Loan Eligibility Prediction

This project aims to predict the eligibility of loan applicants based on various demographic and financial attributes. The dataset for this project is sourced from the Analytics Vidhya Loan Prediction competition on Kaggle.

Project Overview

The goal is to build a predictive model that classifies whether an applicant will be eligible for a loan based on features such as gender, marital status, income, and credit history.

Data Source

• The dataset used in this project can be found on Kaggle: Loan Prediction Data.

Repository Structure

• data/: Contains the dataset used for training the model.
• notebooks/: Jupyter notebooks with detailed exploratory data analysis (EDA), feature engineering, and model training.
• src/: Python scripts for data preprocessing, model training, and evaluation.
• README.md: Project overview and instructions.

Data Preprocessing

• Handling Missing Values: Missing values were imputed using the mode for categorical variables and the mean for numerical variables.
• Outlier Removal: Outliers were detected and removed using the Interquartile Range (IQR) method.
• Skewed Distribution: Features like ApplicantIncome, CoapplicantIncome, and LoanAmount were transformed using the square root transformation to handle skewed distributions.
• One-Hot Encoding: Categorical variables were converted into numerical format using one-hot encoding, and unnecessary columns were dropped.

Model

• The model used in this project is a Random Forest Classifier with hyperparameters tuned for optimal performance.
• The dataset had an imbalanced class distribution, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the classes.
• The model was evaluated using accuracy, ROC-AUC score, and a detailed classification report.

Model Performance

• Accuracy: 83.72%
• ROC-AUC: 94.20%

Feature Importance

The Random Forest model provided insights into the most important features influencing loan eligibility. A bar chart of feature importances was generated to visualize the contributions of different features.

Installation and Usage

To run this project locally:

1. Clone the repository:

   git clone https://github.com/Ismat-Samadov/Loan_Eligiblity.git

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the notebook or script to train the model:

   jupyter notebook notebooks/Loan_Eligibility_Prediction.ipynb

   or

   python src/train_model.py

Dependencies

• Python 3.7+
• pandas
• numpy
• scikit-learn
• imbalanced-learn
• seaborn
• matplotlib

Contributing

Feel free to open an issue or submit a pull request if you would like to contribute to this project.