CS:GO Match Outcome Prediction

This project aims to predict the outcome of CS:GO matches using machine learning and deep learning techniques. The dataset used for training and evaluation is obtained from OpenML.

Files Included

• dataset.txt: Raw dataset obtained from OpenML.
• df.csv: Processed dataset after preprocessing and feature selection.
• csgo_prediction.ipynb: Jupyter notebook containing data preprocessing, model training, and evaluation code.

Instructions

1. Clone this repository to your local machine.
2. Ensure you have the necessary libraries installed (pandas, matplotlib, seaborn, scikit-learn, tensorflow, etc.).
3. Run the csgo_prediction.ipynb notebook to reproduce the results.

Conclusion

This project demonstrates the effectiveness of machine learning and deep learning techniques in predicting CS:GO match outcomes based on in-game statistics. The Random Forest Classifier performs the best among the models considered, achieving an accuracy of approximately 82.57% on the test set.