x-suni/KPL-PREDICTION-SYSTEM

Use of data to be able to predict the KENYA PRIEMER LEAGUE games on which teams will win by doing calculations and probabilities. # Kenya Premier League Match Predictor ## Overview The Kenya Premier League Match Predictor is a machine learning-based system designed to predict match outcomes using historical data. The system uses various features such as team performance, player statistics, and historical match results to generate predictions. ## Features - **Data Collection**: Gather match data, player statistics, and team performance records. - **Preprocessing**: Clean and transform the data for use in machine learning models. - **Feature Engineering**: Extract relevant features such as recent form, head-to-head records, and team strength. - **Model Training**: Use machine learning algorithms like Logistic Regression, Random Forest, or Neural Networks. - **Prediction**: Provide probabilities of match outcomes (Win, Draw, Loss). - **Evaluation**: Assess model accuracy using metrics such as precision, recall, and F1-score. ## Workflow 1. **Data Collection** - Obtain historical match results from sources such as API feeds or CSV datasets. - Include features like team form, goals scored/conceded, and player ratings. 2. **Data Preprocessing** - Handle missing values. - Normalize numerical data. - Convert categorical data (e.g., teams) into numerical format using encoding techniques. 3. **Feature Engineering** - Compute rolling averages for team performance. - Analyze home vs away performance. - Extract key player statistics. 4. **Model Selection and Training** - Train models using algorithms such as: - Logistic Regression - Decision Trees - Random Forest - Gradient Boosting - Tune hyperparameters using cross-validation. 5. **Prediction** - Input current match data into the trained model. - Generate match outcome probabilities. 6. **Model Evaluation** - Compare predictions with actual results. - Use accuracy, precision, recall, and F1-score to assess performance. ## Technologies Used - **Python** for scripting and model development. - **Pandas & NumPy** for data processing. - **Scikit-Lear …