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---
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)](https://www.kaggle.com/datasets/arashnic/dynamic-pricing-dataset)
- **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
```bash
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`](https://huggingface.co/spaces/PranavSharma/RidePricingInsightEngine)
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