FitPulse – Health Metrics Analysis & Dashboard

FitPulse is a data analytics project that focuses on understanding user activity and biometric patterns using a structured health dataset. The project covers data preprocessing, exploratory analysis, anomaly identification, and the creation of an interactive Streamlit dashboard for visual reporting.

🔍 Project Overview The goal of FitPulse is to analyze daily activity and health indicators such as steps, heart rate, pulse, age, and gender. The project includes:

• Cleaning and preparing the dataset
• Generating insights using NumPy, Pandas, and visualization libraries
• Building a regression model to study relationships between variables
• Detecting anomalies using predefined labels
• Presenting the results through a simple and interactive dashboard

📊 Streamlit Dashboard Features The dashboard provides the following visual insights:

1. Bar Chart – Average Heart Rate by Age Group
2. Line Chart – Average Steps by Age
3. Scatter Plot – Heart Rate vs Pulse
4. Table – High-Risk / Anomaly Users

These visualizations help in identifying health trends and potential risk users.

📂 Dataset Description The dataset includes the following attributes:

• customer_id
• age
• gender
• steps
• heart_rate
• pulse
• blood_points
• anomaly_label (0 = normal, 1 = anomaly)

▶️ Running the Streamlit Dashboard Install dependencies:

pip install -r Streamlit/requirements.txt