Overview
Teamfight Tactics (TFT) is a strategy auto-battler game by Riot Games where players draft champions, equip items, and build team compositions to outperform opponents. This project applies machine learning techniques to analyze game data and recommend optimal team compositions, item placements, and augment choices based on the current game state.
Why TFT Recommender?
TFT has an enormous decision space with hundreds of champion combinations, items, and augments. Choosing the right comp each game is complex and depends on many factors like what other players are building, the current meta, and available augments. This project uses data-driven approaches to help players make more informed decisions and improve their gameplay.
Approach
Data Collection & Processing
Game data is collected and preprocessed using Pandas and NumPy. Champion traits, synergies, item combinations, and match outcomes are organized into structured datasets for model training.
ML-Based Recommendations
The recommendation engine analyzes patterns in successful compositions to suggest team builds. The model considers champion synergies, item effectiveness, and meta trends to provide tailored suggestions.