Dynamic Pricing Project

Overview

A project to implement dynamic pricing strategies for ride-sharing platforms using historical data and machine learning.

Features

• Predictive pricing model
• Real-time price adjustments
• Customer behavior analysis

Data

• Source: Dynamic Pricing Dataset
• Key Features:
  • Riders and drivers count
  • Location type (urban, suburban, rural)
  • Customer loyalty status
  • Booking time and vehicle type
  • Historical pricing data

Installation

1. Clone the repository:

   git clone https://github.com/imane0x/Dynamic-Pricing

2. Install dependencies:

   pip install -r requirements.txt

3. Train the model:

python main.py

4. Build and run the Docker container:

docker build -t dynamic-pricing .
docker run -p 8000:8000 dynamic-pricing

Hyperparameter Tuning

Uses Grid Search for tuning the Random Forest Regressor.

Weights & Biases

Integrated with wandb for experiment tracking.

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