https://github.com/zahramh99/dynamic-pricing-strategy

Dynamic Pricing is an application of data science that involves adjusting the prices of a product or service based on various factors in real time. It is used by companies to optimize revenue by setting flexible prices that respond to market demand, demographics, customer behaviour and competitor prices.
https://github.com/zahramh99/dynamic-pricing-strategy

business-intelligence data-science data-visualization demand-prediction dynamic-pricing machine-learning predictive-modeling price-prediction price-prediction-model pricing-strategy revenue-optimization ride-sharing

Last synced: 3 months ago
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Dynamic Pricing is an application of data science that involves adjusting the prices of a product or service based on various factors in real time. It is used by companies to optimize revenue by setting flexible prices that respond to market demand, demographics, customer behaviour and competitor prices.

• Host: GitHub
• URL: https://github.com/zahramh99/dynamic-pricing-strategy
• Owner: zahramh99
• Created: 2025-04-07T21:17:25.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2025-04-07T21:28:55.000Z (6 months ago)
• Last Synced: 2025-05-16T22:13:56.970Z (5 months ago)
• Topics: business-intelligence, data-science, data-visualization, demand-prediction, dynamic-pricing, machine-learning, predictive-modeling, price-prediction, price-prediction-model, pricing-strategy, revenue-optimization, ride-sharing
• Language: Python
• Homepage:
• Size: 33.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Dynamic Pricing Strategy using Python

![Project Banner](assets/images/banner.png)  

## Overview

Dynamic Pricing is an application of data science that involves adjusting the prices of a product or service based on various factors in real time. This project implements a dynamic pricing strategy for a ride-sharing service using Python.

## Features

- Data analysis and visualization

- Dynamic pricing algorithm implementation

- Machine learning model for price prediction

- Profitability analysis

## Usage

Run the Jupyter notebook for exploratory analysis:

jupyter notebook notebooks/dynamic_pricing_analysis.ipynb

To train the model:

python src/model_training.py

To make predictions:

python src/predict.py

## Data :

The dataset used in this project contains information about:

Number of riders

Number of drivers

Vehicle type

Expected ride duration

Historical cost of rides

## Results : 

Our dynamic pricing strategy achieved [X]% improvement in profitability compared to fixed pricing.