HealthForAll

🧠 FitFounder AI – Data-Driven Fitness App Analytics Dashboard

🚀 Overview

FitFounder AI is a data-driven analytics and decision-support system built to support the launch and growth of a personalized fitness and nutrition app.

This project combines:

• Descriptive & Diagnostic Analytics
• Machine Learning Models
• Customer Segmentation
• Association Rule Mining
• Pricing Intelligence
• Future Customer Prediction System

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🎯 Key Features

Market Overview

• Customer demographics
• Fitness and diet behavior trends
• Feature demand analysis
• Willingness-to-pay distribution

Customer Diagnostics

• Pain-point analysis
• Churn reasons
• Behavior vs interest insights

Predictive Analytics

• Classification (Accuracy, Precision, Recall, F1, ROC)
• Regression (Willingness to pay)

Customer Segmentation

• K-Means clustering
• Persona identification

Association Rules

• Apriori algorithm
• Support, Confidence, Lift

Future Customer Prediction

Upload new data to:

• Predict app interest
• Predict willingness to pay
• Get marketing recommendations

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🗂️ Files Included

• app.py
• requirements.txt
• dataset files
• sample upload file

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⚙️ Setup

Install dependencies: pip install -r requirements.txt

Run app: streamlit run app.py

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🌐 Deployment

Upload all files to GitHub and deploy via Streamlit Cloud.

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📊 Models Used

• Logistic Regression
• Decision Tree
• Random Forest
• Gradient Boosting
• K-Means
• Apriori
• Linear Regression

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👨‍💼 Purpose

This project helps founders:

• Identify target customers
• Optimize pricing
• Design marketing strategies