Credit Scoring & Loan Eligibility Predictor 🇮🇳

This project is a machine learning application that predicts an individual's creditworthiness and loan eligibility based on their past financial data. The model is deployed in an interactive web application built with Streamlit.

(Note: You will need to replace 'Screenshot-of-App.png' with the actual name of your screenshot file after uploading it to GitHub.)

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📋 Features

• End-to-End Machine Learning Pipeline: From data loading and preprocessing to model training and evaluation.
• Predictive Model: Uses a Logistic Regression model trained on the German Credit Data dataset.
• Data Preprocessing: Implements one-hot encoding for categorical variables and feature scaling (StandardScaler) for improved model performance.
• Model Evaluation: The model's performance is assessed using key metrics like Precision, Recall, F1-Score, and ROC-AUC Score.
• Interactive UI: A user-friendly web interface built with Streamlit that allows for real-time predictions.
• Localized for India: The UI has been adapted for an Indian context, using Rupees (₹) and more familiar financial terms.

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🛠️ Technologies Used

• Python: The core programming language.
• Pandas: For data manipulation and analysis.
• Scikit-learn: For building and evaluating the machine learning model.
• Streamlit: For creating and serving the web application.
• Joblib: For saving and loading the trained model and scaler.

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🚀 How to Run Locally

To run this application on your own machine, follow these steps:

1. Clone the Repository:

git clone [https://github.com/your-username/your-repository-name.git](https://github.com/your-username/your-repository-name.git)
cd your-repository-name
2. Install Dependencies:
It's recommended to create a virtual environment first. Then, install the required libraries from the requirements.txt file.

Bash

pip install -r requirements.txt
3. Run the Streamlit App:
Once the dependencies are installed, run the main application file:

Bash

streamlit run app.py
The application should now be running and accessible in your web browser.

📁 Project Structure
├── app.py                     # The main Streamlit application script
├── credit_model.py            # Script for data processing, model training, and evaluation
├── credit_model.joblib        # Saved trained Logistic Regression model
├── scaler.joblib              # Saved feature scaler
├── training_columns.joblib    # Saved column order for prediction
├── requirements.txt           # List of Python dependencies
└── README.md                  # This file