This project focuses on building a machine learning model to predict the outcomes of sports events. Using historical match data, player statistics, and other relevant features, the model aims to forecast the result of upcoming games (e.g., win/loss/draw).

• Data preprocessing and feature engineering for sports datasets.
• Exploratory Data Analysis (EDA) to understand patterns and trends.
• Training and evaluation of classification models (e.g., Logistic Regression, Random Forest, XGBoost).
• Hyperparameter tuning for optimal model performance.
• Deployment-ready script for predicting future matches.

├── data/                         # Raw and processed datasets
│   ├── matches.csv               # Match data (downloaded dataset)
│   ├── processed_data.csv        # Preprocessed dataset
├── src/                          # Source code for the project
│   ├── data_preprocessing.py     # Data cleaning and feature engineering
│   ├── model_training.py         # Model training and evaluation
│   ├── prediction_script.py      # Script for predicting future matches
├── notebooks/                    # Jupyter notebooks for EDA and testing
│   ├── sports_prediction.ipynb   # EDA and preliminary modeling
├── configs/                      # Configuration files
│   ├── model_config.yaml         # Model hyperparameters
├── README.md                     # Project documentation

We use the Football Dataset from Kaggle, which contains match results, team statistics, and player data. Alternatively, you can use other sports datasets, such as NBA or cricket data.

1. Clone this repository:

   git clone https://github.com/yourusername/sports-prediction-model.git
   cd sports-prediction-model

2. Install the required dependencies:

   pip install -r requirements.txt

3. Download the dataset and place it in the data/ folder.
4. Run the Jupyter notebook in the notebooks/ folder for EDA.
5. Train the model using:

   python src/model_training.py

6. Make predictions using:

   python src/prediction_script.py