Shill Bid Dataset Analysis 🎉

This web app allows users to interactively explore and analyze the Shill Bid dataset. The main goal of this app is to detect Shill bids using various filtering and machine learning techniques. The app provides detailed charts and visualizations to effectively communicate insights and patterns within the data. Users can also experiment with different feature values and classifiers to improve the detection of Shill bids.

Features

• Introduction: A brief overview of the Shill Bid dataset and the goals of the web app.
• Dataset: Displays the dataset and basic statistics to help users understand the data.
• Histograms: Interactive histograms for visualizing the distribution of each feature.
• Histogram Analyser: Allows users to explore and compare histograms for different features.
• Clusters: Visualizations of 2D and 3D clusters to identify Shill bids patterns.
• Filtering Shill Bids: An interactive feature to filter and display Shill bids based on feature values.
• Detecting Shill Bids: A machine learning based approach to detecting Shill bids using Decision Tree, KNN, and SVM classifiers.

Technologies Used

• Streamlit - A fast and easy-to-use library for building data-driven web apps.
• Pandas - A powerful data manipulation and analysis library.
• Plotly - A versatile graphing and visualization library.
• Seaborn - A library for making statistical graphics in Python.
• Matplotlib - A 2D plotting library for creating static, animated, and interactive visualizations in Python.
• Scikit-learn - A comprehensive library for machine learning, including classification, regression, clustering, and dimensionality reduction.

Getting Started

To use this web app, simply visit https://dataviz2.webtool.page or https://dataap.streamlit.app/ and start exploring the Shill Bid dataset interactively!

For running the app locally, follow these steps:

1. Clone this repository.
2. Install the required dependencies using pip install -r requirements.txt.
3. Run the app using streamlit run app.py.

Developers

This web app was developed with ❤ by Marcin Mrugacz.

License

This project is licensed under the MIT License.

Acknowledgements

Thanks to Streamlit, Pandas, Plotly, Seaborn, Matplotlib, and Scikit-learn for providing the amazing tools used to build this web app.