This project focuses on performing an extensive analysis and visualization of heart rate data using Python. By exploring and analyzing heart rate patterns, the project aims to provide insights into how factors like physical activity, sleep, stress, and personal characteristics (e.g., age, gender) affect heart rate variability. Using various Python libraries, the project combines data exploration, visualization, and machine learning to understand heart health and detect anomalies in heart rate trends.
Data Exploration: Examine heart rate data to identify trends, patterns, and anomalies. Activity & Heart Rate Correlation: Analyze the relationship between heart rate and activity levels (e.g., exercise, rest, sleep). Visualization: Use Python’s visualization libraries to create informative plots that highlight key insights. Anomaly Detection: Identify unusual heart rate readings that may indicate health issues. Predictive Modeling: Develop machine learning models to predict heart rate or detect health risks based on various factors.
Data Analysis: Process and analyze heart rate data to gain insights into how heart rate changes with various activities or stress factors. Visual Exploration: Visualize time series data to detect trends and cycles in heart rate. Plot correlations between different factors and heart rate. Anomaly Detection: Use statistical methods to identify outliers in heart rate data that may signal abnormal conditions. Machine Learning: Build and train models to predict heart rate based on various factors (e.g., age, activity, sleep). Explore both supervised and unsupervised learning techniques. Interactive Visualizations: Leverage interactive tools to create user-friendly charts for in-depth analysis.
Python Libraries: Pandas: Data manipulation and cleaning. NumPy: Numerical operations for processing data. Matplotlib & Seaborn: Static data visualizations (line plots, scatter plots, histograms). Plotly: Interactive visualizations (for better user experience). Jupyter Notebooks: For interactive data exploration and reporting. Google Colab: For cloud-based notebook execution (optional).