README.md

metadata

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