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https://github.com/hootbu/nfl-athlete-performance

The project utilizes Python to analyze and visualize daily performance data of a NFL athlete. The dataset offers valuable insights into various aspects of player performance.
https://github.com/hootbu/nfl-athlete-performance

kaggle-dataset nfl python random-forest regression

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The project utilizes Python to analyze and visualize daily performance data of a NFL athlete. The dataset offers valuable insights into various aspects of player performance.

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# NFL Athlete Performance
###### Created by Emir Yorgun

## Overview

This project encompasses comprehensive analyses to optimize the performance of NFL player Justin Hillard. Utilizing Random Forest Regression in Python, the project aims to provide insights into various performance metrics.

## Objectives

- **Performance Analysis:** Calculate the relationship between general features and overall performance.
- **Sleep and Performance Correlation:** Examine the relationship between sleep patterns and next-day performance.
- **Training Effectiveness:** Assess whether the targeted performance from training sessions is being achieved.

## Dataset

The dataset used in this project is the [NFL Daily Performance Dataset by Justin Hilliard](https://www.kaggle.com/datasets/justinhilliard97/nfl-daily-performance-dataset-justin-hilliard), which includes comprehensive information on NFL athlete's daily performances.

## Getting Started

To get started with this project, follow these steps:

1. Clone the repository:
```bash
git clone https://github.com/hootbu/NFL-Athlete-Performance.git
```
2. Navigate to the project directory:
```bash
cd NFL-Athlete-Performance
```
3. Install the necessary dependencies:
```bash
pip install -r requirements.txt
```

## Usage

1. Start Jupyter Notebook:
```bash
Jupiter notebook
```
2. Open the `Performance.ipynb` file and run.

## Acknowledgements

A big thank you to Justin Hilliard for providing the dataset on Kaggle.