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6.5.1
metadata
title: RidePricingInsightEngine
emoji: 🚗
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.22.0
app_file: app.py
pinned: false
license: mit
short_description: ML-powered ride fare prediction using regression
Dynamic Ride Pricing Optimization
🚀 Overview
This project presents a machine learning-based solution for ride fare prediction and optimization, using a rich synthetic dataset of historical ride data. By analyzing real-time supply-demand conditions and customer attributes, the system aims to help ride-sharing platforms implement data-driven dynamic pricing strategies.
📊 Dataset
- Source: Dynamic Pricing Dataset (Kaggle)
- Description: A synthetic dataset designed for ride-sharing fare prediction. Features include:
- Number of Riders / Drivers
- Location Category
- Loyalty Status
- Number of Past Rides
- Ratings
- Booking Time
- Vehicle Type
- Expected Ride Duration
- Historical Cost of Ride
🔍 Problem Statement
The objective is to identify which features most influence ride fares and approximate pricing behavior through various regression models. This includes:
- Finding key predictors for optimal fare setting.
- Estimating price sensitivity across rider and supply characteristics.
- Offering recommendations for dynamic fare adjustments in real time.
🧠 Model & Features
- Regression-based modeling (e.g., Linear, Ridge, XGBoost)
- Final model: Huber Regressor with non-zero intercept — robust to outliers, encouraging sparse feature usage
- Feature selection pipeline to reduce unnecessary variables
- Price approximation engine that generalizes across different booking conditions
- Gradio-powered UI for hands-on exploration
🛠️ Tools and Libraries
- Python
- Scikit-learn, XGBoost
- Pandas, NumPy
- Gradio for app deployment
- Plotly for interactive visualization
🧪 How to Run Locally
git clone https://github.com/Sharma-Pranav/Portfolio.git
cd Portfolio/projects/DynamicPricingOptimization
pip install -r requirements.txt
python app.py
📈 Results
- Clear insights into fare-driving features like rider demand, loyalty, and time of day.
- Huber Regressor outperformed others due to robustness and minimal feature reliance.
- Optimal pricing zones visualized for strategic recommendations.
- Portable deployment for teams to simulate and iterate pricing strategies.
✨ Developed by Pranav Sharma | 🚀 Hugging Face Space:
RidePricingInsightEngine