Data Bolts - Codedex Hackathon 2024

For graphing Olympic data and predicting the performance of countries.

Description

Data Bolt is a comprehensive tool designed to visualize Olympic data and predict future performance trends of various countries. By utilizing historical data and machine learning models, it generates insightful graphs and predictions, helping analysts and enthusiasts to understand and forecast Olympic outcomes.

Website: https://faisalmujawar148.github.io/

Video of the Website:

Getting Started

Dependencies

Python 3.6 or higher
pandas
numpy
seaborn
matplotlib
scikit-learn

To install the necessary Python libraries, you can use the following command:

pip install pandas numpy seaborn matplotlib scikit-learn

Installing

Clone the repository:

git clone https://github.com/yourusername/databolt.git

Navigate to the project directory:
```
cd databolt
```
Ensure the data files are in the correct directory structure as expected by the scripts.

Executing program

Ensure the data files are in the following paths:
- Data/Olympics/olympic_hosts.csv
- Data/Olympics/olympic_medals.csv
- Data/Olympics/olympic_results.csv
- Data/Olympics/olympic_athletes.csv
- Data/Olympics/Summer-Olympic-medals-1976-to-2008.csv
Run the visualization script:
```
python DataSetVisualier9000.py
```
Run the prediction script:
```
python OlymPicsGenerator500.py
```

The first script (DataSetVisualiser9000) will generate a singular graph, which can be saved if wanted. The second script (OlymPicsGenerator500) will generate plots based on the provided data and save them to an output folder directory.

Help

For common issues or further assistance, you can use the following command:

python -m pip help

Check if all required libraries are installed and the data paths are correct.

Authors

Contributors names and contact info:

Faisal Mujawar Linkedin
Mei Li Garcia O’Neil Linkedin
Anthony Padilla Linkedin

🏛️ License

MIT

Acknowledgments

Inspiration, code snippets, etc.: