imputation\n",
        "Let's list out feature-wise count of missing values.\n"
      ],
      "metadata": {
        "id": "0dcz83JcuMdP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Bu3_6xbtBh7",
        "outputId": "eb6cafb0-7261-4c6b-b7f2-516c4eb6d28b"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are missing values in Gender, Married, Dependents, Self_Employed, LoanAmount,\n",
        "Loan Amount Term and Credit History features.\n",
        "\n",
        "We will treat the missing values in all the features one by one.\n",
        "We can consider these methods to fill the missing values:\n",
        "* For numerical variables: imputation using mean or median\n",
        "* For categorical variables: imputation using mode\n",
        "\n",
        "\n",
        "There are very less missing values in Gender, Married, Dependents, Credit_ History and\n",
        "Self_Employed features so we can fill them using the mode of the features."
      ],
      "metadata": {
        "id": "5L2qXALbuix1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are very less missing values in Gender,Married, Dependents, Self_Employed features so we can fill them using the mode of the features.\n"
      ],
      "metadata": {
        "id": "VQ0gUKFBu76J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train[ 'Gender'].fillna(train['Gender'].mode()[0], inplace=True)\n",
        "\n",
        "train['Married' ].fillna(train['Married' ].mode()[0], inplace=True)\n",
        "\n",
        "train[ 'Dependents' ].fillna(train[ 'Dependents' ].mode()[0], inplace=True)\n",
        "\n",
        "train['Self_Employed'].fillna(train['Self_Employed'].mode()[0],inplace=True) \n",
        "\n",
        "train['Credit_History'].fillna(train['Credit_History' ].mode()[0], inplace=True)\n"
      ],
      "metadata": {
        "id": "zeKg4fcSuXSM"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's try to find a way to fill the missing values in Loan_Amount_Term. We will look at the\n",
        "value count of the Loan amount term variable.\n"
      ],
      "metadata": {
        "id": "-8e3WWrIvVyP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['Loan_Amount_Term'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Awd6hMWFvb3z",
        "outputId": "dcf78c34-e3e5-4671-abbd-dd822b20ed63"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "360.0    512\n",
              "180.0     44\n",
              "480.0     15\n",
              "300.0     13\n",
              "84.0       4\n",
              "240.0      4\n",
              "120.0      3\n",
              "36.0       2\n",
              "60.0       2\n",
              "12.0       1\n",
              "Name: Loan_Amount_Term, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be seen that in loan amount term variable, the value of 360 is repeating the most. So we\n",
        "will replace the missing values in this variable using the mode of this variable.\n"
      ],
      "metadata": {
        "id": "2V53H8wmvum4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)\n"
      ],
      "metadata": {
        "id": "xA6ifMLNvfmH"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will see the LoanAmount variable. As it is a numerical variable, we can use mean or\n",
        "median to impute the missing values. \n",
        "\n",
        "We will use median to fill the null values as earlier we saw\n",
        "that loan amount have outliers so the mean will not be the proper approach as it is highly\n",
        "affected by the presence of outliers."
      ],
      "metadata": {
        "id": "7ji6iW2uwOjY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "train['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)\n"
      ],
      "metadata": {
        "id": "pBmx79IIv5dV"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets check whether all the missing values are filled in the dataset.\n"
      ],
      "metadata": {
        "id": "Y7QaYZ1dwbIW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.isnull().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1VwVAGuwWO-",
        "outputId": "6de8576f-2692-4f38-ade5-cb3cbab60770"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID              0\n",
              "Gender               0\n",
              "Married              0\n",
              "Dependents           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"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test['Gender'].fillna(train['Gender'].mode()[0], inplace=True)\n",
        "\n",
        "test['Married' ].fillna(train['Married' ].mode()[0], inplace=True)\n",
        "\n",
        "test[ 'Dependents' ].fillna(train[ 'Dependents' ].mode()[0], inplace=True)\n",
        "\n",
        "test['Self_Employed'].fillna(train['Self_Employed'].mode()[0],inplace=True) \n",
        "\n",
        "test['Credit_History'].fillna(train['Credit_History' ].mode()[0], inplace=True)\n",
        "\n",
        "test['Loan_Amount_Term'].fillna(train['Loan_Amount_Term' ].mode()[0], inplace=True)\n",
        "\n"
      ],
      "metadata": {
        "id": "wOV-2-9uRtKM"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test['LoanAmount'].fillna(train['LoanAmount' ].mode()[0], inplace=True)"
      ],
      "metadata": {
        "id": "sCt_0IjSTc3s"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see that all the missing values have been filled in the test dataset. Let's fill all the\n",
        "missing values in the test dataset too with the same approach."
      ],
      "metadata": {
        "id": "zxBlMEnIwujV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Outlier Treatment\n",
        "As we saw earlier in univariate analysis, LoanAmount contains outliers so we have to treat them\n",
        "as the presence of outliers affects the distribution of the data. Let's examine what can happen to\n",
        "a data set with outliers. For the sample data set:\n",
        "\n",
        "1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4\n",
        "\n",
        "We find the following: mean, median, mode, and standard deviation\n",
        "\n",
        "* Mean = 2.58\n",
        "* Median = 2.5\n",
        "* Mode = 2\n",
        "\n",
        "* Standard Deviation = 1.08\n",
        "\n",
        "If we add an outlier to the data set:\n",
        "\n",
        "1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 400\n",
        "\n",
        "The new values of our statistics are:\n",
        "\n",
        "* Mean = 35.38\n",
        "* Median = 2.5\n",
        "* Mode = 2\n",
        "* Standard Deviation = 114.74\n",
        "\n",
        "\n",
        "It can be seen that having outliers often has a significant effect on the mean and standard\n",
        "deviation and hence affecting the distribution. \n",
        "* We must take steps to remove outliers from our\n",
        "data sets.\n",
        "* Due to these outliers bulk of the data in the loan amount is at the left and the right tail is longer.\n",
        "This is called right skewness. \n",
        "* One way to remove the skewness is by doing the log\n",
        "transformation. As we take the log transformation, it does not affect the smaller values much,\n",
        "but reduces the larger values. So, we get a distribution similar to normal distribution.\n",
        "\n",
        "Let's visualize the effect of log transformation. We will do the similar changes to the test file\n",
        "simultaneously.\n"
      ],
      "metadata": {
        "id": "Spg1M22Cxf--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test['LoanAmount'].dtypes"
      ],
      "metadata": {
        "id": "zV1-6-T16jr_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train['LoanAmount_log' ] = np.log(train['LoanAmount'])\n",
        "train['LoanAmount_log'].hist (bins=20) \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "iKFxNcxcxKbL",
        "outputId": "9cfa1644-b8ff-44cf-c1a4-42b114e6c4b0"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e592e3f90>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#test['LoanAmount']= pd.to_numeric(test['LoanAmount'],errors = 'coerce')\n",
        "\n",
        "#test['LoanAmount'] = test['LoanAmount'].apply(lambda x: float(x))\n",
        "\n",
        "\n",
        "#test['LoanAmount'] = pd.to_numeric(test['LoanAmount'])\n",
        "test[ 'LoanAmount_log'] = np.log(test['LoanAmount'])"
      ],
      "metadata": {
        "id": "1nCA7tmN7CMU"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test['LoanAmount_log']"
      ],
      "metadata": {
        "id": "D57Cz5_BEJEy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.columns"
      ],
      "metadata": {
        "id": "crgC5iIVBLvP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_original.columns"
      ],
      "metadata": {
        "id": "8mlQqWXSA4i4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# test['Gender'] = test['Gender'].fillna(train_original['Gender'].mode()[0])\n",
        "# test['Dependents'] = test['Dependents']. fillna(train_original[ 'Dependents'].mode()[0])\n",
        "# test['Self_Employed'] = test['Self_Employed'].fillna(train_original['Self_Employed' ].mode()[0]) \n",
        "# test['Credit_History'] = test['Credit_History'].fillna(train_original['Credit_History' ].mode()[0])\n",
        "# test['Loan_Amount_Term'] = test['Loan_Amount_Term'].fillna(train_original['Loan_Amount_Term'].mode()[0]) \n",
        "# test['LoanAmount'] = test['LoanAmount'].fillna(train_original['LoanAmount'].median)"
      ],
      "metadata": {
        "id": "CUSXQ0zywsP6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now the distribution looks much closer to normla and effect of extreme values has been significantly subsided. Let's build a logistic regression model and make predictions for the test datasets."
      ],
      "metadata": {
        "id": "BhuQZqYG1Zn6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The process of model building is not complete without evaluation of model's performance.\n",
        "\n",
        "* Suppose we have the predictions from the model, how can we decide whether the predictions\n",
        "are accurate? \n",
        "* We can plot the results and compare them with the actual values, i.e. calculate the\n",
        "distance between the predictions and actual values. Lesser this distance more accurate will be\n",
        "the predictions. \n",
        "* Since this is a classification problem, we can evaluate our models using any one\n",
        "of the following evaluation metrics:\n",
        "\n",
        "1. Accuracy: Let us understand it using the confusion matrix which is a tabular\n",
        "representation of Actual vs Predicted values. This is how a confusion matrix looks like:"
      ],
      "metadata": {
        "id": "v3Qm_E0W2L9_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "MchMAjR92Tgl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* True Positive - Targets which are actually true(Y) and we have predicted them true(Y)\n",
        "* True Negative - Targets which are actually false(N) and we have predicted them false(N)\n",
        "* False Positive - Targets which are actually false(N) but we have predicted them true(T)\n",
        "* False Negative - Targets which are actually true(T) but we have predicted them false(N)\n",
        "\n",
        "Using these values, we can calculate the accuracy of the model. The accuracy is given by:\n",
        "\n",
        "( True Positive + True Negatives ) / ( True Positive + True Negatives + False Positives + False Negatives )"
      ],
      "metadata": {
        "id": "Wztw8nux2a6K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "* Precision: It is a measure of correctness achieved in true prediction i.e. of observations\n",
        "labeled as true, how many are actually labeled true.\n",
        "\n",
        "Precision = TP/ (TP + FP)\n",
        "\n",
        "* Recall(Sensitivity) - It is a measure of actual observations which are predicted correctly i.e.\n",
        "how many observations of true class are labeled correctly. It is also known as 'Sensitivity!\n",
        "\n",
        "Recall = TP / (TP + FN)\n",
        "\n",
        "* Specificity - It is a measure of how many observations of false class are labeled correctly.\n",
        "\n",
        "Specificity = TN / (TN + FP)\n",
        "\n",
        "Specificity and Sensitivity plays a crucial role in deriving ROC curve.\n",
        "\n",
        "* ROC curve\n",
        "* Receiver Operating Characteristic(ROC) summarizes the model's performance by\n",
        "evaluating the trade offs between true positive rate (sensitivity) and false positive rate(1-\n",
        "specificity).\n",
        "* The area under curve (AUC), referred to as index of accuracy(A) or concordance index, is a\n",
        "perfect performance metric for ROC curve. Higher the area under curve, better the\n",
        "prediction power of the model.\n",
        "This is how a ROC curve looks like:"
      ],
      "metadata": {
        "id": "78T3gek621Gi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "UDRT1GIM3EdN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The area of this curve measures the ability of the model to correctly classify true positives\n",
        "and true negatives. We want our model to predict the true classes as true and false classes\n",
        "as false.\n",
        "* So it can be said that we want the true positive rate to be 1. But we are not concerned with\n",
        "the true positive rate only but the false positive rate too. For example in our problem, we\n",
        "are not only concerned about predicting the Y classes as Y but we also want N classes to be\n",
        "predicted as N.\n",
        "* We want to increase the area of the curve which will be maximum for class 2,3,4 and 5 in\n",
        "the above example.\n",
        "* For class 1 when the false positive rate is 0.2, the true positive rate is around 0.6. But for\n",
        "class 2 the true positive rate is 1 at the same false positive rate. So, the AUC for class 2 will\n",
        "be much more as compared to the AUC for class 1. So, the model for class 2 will be better.\n",
        "* The class 2,3,4 and 5 model will predict more accurately as compared to the class 0 and 1\n",
        "model as the AUC is more for those classes.\n",
        "At the competition's page, it has been mentioned that our submission data would be evaluated\n",
        "based on the accuracy. Hence, we will use accuracy as our evaluation metric."
      ],
      "metadata": {
        "id": "MpfvrWEO43Eb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us make our first model to predict the target variable. We will start with Logistic Regression\n",
        "which is used for predicting binary outcome.\n",
        "* Logistic Regression is a classification algorithm. It is used to predict a binary outcome (1 /\n",
        "O, Yes / No, True / False) given a set of independent variables.\n",
        "* Logistic regression is an estimation of Logit function. Logit function is simply a log of odds\n",
        "in favor of the event.\n",
        "* This function creates a s-shaped curve with the probability estimate, which is very similar\n",
        "to the required step wise function\n",
        "\n",
        "\n",
        "Lets drop the Loan ID variable as it do not have any effect on the loan status. We Will do the\n",
        "same changes to the test dataset which we did for the training dataset.\n"
      ],
      "metadata": {
        "id": "KVzXjF9U5Rvv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = train.drop ('Loan_ID', axis=1)\n",
        "test = test.drop ('Loan_ID', axis=1)\n"
      ],
      "metadata": {
        "id": "eCad-ZTayQ_4"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use scikit-learn (sklearn) for making different models which is an open source library for\n",
        "Python. It is one of the most efficient tool which contains many inbuilt functions that can be used\n",
        "for modeling in Python.\n",
        "\n",
        "Sklearn requires the target variable in a separate dataset. So, we will drop our target variable\n",
        "from the train dataset and save it in another dataset."
      ],
      "metadata": {
        "id": "gCFWKws25rPf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = train.drop ('Loan_Status',1)\n",
        "y = train.Loan_Status"
      ],
      "metadata": {
        "id": "vbEmA7j75g-e"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will make dummy variables for the categorical variables. Dummy variable turns\n",
        "categorical variables into a series of 0 and 1, making them lot easier to quantify and compare. \n",
        "\n",
        "* Let\n",
        "us understand the process of dummies first:\n",
        "Consider the \"Gender\" variable. It has two classes, Male and Female.\n",
        "* As logistic regression takes only the numerical values as input, we have to change male and\n",
        "female into numerical value.\n",
        "* Once we apply dummies to this variable, it will convert the \"Gender\" variable into two\n",
        "variables(Gender Male and Gender_Female), one for each class, i.e. Male and Female.\n",
        "Gender Male will have a value of O if the gender is Female and a value of 1 if the gender is\n",
        "Male."
      ],
      "metadata": {
        "id": "QIxn91Xe6MnU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = pd.get_dummies(X)\n",
        "train = pd.get_dummies(train)\n",
        "test = pd.get_dummies(test)\n"
      ],
      "metadata": {
        "id": "FuGr06yJ6A8F"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "PytpTANcNiCB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will train the model on training dataset and makepredictions for the test dataset. But\n",
        "can we validate these predictions? \n",
        "\n",
        "One way of doing this is we can divide our train dataset into\n",
        "two parts: train and validation. \n",
        "\n",
        "We can train the model on this train part and using that make\n",
        "predictions for the validation part. In this way we can validate our predictions as we have the\n",
        "true predictions for the validation part (which we do not have for the test dataset).\n",
        "\n",
        "We will use the train _test_split function from sklearn to divide our train dataset. So, first let us\n",
        "importtrain test split."
      ],
      "metadata": {
        "id": "54gxayno-qKs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset has been divided into training and validation part. Let us import LogisticRegression\n",
        "and accuracy_score from sklearn and fit the logistic regression model."
      ],
      "metadata": {
        "id": "JotlSuEL_Iap"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_cv, y_train, y_cv = train_test_split(X,y, test_size =0.3)"
      ],
      "metadata": {
        "id": "GmM0_C-XTvpQ"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "model = LogisticRegression()\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, max_iter=100, multi_class='ovr',n_jobs = 1,penalty='l2', random_state=1, solver='liblinear', tol = 0.0001,verbose=0, warm_start=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dd-CxR6k_Amq",
        "outputId": "21e9eef0-66c9-4292-85d9-b4c4b2f91014"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(multi_class='ovr', n_jobs=1, random_state=1,\n",
              "                   solver='liblinear')"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here the C parameter represents inverse of regularization strength. Regularization is applying a\n",
        "penalty to increasing the magnitude of parameter values in order to reduce overfitting. Smaller\n",
        "values of C specify stronger regularization."
      ],
      "metadata": {
        "id": "Ff_-7kuvAUI-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred_cv = model.predict(x_cv)"
      ],
      "metadata": {
        "id": "Y6bxZidr_qYB"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_cv, pred_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fC2Ty4luAaD-",
        "outputId": "3e4d9868-38f6-4d38-fba0-bee5066a8ab9"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8216216216216217"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_test = model.predict(test)"
      ],
      "metadata": {
        "id": "vdTQ8D-JAdwV"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission = pd.read_csv(\"sample_submission_49d68Cx.csv\")\n",
        "submission['Loan_Status'] = pred_test\n",
        "submission['Loan_ID'] = test_original['Loan_ID']\n",
        "\n",
        "submission['Loan_Status'].replace(0,'N', inplace=True)\n",
        "submission['Loan_Status'].replace(1,'Y', inplace=True)\n",
        "\n",
        "pd.DataFrame(submission, columns = ['Loan_ID' , 'Loan_Status']).to_csv('logistic.csv')\n"
      ],
      "metadata": {
        "id": "ckTEzshiAi8e"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedKFold"
      ],
      "metadata": {
        "id": "dUEzXaxka1Kg"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "i=1 \n",
        "kf = StratifiedKFold (n_splits=5, random_state=1,shuffle=True) \n",
        "for train_index, test_index in kf.split(X,y):\n",
        "   print ('\\nf{} of kfold {}'.format(i,kf.n_splits))\n",
        "   xtr, xvl = X.iloc[train_index],X.iloc[test_index]\n",
        "   ytr, yvl = y.iloc[train_index],y.iloc[test_index]\n",
        "   model = LogisticRegression (random_state=1)\n",
        "   model.fit(xtr, ytr)\n",
        "   pred_test = model.predict(xvl)\n",
        "   score = accuracy_score(yvl,pred_test)\n",
        "   print( 'accuracy score', score)\n",
        "   i+=1 \n",
        "   pred_test = model.predict(test)\n",
        "   pred = model.predict_proba(xvl)[:,1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXhkE6f0U8d6",
        "outputId": "98982837-b364-4d24-9621-c0b5ff86d540"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "f1 of kfold 5\n",
            "accuracy score 0.8048780487804879\n",
            "\n",
            "f2 of kfold 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy score 0.8373983739837398\n",
            "\n",
            "f3 of kfold 5\n",
            "accuracy score 0.7804878048780488\n",
            "\n",
            "f4 of kfold 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy score 0.7886178861788617\n",
            "\n",
            "f5 of kfold 5\n",
            "accuracy score 0.7950819672131147\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics \n",
        "fpr, tpr, _ = metrics.roc_curve(yvl,pred) \n",
        "auc = metrics.roc_auc_score (yvl, pred) \n",
        "plt.figure (figsize=(12,8))\n",
        "plt.plot (fpr, tpr, label=\"validation, auc=\"+str (auc))\n",
        "plt.xlabel( 'False Positive Rate') \n",
        "plt.ylabel(\"True Positive Rate\") \n",
        "plt.legend(loc=4)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "xcIgWW7eajlp",
        "outputId": "11b71c0f-6478-4e6f-f68f-abaf2bbdb8c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the domain knowledge, we can come up with new features that might affect the target\n",
        "variable. We will create the following three new features:\n",
        "* Total Income - As discussed during bivariate analysis we will combine the Applicant\n",
        "Income and Coapplicant Income. If the total income is high, chances of loan approval might\n",
        "also be high.\n",
        "* EMI - EMI is the monthly amount to be paid by the applicant to repay the loan. Idea behind\n",
        "making this variable is that people who have high EMI's might find it difficult to pay back\n",
        "the loan. We can calculate the EMI by taking the ratio of loan amount with respect to loan\n",
        "amount term.\n",
        "* Balance Income - This is the income left after the EMI has been paid. Idea behind creating\n",
        "this variable is that if this value is high, the chances are high that a person will repay the\n",
        "loan and hence increasing the chances of loan approval.\n"
      ],
      "metadata": {
        "id": "S1e1QA94cLGI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['Total_Income'] = train['ApplicantIncome'] + train['CoapplicantIncome']\n",
        "test['Total_Income' ] = test['ApplicantIncome'] + test['CoapplicantIncome']\n"
      ],
      "metadata": {
        "id": "IZhRCcQBbmmh"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(train['Total_Income'])"
      ],
      "metadata": {
        "id": "SzTaU78wcbWH",
        "outputId": "bc63e030-3dff-4760-e193-78801b9c4fcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        }
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e50d11c90>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['Total_Income_log'] = np.log(train['Total_Income'])\n",
        "sns.distplot(train['Total_Income_log'])"
      ],
      "metadata": {
        "id": "pW-tCfGaclw4",
        "outputId": "dd45a6ce-9280-40e6-aaf2-931fe363d505",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        }
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e50c160d0>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test['Total_Income_log'] = np.log(test['Total_Income'])"
      ],
      "metadata": {
        "id": "PpKXEDXBdIhT"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train['EMI'] = train['LoanAmount'] / train['Loan_Amount_Term']\n",
        "test['EMI'] = test['LoanAmount'] / test['Loan_Amount_Term']"
      ],
      "metadata": {
        "id": "iMFM10_BdPO-"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(train['EMI'])"
      ],
      "metadata": {
        "id": "_dHeDVRxdgDZ",
        "outputId": "a4cd50f0-9e74-40aa-f5c3-26a3d236e79c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e50f7a890>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us create Balance Income feature now and check its distribution."
      ],
      "metadata": {
        "id": "90df4QRnd_uF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "train[ 'Balance_Income'] = train['Total_Income'] - (train['EMI']*1000) #Multiply with 1000 to make the units equal test['Balance\n",
        "test['BalanceIncome']= test['Total_Income' ]-(test['EMI']*1000)\n",
        "sns.distplot(train['Balance_Income' ]) "
      ],
      "metadata": {
        "id": "NvgfFmlCdoNz",
        "outputId": "1bd88ee3-b9a9-4075-9692-634bce6ba0b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        }
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e5084e190>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us now drop the variables which we used to create these new features. Reason for doing this\n",
        "is, the correlation between those old features and these new features will be very high and\n",
        "logistic regression assumes that the variables are not highly correlated. We also wants to\n",
        "remove the noise from the dataset, so removing correlated features will help in reducing the\n",
        "noise too."
      ],
      "metadata": {
        "id": "gWk8z2gvefk7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test=test.drop (['ApplicantIncome','CoapplicantIncome','LoanAmount', 'Loan_Amount_Term'],axis=1)\n",
        "\n"
      ],
      "metadata": {
        "id": "_e7sctateQON"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.dtypes"
      ],
      "metadata": {
        "id": "pLqfw8tVf7t_",
        "outputId": "ce5ab365-7541-44cc-a553-1d6daf0a21f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Credit_History             float64\n",
              "Loan_Status                  int64\n",
              "LoanAmount_log             float64\n",
              "Gender_Female                uint8\n",
              "Gender_Male                  uint8\n",
              "Married_No                   uint8\n",
              "Married_Yes                  uint8\n",
              "Dependents_3                 uint8\n",
              "Dependents_0                 uint8\n",
              "Dependents_1                 uint8\n",
              "Dependents_2                 uint8\n",
              "Education_Graduate           uint8\n",
              "Education_Not Graduate       uint8\n",
              "Self_Employed_No             uint8\n",
              "Self_Employed_Yes            uint8\n",
              "Property_Area_Rural          uint8\n",
              "Property_Area_Semiurban      uint8\n",
              "Property_Area_Urban          uint8\n",
              "Total_Income               float64\n",
              "Total_Income_log           float64\n",
              "EMI                        float64\n",
              "Balance_Income             float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.dtypes"
      ],
      "metadata": {
        "id": "yZRKa8-Zf4jQ",
        "outputId": "97901270-8993-44cf-cc72-91c2da9afa12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Credit_History             float64\n",
              "LoanAmount_log             float64\n",
              "Gender_Female                uint8\n",
              "Gender_Male                  uint8\n",
              "Married_No                   uint8\n",
              "Married_Yes                  uint8\n",
              "Dependents_3                 uint8\n",
              "Dependents_0                 uint8\n",
              "Dependents_1                 uint8\n",
              "Dependents_2                 uint8\n",
              "Education_Graduate           uint8\n",
              "Education_Not Graduate       uint8\n",
              "Self_Employed_No             uint8\n",
              "Self_Employed_Yes            uint8\n",
              "Property_Area_Rural          uint8\n",
              "Property_Area_Semiurban      uint8\n",
              "Property_Area_Urban          uint8\n",
              "Total_Income                 int64\n",
              "Total_Income_log           float64\n",
              "EMI                        float64\n",
              "BalanceIncome              float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "i=1 \n",
        "kf = StratifiedKFold (n_splits=5, random_state=1,shuffle=True) \n",
        "for train_index, test_index in kf.split(X,y):\n",
        "   print ('\\nf{} of kfold {}'.format(i,kf.n_splits))\n",
        "   xtr, xvl = X.iloc[train_index],X.iloc[test_index]\n",
        "   ytr, yvl = y.iloc[train_index],y.iloc[test_index]\n",
        "   model = LogisticRegression (random_state=1)\n",
        "   model.fit(xtr, ytr)\n",
        "   pred_test = model.predict(xvl)\n",
        "   score = accuracy_score(yvl,pred_test)\n",
        "   print( 'accuracy score', score)\n",
        "   i+=1 \n",
        "   pred_test = model.predict(test)\n",
        "   pred = model.predict_proba(xvl)[:,1]"
      ],
      "metadata": {
        "id": "Cjeo2DNDe-e2",
        "outputId": "020746d0-8cc3-4ffe-ff1f-9af54df3177d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "f1 of kfold 5\n",
            "accuracy score 0.8048780487804879\n",
            "\n",
            "f2 of kfold 5\n",
            "accuracy score 0.8373983739837398\n",
            "\n",
            "f3 of kfold 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- BalanceIncome\n",
            "- EMI\n",
            "- Total_Income\n",
            "- Total_Income_log\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- ApplicantIncome\n",
            "- CoapplicantIncome\n",
            "- LoanAmount\n",
            "- Loan_Amount_Term\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- BalanceIncome\n",
            "- EMI\n",
            "- Total_Income\n",
            "- Total_Income_log\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- ApplicantIncome\n",
            "- CoapplicantIncome\n",
            "- LoanAmount\n",
            "- Loan_Amount_Term\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- BalanceIncome\n",
            "- EMI\n",
            "- Total_Income\n",
            "- Total_Income_log\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- ApplicantIncome\n",
            "- CoapplicantIncome\n",
            "- LoanAmount\n",
            "- Loan_Amount_Term\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy score 0.7804878048780488\n",
            "\n",
            "f4 of kfold 5\n",
            "accuracy score 0.7886178861788617\n",
            "\n",
            "f5 of kfold 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- BalanceIncome\n",
            "- EMI\n",
            "- Total_Income\n",
            "- Total_Income_log\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- ApplicantIncome\n",
            "- CoapplicantIncome\n",
            "- LoanAmount\n",
            "- Loan_Amount_Term\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy score 0.7950819672131147\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- BalanceIncome\n",
            "- EMI\n",
            "- Total_Income\n",
            "- Total_Income_log\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- ApplicantIncome\n",
            "- CoapplicantIncome\n",
            "- LoanAmount\n",
            "- Loan_Amount_Term\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "i=1 \n",
        "kf = StratifiedKFold (n_splits=5, random_state=1,shuffle=True) \n",
        "for train_index, test_index in kf.split(X,y):\n",
        "   print ('\\nf{} of kfold {}'.format(i,kf.n_splits))\n",
        "   xtr, xvl = X.iloc[train_index],X.iloc[test_index]\n",
        "   ytr, yvl = y.iloc[train_index],y.iloc[test_index]\n",
        "   model = tree.DecisionTreeClassifier (random_state=1)\n",
        "   model.fit(xtr, ytr)\n",
        "   pred_test = model.predict(xvl)\n",
        "   score = accuracy_score(yvl,pred_test)\n",
        "   print( 'accuracy score', score)\n",
        "   i+=1 \n",
        "   pred_test = model.predict(test)\n",
        "   pred = model.predict_proba(xvl)[:,1]"
      ],
      "metadata": {
        "id": "kRuK5zzzf6pP",
        "outputId": "6fc94794-6d3f-4afe-cd18-cbfe49413a7d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "f1 of kfold 5\n",
            "accuracy score 0.6991869918699187\n",
            "\n",
            "f2 of kfold 5\n",
            "accuracy score 0.7398373983739838\n",
            "\n",
            "f3 of kfold 5\n",
            "accuracy score 0.7154471544715447\n",
            "\n",
            "f4 of kfold 5\n",
            "accuracy score 0.7235772357723578\n",
            "\n",
            "f5 of kfold 5\n",
            "accuracy score 0.6721311475409836\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- BalanceIncome\n",
            "- EMI\n",
            "- Total_Income\n",
            "- Total_Income_log\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- ApplicantIncome\n",
            "- CoapplicantIncome\n",
            "- LoanAmount\n",
            "- Loan_Amount_Term\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- BalanceIncome\n",
            "- EMI\n",
            "- Total_Income\n",
            "- Total_Income_log\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- ApplicantIncome\n",
            "- CoapplicantIncome\n",
            "- LoanAmount\n",
            "- Loan_Amount_Term\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- BalanceIncome\n",
            "- EMI\n",
            "- Total_Income\n",
            "- Total_Income_log\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- ApplicantIncome\n",
            "- CoapplicantIncome\n",
            "- LoanAmount\n",
            "- Loan_Amount_Term\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- BalanceIncome\n",
            "- EMI\n",
            "- Total_Income\n",
            "- Total_Income_log\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- ApplicantIncome\n",
            "- CoapplicantIncome\n",
            "- LoanAmount\n",
            "- Loan_Amount_Term\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- BalanceIncome\n",
            "- EMI\n",
            "- Total_Income\n",
            "- Total_Income_log\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- ApplicantIncome\n",
            "- CoapplicantIncome\n",
            "- LoanAmount\n",
            "- Loan_Amount_Term\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "3O8jaC1_fyj6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}