Loan Approval Prediction

Overview

The Loan Approval Prediction project aims to predict whether a loan application will be approved or not based on various features.

Table of Contents

1. Project Setup
2. Data
3. Data Preprocessing
4. Model Training
5. Model Evaluation

1. Project Setup

Prerequisites

Before getting started, ensure you have the following:

• Python (>=3.6) installed
• Python virtual environment (optional but recommended)
• Necessary packages and libraries installed (pip install -r requirements.txt)

Installation

1. Clone this repository:

   git clone https://github.com/vikascod/Loan-Approven-EndtoEnd-Project.git
   cd Loan-Approven-EndtoEnd-Project
   
   

2. Data

Data Source The dataset used in this project is sourced from [data_source_url]. You can download it from [data_source_download_link] and place it in the data/ directory.

Data Description The dataset contains information about loan applicants, including features like gender, marital status, education, income, and more. The target variable is whether the loan was approved or not.

3. Data Preprocessing

Data Cleaning Handle missing values. Remove duplicates. Handle outliers if necessary. Feature Engineering Create new features if relevant. Encode categorical variables. Normalize or scale numerical variables.

4. Model Training

Classification Models Train various classification models such as Random Forest, Decision Tree, Logistic Regression, etc. Hyperparameter Tuning Use hyperparameter tuning techniques to optimize model performance.

5. Model Evaluation

Metrics Evaluate models using classification metrics like accuracy, precision, recall, F1-score, and ROC-AUC. Best Model Selection Select the best-performing model based on evaluation metrics.