Smartwatch-adoption-for-Fitness-goals

Overview

This project explores the relationship between user attributes and smartwatch data to predict fitness goals, specifically the number of steps a user should aim for. It utilizes machine learning models, including linear regression and random forest regression, to make these predictions based on user characteristics.

Dataset

We used a dataset obtained from Kaggle, which includes various user attributes and smartwatch data. The dataset contains the following columns:

• Age: User's age.
• Activity Level (Steps): User's current activity level measured in steps.
• Sleep (round off Hours): User's average sleep duration, rounded to the nearest hour.
• Heart Rate (BPM): User's average heart rate in beats per minute.
• Weight (kg): User's weight in kilograms.
• Height (cm): User's height in centimeters.
• Fitness Goals (Steps): The target variable we aim to predict, representing the user's fitness goal in terms of daily steps.

Model Training

We've employed two regression models for this project:

1. Linear Regression: A straightforward linear model that establishes a relationship between the input features and fitness goals.

2. Random Forest Regressor: An ensemble learning model that combines multiple decision trees to provide more accurate predictions.

Both models have been trained and evaluated to predict fitness goals based on the provided user attributes.

Usage

1. Clone the repository
2. Install the required Python libraries
3. Run the prediction script
5. Input your values for the following user attributes when prompted:

• Age
• Activity Level (Steps)
• Sleep (round off Hours)
• Heart Rate (BPM)
• Weight (kg)
• Height (cm)

5. The script will then use the trained model to predict your fitness goals (daily steps) based on the provided attributes.

Model Persistence

We've saved the trained model as a .pkl file. You can easily load this model and use it for predictions in your own applications.

Contributing

We welcome contributions to this project. If you have ideas for improvements or bug fixes, please open an issue or submit a pull request.

Acknowledgments

• Kaggle for providing the dataset.
• Scikit-learn for the machine learning libraries used in this project.

Feel free to reach out with any questions or feedback!