Welcome! This repository contains a collection of early data science projects from 2017 - my first steps into the world of data analysis and machine learning. These projects showcase explorations in sports analytics, interactive simulations, and predictive modeling across different domains.
Interactive Pokémon Battle Simulation
- Main Notebook: Pokemon Battle.ipynb
- Description: A fun interactive simulation that brings Pokémon battles to life! Create custom Pokémon with different types (Fire, Water, Grass), stats (attack, defense), and movesets, then watch them battle it out in turn-based combat. The system includes type effectiveness, health bars, and strategic gameplay elements.
- Features: Object-oriented Pokémon classes, type advantage calculations, interactive move selection, and real-time battle visualization.
German Bundesliga Data Analysis
- Main Notebook: Bundesliga Analysis.ipynb
- Data File: Bundesliga.csv
- Description: An in-depth analysis of German Bundesliga football data spanning multiple seasons. This project explores scoring patterns, seasonal trends, and statistical insights from one of Europe's top football leagues.
- Features: Goals per game analysis, temporal trend visualization, statistical distributions, and historical comparisons of match outcomes.
Fight Outcome Prediction Model
- Main Notebook: ufc-prediction.ipynb
- Data File: ufc-master.csv
- Description: A comprehensive machine learning project that predicts UFC fight outcomes using fighter statistics and historical data. The notebook is structured in three parts: baseline modeling, data visualization, and advanced feature engineering.
- Features: Fighter statistics analysis, stance effectiveness studies, predictive modeling with RandomForest, and extensive data visualization for insights into what makes a winning fighter.
Each project is self-contained within its respective folder. Simply navigate to any project directory and open the Jupyter notebook to explore the code, analysis, and results. The notebooks include detailed explanations and visualizations to help you understand the methodology and findings.
These projects represent learning experiments and early explorations in data science. Feel free to run the code, modify parameters, and extend the analyses with your own ideas. Whether you're interested in sports analytics, game simulations, or machine learning, there's something here to spark your curiosity!
Happy exploring! 🚀