📊 Loan Creditworthiness Classification Dashboard

This Streamlit-based dashboard helps visualize and compare the performance of multiple machine learning models trained to predict loan applicant creditworthiness.

🚀 Features

• Upload your custom dataset or use the preloaded one
• Clean and preprocess data automatically
• Train and evaluate 7 machine learning models:
  • Logistic Regression
  • Random Forest
  • Gradient Boosting
  • SVM
  • KNN
  • MLP (Neural Network)
  • Extra Trees
• Visualize metrics:
  • Accuracy
  • Precision
  • Recall
  • F1 Score
• Compare model performance through bar graphs and confusion matrices
• Auto-select the best model based on accuracy
• Toggle between Light 🌞 and Dark 🌙 themes
• Minimal and responsive layout

📁 Folder Structure

Loan-Creaditworthiness-classification-main/ ├── Archives/ │ ├── data_processing.py │ ├── model_training.py │ └── visualisation.py ├── data/ │ └── Preprocessed/ │ └── final.csv ├── app.py └── requirements.txt

📦 Installation

1. Clone the repo:

     git clone https://github.com/your-username/Loan-Creaditworthiness-classification.git
     cd Loan-Creaditworthiness-classification
   

2. Create a virtual environment (optional but recommended):

  python -m venv venv
  source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

  pip install -r requirements.txt

4. Run the app:

  streamlit run app.py

📸 Screenshot

** Video Demo 📼

https://youtu.be/a6mWAd7iBGE

📌 Requirements

Python 3.7+ Streamlit Pandas Plotly scikit-learn 🙌 Credits

Developed by Prajwal Waykos, Eklavya Mishra, Ashutosh Vashishth for a hackathon project focused on automating credit risk prediction.

📃 License

This project is licensed under the MIT License.

PPT -

Artificial Intelligence Presentation.pdf

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Let us know if you want help with a logo or uploading a screenshot to your repo too.

Ashutosh Vashishth - ashutoshavashishth99@gmail.com Prajwal Waykos - pwaykos1@gmail.com Eklavya Mishra - eklavyamishrax@gmail.com