Loan Status Prediction

This project aims to develop a machine learning model to predict the loan status (approved or rejected) based on various features such as applicant income, credit history, loan amount, and other relevant factors. Additionally, a FastAPI application has been developed to serve the trained model for making predictions.

Dataset

The dataset used in this project is the "Loan Status Prediction" dataset from Kaggle. It contains information about loan applications, including the applicant's personal details, employment details, and loan-related information.

Code Structure

The project consists of the following files:

1. app.py: This file contains a FastAPI application that exposes an endpoint /predict/ for making predictions using the trained model.
2. loan - Kaggle.ipynb: This Jupyter Notebook contains the code for data exploration, preprocessing, feature engineering, model training, and evaluation.

Requirements

To run this project, you'll need the following dependencies:

• Python (version 3.6 or higher)
• NumPy
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn
• FastAPI
• Joblib (for model serialization)

Model Performance

The notebook (loan - Kaggle.ipynb) includes code for training and evaluating multiple machine learning models, such as Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, and Random Forest Classifier. The performance of each model is reported using accuracy as the evaluation metric.