This project aims to predict the number of medals a country will win in the Olympic Games using historical data and machine learning techniques. The project uses Python, pandas, and numpy for data manipulation and analysis, and scikit-learn for building the prediction model.
The goal of this project is to create a model that can predict the number of medals a country will win in the Olympics based on historical data. This can be useful for sports analysts, enthusiasts, and researchers interested in understanding the factors that contribute to a country's success in the Olympics.
- Data cleaning and preprocessing
- Exploratory data analysis
- Building a linear regression model
- Model evaluation and improvement
To get started, clone the repository and install the required dependencies:
git clone https://github.com/mrravipandee/olympic-medal-prediction.git
cd olympic-medal-predictionThe dataset used in this project consists of historical Olympic data, including the number of medals won by each country, and various features such as GDP, population, and more. You can obtain the dataset from Kaggle or other open data sources.
The project uses a linear regression model to predict the number of medals. The model is built using scikit-learn and includes steps for training, testing, and evaluating the model.
Contributions are welcome! If you have any ideas or improvements, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.