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README.md

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+# 🚀 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)
+
+---
+
+## 🧠 Pipeline
+
+### 1. Data Processing (Notebook 1)
+- Synthetic ride data (2,000 rides)
+- Review data (500 reviews)
+- Data cleaning & preprocessing
+- VADER sentiment analysis
+- Output: `merged_summary.csv`
+
+### 2. Predictive Modelling (Notebook 2)
+- Random Forest classifier → predict satisfaction
+- Feature importance → price is key driver
+- ARIMA → revenue forecasting
+- Output:
+  - `rf_model.pkl`
+  - encoder files
+
+---
+
+## 💻 Hugging Face App
+
+### 🔹 Dashboard
+- KPI overview (price, rating, sentiment, cancellation)
+- Interactive charts by city and vehicle type
+
+### 🔹 Prediction
+- Input ride parameters
+- Output:
+  - satisfaction probability
+  - predicted label (High / Low)
+
+### 🔹 Recommendation
+- Pricing recommendation logic based on segment
+
+---
+
+## ⚙️ Technologies Used
+- Python (pandas, numpy)
+- scikit-learn (Random Forest)
+- statsmodels (ARIMA)
+- VADER Sentiment Analysis
+- Gradio (UI)
+- Hugging Face Spaces
+
 ---
-title: SE21 App Template
-emoji: 📊
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-short_description: AI-enhanced analytics dashboard template for SE21 students
+
+## 📈 Key Insights
+- Final price is the strongest driver of satisfaction
+- E-scooters have highest usage but lower sentiment
+- Discounts improve ratings
+- Revenue stabilizing in mature markets
+
+---
+
+## 🔗 Deliverables
+- Hugging Face Space (this app)
+- Data pipeline notebooks (.ipynb)
+- Predictive model
+- Interactive dashboard
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 👥 Team
+Group 08 – AI for Big Data Management