Clinical Trial Data Analysis and Game Recommender System

Project Overview

This project involves analyzing multi-year clinical trial data and developing a game recommender system using PySpark and collaborative filtering techniques. The aim is to derive insights from clinical data and provide personalized game recommendations based on user preferences.

Table of Contents

• Dataset
• Data Analysis
• Game Recommender System
• Technologies Used
• Contributing
• License

Dataset

The dataset consists of multi-year clinical trial data, which includes information on patient demographics, treatment outcomes, and trial specifics. This data is utilized to explore trends and draw conclusions relevant to clinical research.

Data Sources

• [Link to the clinical trial dataset or description]
• [Additional datasets used, if any]

Objectives

1. Data Analysis:

   • Perform exploratory data analysis (EDA) to uncover trends and insights from the clinical trial data.
   • Visualize key findings to communicate results effectively.

2. Game Recommender System:

   • Develop a collaborative filtering-based recommender system to suggest games to users.
   • Evaluate the performance of the recommender system and refine the model based on user feedback.

Technologies Used

• Python
• PySpark
• Pandas
• NumPy
• Matplotlib (for visualizations)
• scikit-learn

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any enhancements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for details.