model_training.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Assignment_3.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9N6FiQbl7fO"
      },
      "source": [
        "# load dataset\r\n",
        "import pandas as pd\r\n",
        "data= pd.read_csv(\"https://raw.githubusercontent.com/dphi-official/Datasets/master/Loan_Data/loan_train.csv\")"
      ],
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kliibgxu1A5-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "outputId": "0f064417-0f4f-45d6-c849-3851777c3555"
      },
      "source": [
        "# Basic EDA\r\n",
        "data.head(10)"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>LP002305</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4547</td>\n",
              "      <td>0.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>LP001715</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3+</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5703</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>LP002086</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4333</td>\n",
              "      <td>2451.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>LP001136</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4695</td>\n",
              "      <td>0.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>LP002529</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6700</td>\n",
              "      <td>1750.0</td>\n",
              "      <td>230.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>LP001603</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4344</td>\n",
              "      <td>736.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>LP001273</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6000</td>\n",
              "      <td>2250.0</td>\n",
              "      <td>265.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>LP002706</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5285</td>\n",
              "      <td>1430.0</td>\n",
              "      <td>161.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>LP001859</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>14683</td>\n",
              "      <td>2100.0</td>\n",
              "      <td>304.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>LP001228</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3200</td>\n",
              "      <td>2254.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0   Loan_ID  Gender  ... Credit_History Property_Area Loan_Status\n",
              "0           0  LP002305  Female  ...            1.0     Semiurban           1\n",
              "1           1  LP001715    Male  ...            1.0         Rural           1\n",
              "2           2  LP002086  Female  ...            1.0         Urban           0\n",
              "3           3  LP001136    Male  ...            1.0         Urban           1\n",
              "4           4  LP002529    Male  ...            1.0     Semiurban           1\n",
              "5           5  LP001603    Male  ...            1.0     Semiurban           0\n",
              "6           6  LP001273    Male  ...            NaN     Semiurban           0\n",
              "7           7  LP002706    Male  ...            0.0     Semiurban           1\n",
              "8           8  LP001859    Male  ...            1.0         Rural           0\n",
              "9           9  LP001228    Male  ...            0.0         Urban           0\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1QOcgOe26MF",
        "outputId": "893f9ac1-197c-4bc1-f0a2-494ecff6fa43"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 491 entries, 0 to 490\n",
            "Data columns (total 14 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Unnamed: 0         491 non-null    int64  \n",
            " 1   Loan_ID            491 non-null    object \n",
            " 2   Gender             481 non-null    object \n",
            " 3   Married            490 non-null    object \n",
            " 4   Dependents         482 non-null    object \n",
            " 5   Education          491 non-null    object \n",
            " 6   Self_Employed      462 non-null    object \n",
            " 7   ApplicantIncome    491 non-null    int64  \n",
            " 8   CoapplicantIncome  491 non-null    float64\n",
            " 9   LoanAmount         475 non-null    float64\n",
            " 10  Loan_Amount_Term   478 non-null    float64\n",
            " 11  Credit_History     448 non-null    float64\n",
            " 12  Property_Area      491 non-null    object \n",
            " 13  Loan_Status        491 non-null    int64  \n",
            "dtypes: float64(4), int64(3), object(7)\n",
            "memory usage: 53.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cc5FDvP73XT8",
        "outputId": "8b803b9e-8b7f-4327-92a0-b75d3e64ba98"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0            0\n",
              "Loan_ID               0\n",
              "Gender               10\n",
              "Married               1\n",
              "Dependents            9\n",
              "Education             0\n",
              "Self_Employed        29\n",
              "ApplicantIncome       0\n",
              "CoapplicantIncome     0\n",
              "LoanAmount           16\n",
              "Loan_Amount_Term     13\n",
              "Credit_History       43\n",
              "Property_Area         0\n",
              "Loan_Status           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTGFLuG_4eTY",
        "outputId": "b466cf04-7cce-48d6-c763-e5e848577380"
      },
      "source": [
        "data['Loan_Status'].value_counts()"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    343\n",
              "0    148\n",
              "Name: Loan_Status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJU7wCtn3xd1"
      },
      "source": [
        "# Missing values Treatment numerical data\r\n",
        "import numpy as np\r\n",
        "from sklearn.impute import SimpleImputer\r\n",
        "\r\n",
        "mean_imputer = SimpleImputer(missing_values=np.nan,strategy=\"mean\")\r\n",
        "mean_imputer_CH = mean_imputer.fit(data[['Credit_History']])\r\n",
        "mean_imputer_LAT = mean_imputer.fit(data[['Loan_Amount_Term']])\r\n",
        "mean_imputer_LMT = mean_imputer.fit(data[['LoanAmount']])\r\n",
        "\r\n",
        "data['Credit_History']=mean_imputer_CH.transform(data[['Credit_History']]).ravel()\r\n",
        "data['Loan_Amount_Term']=mean_imputer_LAT.transform(data[['Loan_Amount_Term']]).ravel()\r\n",
        "data['LoanAmount']=mean_imputer_LMT.transform(data[['LoanAmount']]).ravel()\r\n"
      ],
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "DQYiFd_pOZtM",
        "outputId": "f526218b-cd28-444c-d3f8-618d50b64158"
      },
      "source": [
        "data.head(5)"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>LP002305</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4547</td>\n",
              "      <td>0.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>360.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>LP001715</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3+</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5703</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>360.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>LP002086</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4333</td>\n",
              "      <td>2451.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>360.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>LP001136</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4695</td>\n",
              "      <td>0.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>145.014737</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>LP002529</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6700</td>\n",
              "      <td>1750.0</td>\n",
              "      <td>230.0</td>\n",
              "      <td>300.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0   Loan_ID  Gender  ... Credit_History Property_Area Loan_Status\n",
              "0           0  LP002305  Female  ...            1.0     Semiurban           1\n",
              "1           1  LP001715    Male  ...            1.0         Rural           1\n",
              "2           2  LP002086  Female  ...            1.0         Urban           0\n",
              "3           3  LP001136    Male  ...            1.0         Urban           1\n",
              "4           4  LP002529    Male  ...            1.0     Semiurban           1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2j2E60e-26d",
        "outputId": "a5accad6-7682-48b6-b574-16c21d86ccf8"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0            0\n",
              "Loan_ID               0\n",
              "Gender               10\n",
              "Married               1\n",
              "Dependents            9\n",
              "Education             0\n",
              "Self_Employed        29\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": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6d2zNfQW_meS"
      },
      "source": [
        "# Missing values Treatment categorical data\r\n",
        "mean_imputer = SimpleImputer(missing_values=np.nan,strategy=\"most_frequent\")\r\n",
        "mean_imputer_SE = mean_imputer.fit(data[['Self_Employed']])\r\n",
        "mean_imputer_GEN = mean_imputer.fit(data[['Gender']])\r\n",
        "\r\n",
        "data['Self_Employed']=mean_imputer_SE.transform(data[['Self_Employed']]).ravel()\r\n",
        "data['Gender']=mean_imputer_GEN.transform(data[['Gender']]).ravel()"
      ],
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOCrtzedAcgy"
      },
      "source": [
        "#drop unwanted columns\r\n",
        "\r\n",
        "data.drop(['Unnamed: 0','Loan_ID','Married','Gender','Loan_Amount_Term'],axis=1,inplace=True)\r\n",
        "\r\n",
        "data.dropna(axis=0,inplace=True)"
      ],
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l9TQdWSTAhtq"
      },
      "source": [
        "data2=data.copy()"
      ],
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ld0zqEfyCy6t",
        "outputId": "39465060-c0ae-4d79-aae3-591ec510ab79"
      },
      "source": [
        "data2['Dependents'].unique()"
      ],
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0', '3+', '2', '1'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YN_fGVp3DfLD"
      },
      "source": [
        "# change data['Dependents']= 3+ as 3\r\n",
        "\r\n",
        "data2['Dependents']= data2['Dependents'].apply(lambda x: int(3) if x== str(\"3+\") else int(x) )"
      ],
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7w1lMaIWaj8",
        "outputId": "a2a9aca4-fa51-41d4-c9e6-dc6d711d1805"
      },
      "source": [
        "data2.isnull().sum()"
      ],
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dependents           0\n",
              "Education            0\n",
              "Self_Employed        0\n",
              "ApplicantIncome      0\n",
              "CoapplicantIncome    0\n",
              "LoanAmount           0\n",
              "Credit_History       0\n",
              "Property_Area        0\n",
              "Loan_Status          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yxsytKEdgwKF",
        "outputId": "dcc7ecd3-5b57-47b0-8515-91ed7cbee400"
      },
      "source": [
        "#remove male in self employed column\n",
        "data2.Self_Employed.value_counts()\n",
        "\n",
        "data2['Self_Employed']=data2['Self_Employed'].apply(lambda x: str('No') if x==str('Male') else x)\n",
        "data2.Self_Employed.value_counts()\n"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     419\n",
              "Yes     63\n",
              "Name: Self_Employed, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-CGEYJ1oiH4f",
        "outputId": "5a737b9d-b998-4875-c933-23d94e5e92d2"
      },
      "source": [
        "#remove other value in Credit_History\n",
        "data2.Credit_History.value_counts()\n",
        "data2['Credit_History']=data2['Credit_History'].apply(lambda x: float(1) if (x!=1 and x!=0) else x)\n",
        "data2.Credit_History.value_counts()"
      ],
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    418\n",
              "0.0     64\n",
              "Name: Credit_History, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TcVCOkoQA-L"
      },
      "source": [
        "# one not Encoding\r\n",
        "\r\n",
        "data2=pd.get_dummies(data2,columns=['Property_Area','Education','Self_Employed'])\r\n",
        "\r\n"
      ],
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "Hx7FPwH3PYaN",
        "outputId": "57c3daea-b9bd-4e8a-e020-61e0248c71f7"
      },
      "source": [
        "data2.head()"
      ],
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dependents</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Loan_Status</th>\n",
              "      <th>Property_Area_Rural</th>\n",
              "      <th>Property_Area_Semiurban</th>\n",
              "      <th>Property_Area_Urban</th>\n",
              "      <th>Education_Graduate</th>\n",
              "      <th>Education_Not Graduate</th>\n",
              "      <th>Self_Employed_No</th>\n",
              "      <th>Self_Employed_Yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>4547</td>\n",
              "      <td>0.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>5703</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>4333</td>\n",
              "      <td>2451.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>4695</td>\n",
              "      <td>0.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>6700</td>\n",
              "      <td>1750.0</td>\n",
              "      <td>230.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Dependents  ApplicantIncome  ...  Self_Employed_No  Self_Employed_Yes\n",
              "0           0             4547  ...                 1                  0\n",
              "1           3             5703  ...                 0                  1\n",
              "2           0             4333  ...                 1                  0\n",
              "3           0             4695  ...                 0                  1\n",
              "4           2             6700  ...                 1                  0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0apruCxQrHF"
      },
      "source": [
        "# data split\r\n",
        "x= data2.drop('Loan_Status',axis=1)\r\n",
        "y=data2['Loan_Status']"
      ],
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwmHspmySiJL",
        "outputId": "9aa4e1ca-5001-4f83-d3b8-119769cfea61"
      },
      "source": [
        "y"
      ],
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      1\n",
              "2      0\n",
              "3      1\n",
              "4      1\n",
              "      ..\n",
              "486    1\n",
              "487    1\n",
              "488    1\n",
              "489    0\n",
              "490    1\n",
              "Name: Loan_Status, Length: 482, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wm07cCdRD5eN",
        "outputId": "39db1069-aa7c-468a-afc9-625f6e7c9701"
      },
      "source": [
        "# handling data imbalance\r\n",
        "from imblearn.over_sampling import SMOTE\r\n",
        "\r\n",
        "sm = SMOTE(random_state = 25, sampling_strategy = 1.0)\r\n",
        "\r\n",
        "x_train,y_train = sm.fit_sample(x,y)"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhC5NG7wSGpU",
        "outputId": "5e07a082-d1a6-4787-b49e-50cc1f643c76"
      },
      "source": [
        "# model training\r\n",
        "\r\n",
        "from sklearn.tree import DecisionTreeClassifier\r\n",
        "\r\n",
        "tree= DecisionTreeClassifier(max_depth=3)\r\n",
        "\r\n",
        "tree.fit(x,y)"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=3, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QlghCW8sf0dq",
        "outputId": "bc51bc34-e8b7-43bf-fcc4-17b62f88f5af"
      },
      "source": [
        "print(data2.columns)\n",
        "print(data_test.columns)"
      ],
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Dependents', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
            "       'Credit_History', 'Loan_Status', 'Property_Area_Rural',\n",
            "       'Property_Area_Semiurban', 'Property_Area_Urban', 'Education_Graduate',\n",
            "       'Education_Not Graduate', 'Self_Employed_No', 'Self_Employed_Yes'],\n",
            "      dtype='object')\n",
            "Index(['Dependents', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
            "       'Credit_History', 'Property_Area_Rural', 'Property_Area_Semiurban',\n",
            "       'Property_Area_Urban', 'Education_Graduate', 'Education_Not Graduate',\n",
            "       'Self_Employed_No', 'Self_Employed_Yes'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61PRqqD8URBw"
      },
      "source": [
        "# Load test data\r\n",
        "data_test= pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/Loan_Data/loan_test.csv')"
      ],
      "execution_count": 212,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tq8_DAZrU2rR",
        "outputId": "1d22dc95-464f-4865-aeef-c6e3cbdb77ea"
      },
      "source": [
        "data_test.isnull().sum()"
      ],
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID              0\n",
              "Gender               3\n",
              "Married              2\n",
              "Dependents           6\n",
              "Education            0\n",
              "Self_Employed        3\n",
              "ApplicantIncome      0\n",
              "CoapplicantIncome    0\n",
              "LoanAmount           6\n",
              "Loan_Amount_Term     1\n",
              "Credit_History       7\n",
              "Property_Area        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72dotW-0VmHI",
        "outputId": "60660beb-edef-4f9f-cd59-6e3a51e660dc"
      },
      "source": [
        "data_test.info()"
      ],
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 123 entries, 0 to 122\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            123 non-null    object \n",
            " 1   Gender             120 non-null    object \n",
            " 2   Married            121 non-null    object \n",
            " 3   Dependents         117 non-null    object \n",
            " 4   Education          123 non-null    object \n",
            " 5   Self_Employed      120 non-null    object \n",
            " 6   ApplicantIncome    123 non-null    int64  \n",
            " 7   CoapplicantIncome  123 non-null    float64\n",
            " 8   LoanAmount         117 non-null    float64\n",
            " 9   Loan_Amount_Term   122 non-null    float64\n",
            " 10  Credit_History     116 non-null    float64\n",
            " 11  Property_Area      123 non-null    object \n",
            "dtypes: float64(4), int64(1), object(7)\n",
            "memory usage: 11.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwCff1JWVMiE"
      },
      "source": [
        "# Missing values Treatment categorical data\r\n",
        "mean_imputer = SimpleImputer(missing_values=np.nan,strategy=\"most_frequent\")\r\n",
        "mean_imputer_SE = mean_imputer.fit(data_test[['Self_Employed']])\r\n",
        "mean_imputer_DEP = mean_imputer.fit(data_test[['Dependents']])\r\n",
        "mean_imputer_GEN = mean_imputer.fit(data_test[['Gender']])\r\n",
        "mean_imputer_MAR = mean_imputer.fit(data_test[['Married']])\r\n",
        "\r\n",
        "data_test['Self_Employed']=mean_imputer_SE.transform(data_test[['Self_Employed']]).ravel()\r\n",
        "data_test['Dependents']=mean_imputer_DEP.transform(data_test[['Dependents']]).ravel()\r\n",
        "data_test['Gender']=mean_imputer_GEN.transform(data_test[['Gender']]).ravel()\r\n",
        "data_test['Married']=mean_imputer_MAR.transform(data_test[['Married']]).ravel()\r\n"
      ],
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTwU4cy0X8XW"
      },
      "source": [
        "# Missing values Treatment numerical data\r\n",
        "import numpy as np\r\n",
        "from sklearn.impute import SimpleImputer\r\n",
        "\r\n",
        "mean_imputer = SimpleImputer(missing_values=np.nan,strategy=\"mean\")\r\n",
        "mean_imputer_CH = mean_imputer.fit(data_test[['Credit_History']])\r\n",
        "mean_imputer_LAT = mean_imputer.fit(data_test[['Loan_Amount_Term']])\r\n",
        "mean_imputer_LMT = mean_imputer.fit(data_test[['LoanAmount']])\r\n",
        "\r\n",
        "data_test['Credit_History']=mean_imputer_CH.transform(data_test[['Credit_History']]).ravel()\r\n",
        "data_test['Loan_Amount_Term']=mean_imputer_LAT.transform(data_test[['Loan_Amount_Term']]).ravel()\r\n",
        "data_test['LoanAmount']=mean_imputer_LMT.transform(data_test[['LoanAmount']]).ravel()\r\n"
      ],
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUO4zy7aXVcG"
      },
      "source": [
        "#drop unwanted columns\r\n",
        "\r\n",
        "data_test.drop(['Loan_ID','Married','Gender','Loan_Amount_Term'],axis=1,inplace=True)\r\n"
      ],
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNsnwVVimSfb",
        "outputId": "d8355421-9807-4ddd-b1a2-24a246579736"
      },
      "source": [
        "data_test['Dependents'].value_counts()"
      ],
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      69\n",
              "2      23\n",
              "1      17\n",
              "3+      8\n",
              "Yes     6\n",
              "Name: Dependents, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G34gZGAbZCmX"
      },
      "source": [
        "# one not Encoding\r\n",
        "\r\n",
        "data_test=pd.get_dummies(data_test,columns=['Property_Area','Education','Self_Employed'])\r\n"
      ],
      "execution_count": 219,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "id": "q1VjHY26ebSL",
        "outputId": "f7a77a5c-d5bd-4e7e-b358-7b6ceb2bd781"
      },
      "source": [
        "data_test.head(3)"
      ],
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dependents</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area_Rural</th>\n",
              "      <th>Property_Area_Semiurban</th>\n",
              "      <th>Property_Area_Urban</th>\n",
              "      <th>Education_Graduate</th>\n",
              "      <th>Education_Not Graduate</th>\n",
              "      <th>Self_Employed_No</th>\n",
              "      <th>Self_Employed_Yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3748</td>\n",
              "      <td>1668.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3+</td>\n",
              "      <td>4000</td>\n",
              "      <td>7750.0</td>\n",
              "      <td>290.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2625</td>\n",
              "      <td>6250.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Dependents  ApplicantIncome  ...  Self_Employed_No  Self_Employed_Yes\n",
              "0          0             3748  ...                 1                  0\n",
              "1         3+             4000  ...                 1                  0\n",
              "2          0             2625  ...                 1                  0\n",
              "\n",
              "[3 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INW0Kx-Rln9O",
        "outputId": "366ed199-44b4-474a-b9be-89be2bbb5c8d"
      },
      "source": [
        "data_test['Dependents'].unique()"
      ],
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0', '3+', '2', '1', 'Yes'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GlhVrnDHZPBP"
      },
      "source": [
        "# change data['Dependents']= 3+ as 3\r\n",
        "\r\n",
        "data_test['Dependents']= data_test['Dependents'].apply(lambda x: int(2) if x== str(\"Yes\") else str(x) )\r\n",
        "data_test['Dependents']= data_test['Dependents'].apply(lambda x: int(3) if x== str(\"3+\") else int(x) )"
      ],
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vojuUwGxX0bf"
      },
      "source": [
        "# Prediction\r\n",
        "output=tree.predict(data_test)"
      ],
      "execution_count": 223,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XO-h4nkmYxGw"
      },
      "source": [
        "#create output csv file\r\n",
        "\r\n",
        "frame=pd.DataFrame()\r\n",
        "frame['prediction']=output\r\n",
        "frame.to_csv(\"predictions.csv\",index=False)"
      ],
      "execution_count": 224,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dMvxNk4Z8q6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24f281be-282b-4544-d3f9-bafed1977e22"
      },
      "source": [
        "# save model\r\n",
        "import joblib\r\n",
        "\r\n",
        "joblib.dump(tree,\"model.pkl\")"
      ],
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GyVwADFcrSyq"
      },
      "source": [
        "# save features names\r\n",
        "\r\n",
        "import json\r\n",
        "\r\n",
        "features={\r\n",
        "    'data_columns':[col.lower() for col in data_test.columns]\r\n",
        "}\r\n",
        "\r\n",
        "with open('names.json','w') as f:\r\n",
        "  f.write(json.dumps(features))"
      ],
      "execution_count": 226,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H76p31qRuRQl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}