Loan_acceptance_calculator_model_deployment-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n0MvFWcST6cu"
   },
   "source": [
    "**Steps to build the Loan Eligibility model**\n",
    "\n",
    "\n",
    "1.   Loading the dataset\n",
    "2.   Pre-processing the dataset\n",
    "3.   Building the Loan Prediction model\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ta6LNdQuUNX7"
   },
   "source": [
    "# 1. Loading the Dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XqYgHQ3qPwie"
   },
   "source": [
    "Dataset: https://www.kaggle.com/altruistdelhite04/loan-prediction-problem-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "Shtd3B6uPyg6",
    "outputId": "85919828-b0ad-42c7-ac2a-723e5a134b16"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "\n",
    "# loading the dataset\n",
    "train = pd.read_csv('train_u6lujuX_CVtuZ9i.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YoVoYzQafGn1",
    "outputId": "e9583f9b-5b0b-4bf8-894b-a312c02a4b5d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "isIoXflFiDMV",
    "outputId": "bbfc1bf2-807c-4e75-b914-367568d86407"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Credit_History       50\n",
      "Self_Employed        32\n",
      "LoanAmount           22\n",
      "Dependents           15\n",
      "Loan_Amount_Term     14\n",
      "Gender               13\n",
      "Married               3\n",
      "Loan_ID               0\n",
      "Education             0\n",
      "ApplicantIncome       0\n",
      "CoapplicantIncome     0\n",
      "Property_Area         0\n",
      "Loan_Status           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train.isna().sum().sort_values(ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SuhQA35-YALi",
    "outputId": "6d5f582b-aa91-4d93-c2b5-ec92d342542c"
   },
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JLPrMpvHUhej",
    "outputId": "c0424498-a98d-4c42-86f3-a0ab925e1d02"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    475\n",
       "0.0     89\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Credit_History.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ih7oK15riOTN",
    "outputId": "94cf0249-d1c3-462d-e0b1-ee9d3bb3928e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Loan_Status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JwB4R_VEXAT3",
    "outputId": "47f22bef-b829-4b74-c628-5037ebc75877"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    398\n",
       "No     213\n",
       "Name: Married, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Married.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZMC2dM_vXYFv",
    "outputId": "29995415-fb5e-4e2f-dc71-77d81baa3c92"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     500\n",
       "Yes     82\n",
       "Name: Self_Employed, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Self_Employed.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zSuN_dn6W6Vf",
    "outputId": "863df498-5c6e-4d16-f844-406591bf3917"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Semiurban    233\n",
       "Urban        202\n",
       "Rural        179\n",
       "Name: Property_Area, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Property_Area.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1Vsa19yMWu_r",
    "outputId": "67abd9f4-370b-4d29-84e8-580fa370f45f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    512\n",
       "180.0     44\n",
       "480.0     15\n",
       "300.0     13\n",
       "240.0      4\n",
       "84.0       4\n",
       "120.0      3\n",
       "60.0       2\n",
       "36.0       2\n",
       "12.0       1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Loan_Amount_Term.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cxDTGSAMUySf",
    "outputId": "dc16a7fa-55a1-4f6a-df43-793a69d0ecdd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     345\n",
       "1     102\n",
       "2     101\n",
       "3+     51\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Dependents.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JvwwYyXTU-JR",
    "outputId": "3f6aef47-74b7-45ea-e0c6-a05451ae8273"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Graduate        480\n",
       "Not Graduate    134\n",
       "Name: Education, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Education.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "028AIuyDVFMd",
    "outputId": "97c680ce-df26-4f0d-ddc5-31ba70332c3b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      489\n",
       "Female    112\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IferkTZ8WSvV"
   },
   "source": [
    "# 2. Pre-processing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "KGsyiplsWUCY"
   },
   "outputs": [],
   "source": [
    "# converting categories into numbers\n",
    "train['Gender']= train['Gender'].map({'Male':0, 'Female':1})\n",
    "train['Married']= train['Married'].map({'No':0, 'Yes':1})\n",
    "train['Self_Employed']= train['Self_Employed'].map({'No':1, 'Yes':0})\n",
    "train['Dependents']= train['Dependents'].map({'0':0, '1':1, '2':2, '3+':3})\n",
    "train['Education']= train['Education'].map({'Graduate':1, 'Not Graduate':0})\n",
    "train['Property_Area']= train['Property_Area'].map({'Rural':0, 'Semiurban':1, 'Urban':2})\n",
    "train['Loan_Status']= train['Loan_Status'].map({'Y':1, 'N':0})\n",
    "train=train.drop('Loan_ID', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rOkTbS64Ymk0",
    "outputId": "621c2b35-9aac-4a66-b845-6f147bcb2e11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender               float64\n",
       "Married              float64\n",
       "Dependents           float64\n",
       "Education              int64\n",
       "Self_Employed        float64\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area          int64\n",
       "Loan_Status            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "id": "DK-63kmkfYdX",
    "outputId": "08e957d1-fc0f-4dab-9ee0-394827a332b4"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>5849</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0     0.0      0.0         0.0          1            1.0             5849   \n",
       "1     0.0      1.0         1.0          1            1.0             4583   \n",
       "2     0.0      1.0         0.0          1            0.0             3000   \n",
       "3     0.0      1.0         0.0          0            1.0             2583   \n",
       "4     0.0      0.0         0.0          1            1.0             6000   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0         NaN             360.0             1.0   \n",
       "1             1508.0       128.0             360.0             1.0   \n",
       "2                0.0        66.0             360.0             1.0   \n",
       "3             2358.0       120.0             360.0             1.0   \n",
       "4                0.0       141.0             360.0             1.0   \n",
       "\n",
       "   Property_Area  Loan_Status  \n",
       "0              2            1  \n",
       "1              0            0  \n",
       "2              2            1  \n",
       "3              2            1  \n",
       "4              2            1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KIIrclOrdkpZ",
    "outputId": "964fd68e-47ff-440b-dddd-c0fc8a913962"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Credit_History       50\n",
      "Self_Employed        32\n",
      "LoanAmount           22\n",
      "Dependents           15\n",
      "Loan_Amount_Term     14\n",
      "Gender               13\n",
      "Married               3\n",
      "Education             0\n",
      "ApplicantIncome       0\n",
      "CoapplicantIncome     0\n",
      "Property_Area         0\n",
      "Loan_Status           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train.isna().sum().sort_values(ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IangkEHwdnAX",
    "outputId": "aaedba0e-90f6-4710-9458-8ad1459b80bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'kuma_utils' already exists and is not an empty directory.\r\n"
     ]
    }
   ],
   "source": [
    "# !rm -r kuma_utils\n",
    "!git clone https://github.com/analokmaus/kuma_utils.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "nywTm5MQdnEO"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"kuma_utils/\")\n",
    "from kuma_utils.preprocessing.imputer import LGBMImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "GhZn4SMYdt5n"
   },
   "outputs": [],
   "source": [
    "#Defining features of Songs\n",
    "fea = [\"Credit_History\",\n",
    "\"Self_Employed\",\n",
    "\"LoanAmount\",\n",
    "\"Dependents\",\n",
    "\"Loan_Amount_Term\",\n",
    "\"Gender\",\n",
    "\"Married\",\n",
    "\"Education\",\n",
    "\"ApplicantIncome\",\n",
    "\"CoapplicantIncome\",\n",
    "\"Property_Area\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84,
     "referenced_widgets": [
      "400b5f223714430ca0d3beb24a1c32e6",
      "147c3538d97c4c8aba778399152eb93a",
      "5b017eeb8cf940b68c548937c158b9cf",
      "3e1328841463402e894ba34fd62c4769",
      "c4b09dda192d4feabd942f1831689040",
      "69eae835bf1f4533b83b168df19dcf20",
      "9d678deaf86f4025b408f8ca6926e972",
      "0f7d9e5a79c94b07abef346697c3465d",
      "a35794b295b94931b27c44a6f4650050",
      "3fb2db8916424700ba4a0d83b16359b8",
      "4c1c91a22db84b32b5ed1534e2bd3a55"
     ]
    },
    "id": "l1y5rGrVduO8",
    "outputId": "7cc97832-3896-4099-ce99-803fadb44532"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5570a105bfa4ee29f0c3523ce8ee32e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.4 s, sys: 826 ms, total: 12.2 s\n",
      "Wall time: 1.66 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lgbm_imtr = LGBMImputer(n_iter=500)\n",
    "\n",
    "train_iterimp = lgbm_imtr.fit_transform(train[fea])\n",
    "\n",
    "# Create train test imputed dataframe\n",
    "train_imp_df = pd.DataFrame(train_iterimp, columns=fea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "id": "fWsB65GsKoRU",
    "outputId": "aa0ce7f9-7bea-44c7-f2d3-775cc994f8d3"
   },
   "outputs": [
    {
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      "text/plain": [
       "   Credit_History  Self_Employed  LoanAmount  Dependents  Loan_Amount_Term  \\\n",
       "0             1.0            1.0  147.584439         0.0             360.0   \n",
       "1             1.0            1.0  128.000000         1.0             360.0   \n",
       "2             1.0            0.0   66.000000         0.0             360.0   \n",
       "3             1.0            1.0  120.000000         0.0             360.0   \n",
       "4             1.0            1.0  141.000000         0.0             360.0   \n",
       "\n",
       "   Gender  Married  Education  ApplicantIncome  CoapplicantIncome  \\\n",
       "0     0.0      0.0          1             5849                0.0   \n",
       "1     0.0      1.0          1             4583             1508.0   \n",
       "2     0.0      1.0          1             3000                0.0   \n",
       "3     0.0      1.0          0             2583             2358.0   \n",
       "4     0.0      0.0          1             6000                0.0   \n",
       "\n",
       "   Property_Area  \n",
       "0              2  \n",
       "1              0  \n",
       "2              2  \n",
       "3              2  \n",
       "4              2  "
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "train_imp_df.head(5)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "id": "u4tC69buLgON",
    "outputId": "f8097ebb-6575-4a8d-f0ea-332601958c11"
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   "outputs": [
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       "   Credit_History  Self_Employed  LoanAmount  Dependents  Loan_Amount_Term  \\\n",
       "0             1.0            1.0  147.584439         0.0             360.0   \n",
       "1             1.0            1.0  128.000000         1.0             360.0   \n",
       "2             1.0            0.0   66.000000         0.0             360.0   \n",
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       "4             1.0            1.0  141.000000         0.0             360.0   \n",
       "\n",
       "   Gender  Married  Education  ApplicantIncome  CoapplicantIncome  \\\n",
       "0     0.0      0.0          1             5849                0.0   \n",
       "1     0.0      1.0          1             4583             1508.0   \n",
       "2     0.0      1.0          1             3000                0.0   \n",
       "3     0.0      1.0          0             2583             2358.0   \n",
       "4     0.0      0.0          1             6000                0.0   \n",
       "\n",
       "   Property_Area  Loan_Status  \n",
       "0              2            1  \n",
       "1              0            0  \n",
       "2              2            1  \n",
       "3              2            1  \n",
       "4              2            1  "
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_imp_df['Loan_Status'] = train['Loan_Status']\n",
    "train_imp_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "_NKPpdIgNIAm",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit_History  Self_Employed  LoanAmount  Dependents  Loan_Amount_Term  \\\n",
       "0             1.0            1.0  147.584439         0.0             360.0   \n",
       "1             1.0            1.0  128.000000         1.0             360.0   \n",
       "2             1.0            0.0   66.000000         0.0             360.0   \n",
       "3             1.0            1.0  120.000000         0.0             360.0   \n",
       "4             1.0            1.0  141.000000         0.0             360.0   \n",
       "\n",
       "   Gender  Married  Education  ApplicantIncome  CoapplicantIncome  \\\n",
       "0     0.0      0.0          1             5849                0.0   \n",
       "1     0.0      1.0          1             4583             1508.0   \n",
       "2     0.0      1.0          1             3000                0.0   \n",
       "3     0.0      1.0          0             2583             2358.0   \n",
       "4     0.0      0.0          1             6000                0.0   \n",
       "\n",
       "   Property_Area  Loan_Status  \n",
       "0              2            1  \n",
       "1              0            0  \n",
       "2              2            1  \n",
       "3              2            1  \n",
       "4              2            1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train_imp_df\n",
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4XqQ8mN-N3xm",
    "outputId": "2198dce1-f5d6-4383-cdd7-ceadabaea269"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 12)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "_1RFCX89OBdp"
   },
   "outputs": [],
   "source": [
    "# separating dependent and independent variables\n",
    "X = train[[col for col in train.columns if col not in 'Loan_Status']]\n",
    "y = train.Loan_Status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AKRi8X2ib800"
   },
   "source": [
    "**3. Building the Loan Prediction model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 487
    },
    "id": "9ujV4HJNcNTU",
    "outputId": "823d708b-9dc9-48b9-9746-2a085236838f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Education</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>66.000000</td>\n",
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       "      <td>120.000000</td>\n",
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       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>610</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>253.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>360.0</td>\n",
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       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
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       "      <th>612</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "<p>614 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Credit_History  Self_Employed  LoanAmount  Dependents  Loan_Amount_Term  \\\n",
       "0               1.0            1.0  147.584439         0.0             360.0   \n",
       "1               1.0            1.0  128.000000         1.0             360.0   \n",
       "2               1.0            0.0   66.000000         0.0             360.0   \n",
       "3               1.0            1.0  120.000000         0.0             360.0   \n",
       "4               1.0            1.0  141.000000         0.0             360.0   \n",
       "..              ...            ...         ...         ...               ...   \n",
       "609             1.0            1.0   71.000000         0.0             360.0   \n",
       "610             1.0            1.0   40.000000         3.0             180.0   \n",
       "611             1.0            1.0  253.000000         1.0             360.0   \n",
       "612             1.0            1.0  187.000000         2.0             360.0   \n",
       "613             0.0            0.0  133.000000         0.0             360.0   \n",
       "\n",
       "     Gender  Married  Education  ApplicantIncome  CoapplicantIncome  \\\n",
       "0       0.0      0.0          1             5849                0.0   \n",
       "1       0.0      1.0          1             4583             1508.0   \n",
       "2       0.0      1.0          1             3000                0.0   \n",
       "3       0.0      1.0          0             2583             2358.0   \n",
       "4       0.0      0.0          1             6000                0.0   \n",
       "..      ...      ...        ...              ...                ...   \n",
       "609     1.0      0.0          1             2900                0.0   \n",
       "610     0.0      1.0          1             4106                0.0   \n",
       "611     0.0      1.0          1             8072              240.0   \n",
       "612     0.0      1.0          1             7583                0.0   \n",
       "613     1.0      0.0          1             4583                0.0   \n",
       "\n",
       "     Property_Area  \n",
       "0                2  \n",
       "1                0  \n",
       "2                2  \n",
       "3                2  \n",
       "4                2  \n",
       "..             ...  \n",
       "609              0  \n",
       "610              0  \n",
       "611              2  \n",
       "612              2  \n",
       "613              1  \n",
       "\n",
       "[614 rows x 11 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "3-T8dqKfcSSt"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import time\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "85R1jhFIdfPU"
   },
   "outputs": [],
   "source": [
    "#spliiting train data for Optuna\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hUbo6pHe3kx0"
   },
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KA5oOgU4LFaq",
    "outputId": "714585d0-815e-4f49-e223-138c137ce571"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.45      0.60        65\n",
      "           1       0.76      0.97      0.86       120\n",
      "\n",
      "    accuracy                           0.79       185\n",
      "   macro avg       0.84      0.71      0.73       185\n",
      "weighted avg       0.81      0.79      0.77       185\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# training the logistic regression model\n",
    "Logreg = LogisticRegression() \n",
    "Logreg.fit(X_train, y_train)\n",
    "print(classification_report(y_test, Logreg.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pBHSqzi9YX1f",
    "outputId": "88ddb9cd-340d-4ccf-a885-629ce03f4d39"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7891891891891892"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, Logreg.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9IK2RCrXT6ub",
    "outputId": "6538654b-17f4-4520-b144-80fe6eb717af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 29,  36],\n",
       "       [  3, 117]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, Logreg.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pB_EpS_FQvZu",
    "outputId": "fc727379-636c-4012-e4a5-3cd5fdd8f4ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7815384615384615"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, Logreg.predict_proba(X_test)[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qkT7SxR63xe4"
   },
   "source": [
    "LGBM CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J9b84N2JLFib",
    "outputId": "94433659-7b40-408d-8e5d-5429a3cd1eca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.49      0.60        65\n",
      "           1       0.77      0.92      0.84       120\n",
      "\n",
      "    accuracy                           0.77       185\n",
      "   macro avg       0.77      0.70      0.72       185\n",
      "weighted avg       0.77      0.77      0.75       185\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lgbm = LGBMClassifier()\n",
    "lgbm.fit(X_train, y_train)\n",
    "print(classification_report(y_test, lgbm.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A4OwJyyyYg4h",
    "outputId": "af2793dd-daba-40e3-8ecb-a4d2c49cc5d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7675675675675676"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, lgbm.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QxiH-TusLFl0",
    "outputId": "5844147a-2bea-4df0-cd37-007a8519e4b7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7701282051282051"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, lgbm.predict_proba(X_test)[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oAhTKufC31s_"
   },
   "source": [
    "Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dBbpCKnLyCOH",
    "outputId": "03af4254-226f-4e28-a70b-7596042cabed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uDBNRFPcyCTU",
    "outputId": "d1f5c79b-58ec-42d9-ddc5-8b17a2229abe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        65\n",
      "           1       0.65      1.00      0.79       120\n",
      "\n",
      "    accuracy                           0.65       185\n",
      "   macro avg       0.32      0.50      0.39       185\n",
      "weighted avg       0.42      0.65      0.51       185\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, svc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WV8UElmI5RtC",
    "outputId": "befd6ebf-a70d-4cad-8606-2520202e4592"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6486486486486487"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, svc.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "coOxFKHn5Eog",
    "outputId": "856eb161-3a1d-4458-d384-99445ec613f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,  65],\n",
       "       [  0, 120]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, svc.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2atC84L55MYZ",
    "outputId": "ddc1d876-7d3b-471e-cb91-5dab3e5b1ab6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svcrbf = SVC(kernel= 'rbf')\n",
    "svcrbf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sEdD3VED95JC",
    "outputId": "dd7ab479-7361-4b4b-a732-d7dc14e2cf51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        65\n",
      "           1       0.65      1.00      0.79       120\n",
      "\n",
      "    accuracy                           0.65       185\n",
      "   macro avg       0.32      0.50      0.39       185\n",
      "weighted avg       0.42      0.65      0.51       185\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, svcrbf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Xf8tY2sS99r6",
    "outputId": "689287eb-8e5b-4913-a8fd-59026402274f"
   },
   "outputs": [
    {
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      "text/plain": [
       "0.6486486486486487"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, svcrbf.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rZVO1Mci-AuU",
    "outputId": "da3673a9-64a6-4da0-88ab-adbff59557a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,  65],\n",
       "       [  0, 120]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, svcrbf.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xPNJOegW-8UC"
   },
   "source": [
    "Hyperparameter optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [    \n",
    "    {'penalty' : ['l1', 'l2', 'elasticnet', 'none'],\n",
    "    'C' : np.logspace(-4, 4, 20),\n",
    "    'solver' : ['lbfgs','newton-cg','liblinear','sag','saga'],\n",
    "    'max_iter' : [100, 1000,2500, 5000]\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "clf = GridSearchCV(Logreg, param_grid = param_grid, cv = 10, verbose=True, n_jobs=-1)\n",
    "#best_clf = clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.08858667904100823, solver='newton-cg')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clf.best_estimator_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.42      0.57        65\n",
      "           1       0.76      0.98      0.86       120\n",
      "\n",
      "    accuracy                           0.78       185\n",
      "   macro avg       0.84      0.70      0.71       185\n",
      "weighted avg       0.82      0.78      0.76       185\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Logreg1 = LogisticRegression(C=0.08858667904100823, solver='newton-cg') \n",
    "Logreg1.fit(X_train, y_train)\n",
    "print(classification_report(y_test, Logreg1.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the model \n",
    "import pickle \n",
    "pickle_out = open(\"classifier.pkl\", mode = \"wb\") \n",
    "pickle.dump(Logreg1, pickle_out) \n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploying the machine learning model using streamlit\n",
    "\n",
    "1. Building the Frontend of the application\n",
    "2. Loading and Pre-processing the data\n",
    "3. Building the Machine Learning model to automate Loan Eligibility\n",
    "4. Deploying the application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Building the Frontend of the application\n",
    "\n",
    "1.1 Install Required Libraries<br>\n",
    "1.2 Creating the Frontend of the app using Streamlit\n",
    "\n",
    "### 1.1 Install Required Libraries##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install streamlit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Creating the frontend of the app using streamlit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting app.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile app.py\n",
    "\n",
    "# importing required libraries\n",
    "import pickle\n",
    "import streamlit as st\n",
    "\n",
    "# loading the trained model\n",
    "pickle_in = open('classifier.pkl', 'rb') \n",
    "classifier = pickle.load(pickle_in)\n",
    "\n",
    "# this is the main function in which we define our app  \n",
    "def main():       \n",
    "    # header of the page \n",
    "    html_temp = \"\"\" \n",
    "    <div style =\"background-color:yellow;padding:13px\"> \n",
    "    <h1 style =\"color:black;text-align:center;\">Check your Loan Eligibility</h1> \n",
    "    </div> \n",
    "    \"\"\"\n",
    "    st.markdown(html_temp, unsafe_allow_html = True) \n",
    "\n",
    "    # following lines create boxes in which user can enter data required to make prediction \n",
    "    Gender        = st.selectbox('Gender',(\"Male\",\"Female\",\"Other\"))\n",
    "    Married       = st.selectbox('Marital Status',(\"Unmarried\",\"Married\")) \n",
    "    Self_Employed = st.selectbox('Self_Employed',(\"Yes\",\"No\")) \n",
    "    Dependents    = st.selectbox('Number of Dependents',(\"0\",\"1\", \"2\", \"3+\")) \n",
    "    Education     = st.selectbox('Education level',(\"Graduate\",\"Not Graduate\")) \n",
    "    Property_Area = st.selectbox('Property_Area',(\"Rural\",\"Semiurban\", \"Urban\")) \n",
    "    \n",
    "    ApplicantIncome   = st.number_input(\"Monthly Income in Rupees\")\n",
    "    CoapplicantIncome = st.number_input(\"Coapplicant's Monthly Income in Rupees\")\n",
    "    LoanAmount        = st.number_input(\"Loan Amount in Rupees\")\n",
    "    Loan_Amount_Term  = st.number_input(\"Term for Loan Amount\")\n",
    "    Credit_History    = st.number_input(\"Credit_History\")\n",
    "    \n",
    "    \n",
    "    result =\"\"\n",
    "      \n",
    "    # when 'Check' is clicked, make the prediction and store it \n",
    "    if st.button(\"Check\"): \n",
    "        result = prediction(Gender, Married,Self_Employed,Dependents,Education,Property_Area,\n",
    "                            ApplicantIncome, CoapplicantIncome,LoanAmount,Loan_Amount_Term ,Credit_History) \n",
    "        st.success('Your loan is {}'.format(result))\n",
    "        \n",
    "# defining the function which will make the prediction using the data which the user inputs \n",
    "def prediction(Gender, Married,Self_Employed,Dependents,Education,Property_Area, ApplicantIncome,CoapplicantIncome,\n",
    "               LoanAmount,Loan_Amount_Term ,Credit_History): \n",
    "\n",
    "    # 2. Loading and Pre-processing the data \n",
    "\n",
    "    if Gender == \"Male\":\n",
    "        Gender = 0\n",
    "    else:\n",
    "        Gender = 1\n",
    "\n",
    "        \n",
    "    if Married == \"Yes\":\n",
    "        Married = 1\n",
    "    else:\n",
    "        Married = 0\n",
    "\n",
    "        \n",
    "    if Self_Employed == \"Yes\":\n",
    "        Self_Employed = 0\n",
    "    else:\n",
    "        Self_Employed = 1\n",
    "\n",
    "    \n",
    "    if Dependents == \"0\":\n",
    "        Dependents = 0\n",
    "    elif Dependents == \"1\":\n",
    "        Dependents = 1\n",
    "    elif Dependents == \"2\":\n",
    "        Dependents = 2\n",
    "    else:\n",
    "        Dependents = 3\n",
    "    \n",
    "    \n",
    "    if Education == \"Graduate\":\n",
    "        Education = 1\n",
    "    else:\n",
    "        Education = 0\n",
    "    \n",
    "    \n",
    "    if Property_Area == \"Rural\":\n",
    "        Property_Area = 0\n",
    "    elif Property_Area==\"Semiurban\":\n",
    "        Property_Area = 1\n",
    "    else:\n",
    "        Property_Area = 2\n",
    "\n",
    "    prediction = classifier.predict( \n",
    "        [[Gender, Married, Self_Employed,Dependents,Education,Property_Area,ApplicantIncome,CoapplicantIncome,\n",
    "          LoanAmount,  Loan_Amount_Term,Credit_History ]])\n",
    "     \n",
    "    if prediction == 0:\n",
    "        pred = 'Rejected'\n",
    "    else:\n",
    "        pred = 'Approved'\n",
    "    return pred\n",
    " \n",
    "\n",
    "     \n",
    "if __name__=='__main__': \n",
    "    main()"
   ]
  }
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