Health Analysis by Smartwatch using Machine Learning

Introduction

This project aims to perform health analysis using machine learning techniques by leveraging data collected from a smartwatch. The goal is to detect health issues early and provide timely interventions for improved healthcare outcomes. The project focuses on analyzing the heartbeat and Spo2 (oxygen saturation) levels obtained from the smartwatch to predict the individual's health condition.

Key Features

• Data collection from a smartwatch: The project involves collecting data related to heartbeat and Spo2 levels using a smartwatch.
• Machine Learning model: The collected data is used to train a logistic regression model to classify health conditions as normal or abnormal.
• Prediction: The trained model can predict the health condition based on user-input heartbeat and Spo2 levels.
• Visualization: Various visualizations, including pie charts, scatter plots, and bar charts, are used to present insights and feature importance.

Requirements

• Python 3.x
• Pandas
• NumPy
• Matplotlib
• Scikit-learn

Getting Started

1. Clone the repository: