Calories Burned Prediction Model

This model uses a Simple Linear Regression algorithm to learn the relationship between exercise duration and calories burned, and it is deployed via a Flask web application.

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Features

• Predict calories burned from exercise duration in minutes.
• Clean and simple code structure.
• Trained with Linear Regression for interpretability.
• Flask web interface for easy user interaction.
• Model stored using pickle for reusability.

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Project Structure

CaloriesBurned-Prediction/
│
├── model.py            
├── app.py               
├── templates/
│   ├── index.html        
│   └── result.html       
├── static/
│   └── style.css         
├── calories.csv          
├── model.pkl             
├── README.md             
└── requirements.txt      

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Installation

1. Clone the repository:

   git clone https://github.com/harshini202005/LinearRegression.git
   cd LinearRegressor

2. Install dependencies:

   pip install -r requirements.txt

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Dataset

The dataset contains the following columns:

• Minutes: Duration of exercise in minutes.
• CaloriesBurned: Total calories burned during the exercise.

Example:

Minutes	CaloriesBurned
5	25
10	50
15	75

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Model Training

Run the following command to train and save the model:

python model.py

This will:

• Load the dataset.
• Train the Linear Regression model.
• Save the trained model as model.pkl.

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Running the App

Start the Flask server:

python app.py

Open your browser and go to:

http://127.0.0.1:5000

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Screenshots

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Requirements

• Python 3.8+
• Flask
• Pandas
• NumPy
• Scikit-learn

Install all dependencies using:

pip install -r requirements.txt

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Live Demo

You can try the model here: Calories Predictor Web App