This repository contains the necessary files and Jupyter Notebook (dscfirst.ipynb
) to predict the toss for IPL matches using machine learning techniques based on historical IPL data.
This project aims to develop a predictive model that can forecast the outcome of the coin toss in IPL matches. The Notebook (dscfirst.ipynb
) within this repository demonstrates the process of data analysis, preprocessing, model building, and evaluation for toss prediction.
- dscfirst.ipynb: Jupyter Notebook containing code and steps for toss prediction.
- samplesubmission.csv: Sample submission file format for predictions.
- test.csv: Dataset containing test data for model evaluation.
- train.csv: Dataset containing historical IPL data for model training.
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Clone the Repository: Use the following command to clone this repository to your local machine:
git clone https://github.com/avulaankith/IPL-Toss-Prediction.git
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Navigate to Repository Directory: Access the repository directory:
cd ipl20-dream11
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Open Jupyter Notebook: Launch and run the dscfirst.ipynb Jupyter Notebook using Jupyter Notebook or JupyterLab:
jupyter notebook dscfirst.ipynb
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Notebook Usage: Follow the instructions and comments within the Notebook to understand the analysis, preprocessing, model building, and prediction process.
- train.csv: Contains historical IPL data used for training the model.
- test.csv: Contains test data for evaluating the model's performance.
- samplesubmission.csv: Demonstrates the expected format for submitting predictions.
Ensure the following libraries are installed:
- Python 3
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Jupyter Notebooks
pip install pandas numpy scikit-learn matplotlib jupyter
Contributions to improve this project are welcome! If you find any bugs or have suggestions for enhancements, feel free to open an issue or create a pull request.
This project is licensed under the terms of the MIT License. See the LICENSE file for more details.