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 -
Navigate to Repository Directory: Access the repository directory:
cd ipl20-dream11 -
Open Jupyter Notebook: Launch and run the dscfirst.ipynb Jupyter Notebook using Jupyter Notebook or JupyterLab:
jupyter notebook dscfirst.ipynb -
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.