Esports Player Performance Prediction: A Complete Practice of CRISP-DM Methodology in Machine Learning

This project builds an end-to-end machine learning workflow based on the CRISP-DM methodology, with the core goal of predicting esports players' performance scores, covering the entire process of data exploration, modeling, and deployment. Key highlights include: identifying target variable leakage issues, applying multiple regression and classification algorithms, and developing an interactive Streamlit application. The project aims to bridge the gap between machine learning theory and practice, providing learners with a complete case reference.

Machine learning learners often face the problem of disconnection between theory and practice. This project takes esports player performance prediction as a scenario, combines the esports industry's needs for scientific training and state management of players, provides decision support for coaching teams through data-driven methods, and helps learners master full-process practical skills.

The project strictly follows the CRISP-DM standard process, focusing on implementing the first four phases: business understanding (clarifying prediction goals), data understanding (exploring data structure), data preparation (e.g., filtering noise data with reaction time <120ms based on IAAF standards), and modeling (training multiple models).

The core findings of the project include identifying target variable leakage issues (synthetic data leading to R²=1.0 for linear models), emphasizing the importance of exploratory data analysis (EDA). In terms of models, regression algorithms (linear, ridge, decision tree, random forest), classification algorithms (KNN, random forest classifier), and dimensionality reduction techniques are implemented.

The project develops an interactive web application through the Streamlit framework, with features including real-time prediction, visual display, and a user-friendly interface. Deployment steps: clone the repository → create a virtual environment → install dependencies → launch the application (see the original project for specific code examples).

The project is suitable for machine learning beginners (to master the full process), developers transitioning to data science (model deployment), esports practitioners (application potential), and educators (CRISP-DM teaching case). Its modular structure and detailed documentation make it easy for learners to follow.

Improvement directions: 1. Replace synthetic data with real esports datasets; 2. Try XGBoost/LightGBM or neural network models; 3. Add real-time data interfaces for game APIs; 4. Deploy to Heroku/AWS cloud platforms.