Explainable-AI

This project empowers humans to understand the "why" behind AI decisions by exploring Explainable AI (XAI) techniques. We demonstrate XAI with two practical models: house price prediction and Titanic survivor prediction.

Project Structure: House Price Prediction: Contains datasets used for training and testing the House Price Model Titanic Survivor Predicion: Contains datasets used for training and testing the Titanic survival prediction model. main: Contains the Streamlit code for building the interactive website.

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Technologies Used:

• Python: The primary programming language for building the AI models and XAI implementations.
• Streamlit: A Python framework used to create the interactive web application for user interaction and XAI explanation visualization.
• SHAP (SHapley Additive exPlanations): An XAI technique integrated into the house price prediction model to explain its decisions.
• LIME (Local Interpretable Model-agnostic Explanations): An XAI technique employed in the Titanic survival prediction model for interpretability.

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Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

• pip

  pip install -r requirements.txt

Running the project

streamlit run main.py

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch
3. Commit your Changes
4. Push to the Branch
5. Open a Pull Request

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