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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
  1. 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.