Bank Loan Prediction System using Streamlit

Bank Loan Prediction System

These days, with everything being data-based, predicting if someone can get a loan or not can really help banks work faster. This Bank Loan Prediction System is made using Streamlit and gives you a live, interactive web app that runs with machine learning models trained on past data.The project is made in a clean, pro way and it’s actually a paid one. It’s great for students, devs, or even companies who wanna learn about loan prediction or actually use it in real life.

Project Overview

This system predicts whether a loan should be approved based on several applicant details like income, credit history, marital status, employment, etc. Developed using Python and Streamlit, the application is powered by a pre-trained ML model and provides an interactive interface to submit applicant data and get predictions instantly.

Project Details

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Machine Learning and Data Processing

• Jupyter Notebook (Str_Loan_ML.ipynb)
• Cleaned and processed dataset files located in the Loan_Data/ folder
• A pre-trained model stored in Model/ML_Model.pkl used by the main Streamlit app

Files Included in the Project

• Bank_Loan_Prediction.py – Main Streamlit application
• Str_Loan_ML.ipynb – Jupyter notebook for preprocessing and model training
• requirements.txt – Lists all required Python libraries
• Loan_Data/train.csv and Loan_Data/test.csv – Dataset used for training/testing
• Model/ML_Model.pkl – Trained Machine Learning model
• Multiple screenshots and project preview images

Available Features

• User-friendly Web Interface using Streamlit
• Predicts loan approval eligibility
• Uses pre-trained machine learning model
• Clean and structured dataset included
• Easy to customize and retrain
• Includes complete Jupyter Notebook for data processing
• Professional UI with visual feedback
• Visual examples included for reference

Usage

1. Clone the repository or download the complete ZIP.
2. Install the required libraries using: pip install -r requirements.txt
3. Run the Streamlit app using: streamlit run Bank_Loan_Prediction.py

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