README.md

---
title: Group08 UrbanMobilityApp
emoji: 🚗
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: "4.31.0"
app_file: app.py
pinned: false
python_version: "3.10"
---

# 🚀 Urban Mobility Pricing & Satisfaction App

## 📊 Project Overview
This project analyzes a European urban mobility startup operating in:
- Paris
- Berlin
- Madrid
- Warsaw
- Turin

🎯 Goal:
Optimize pricing strategy and user satisfaction using:
- Ride data (quantitative)
- App reviews (qualitative)

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## 🧠 Pipeline

### 🔹 Notebook 1 – Data Processing
- Synthetic ride dataset (2,000 rides)
- Review dataset (500 reviews)
- Data cleaning & preprocessing
- VADER sentiment analysis
- Output: `merged_summary.csv`

### 🔹 Notebook 2 – Predictive Modelling
- Random Forest → predict user satisfaction
- Feature importance → price is key driver
- ARIMA → revenue forecasting
- Outputs:
  - `rf_model.pkl`
  - encoder files

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## 💻 Hugging Face App

### 📊 Dashboard
- KPI overview:
  - Average price
  - Rating
  - Sentiment
  - Cancellation rate
- Interactive charts by city and vehicle type

### 🤖 Prediction
- Input ride parameters
- Output:
  - Satisfaction probability
  - Predicted label (High / Low)

### 💡 Recommendation
- Pricing recommendation based on segment

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## ⚙️ Technologies Used
- Python (pandas, numpy)
- scikit-learn (Random Forest)
- statsmodels (ARIMA)
- VADER Sentiment Analysis
- Gradio (UI)
- Hugging Face Spaces

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## 📈 Key Insights
- Final price is the strongest driver of satisfaction
- E-scooters → highest usage but lower sentiment
- Discounts → improve ratings
- Revenue → stabilizing in mature markets

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## 📦 Files
- `app.py` → application
- `merged_summary.csv` → data
- `rf_model.pkl` → ML model
- encoders → feature transformation
- notebooks → full pipeline

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## 👥 Team
Group 08 – AI for Big Data Management