Retail Price Optimization

Overview

Retail price optimization involves determining the optimal selling price for products or services to maximize revenue and profit. The ultimate aim is to charge a price that helps you make the most money while attracting enough customers to buy your products. This project utilizes data and pricing strategies to find the right price that maximizes sales and profits while keeping customers satisfied.

Project Structure

• retail_price_optimization.ipynb: The main Jupyter notebook containing the analysis and machine learning model for retail price optimization.
• Data/: Directory containing the dataset used for analysis.
  • retail_price.csv: The CSV file containing retail price data.

Key Features

• Data analysis and visualization using libraries such as Pandas and Plotly.
• Exploration of the distribution of prices and relationships between different variables.
• Calculation of average competitor price differences by product category.
• Implementation of a machine learning model (Decision Tree Regressor) to predict optimal retail prices.

Getting Started

1. Clone the repository or download the project files.
2. Ensure you have the required libraries installed. You can install them using:

   pip install pandas plotly scikit-learn

3. Open the retail_price_optimization.ipynb file in Jupyter Notebook or any compatible environment.
4. Run the notebook cells to perform the analysis and see the results.

Conclusion

This project demonstrates how to optimize retail prices using data analysis and machine learning techniques. By analyzing pricing strategies and competitor data, businesses can make informed decisions to enhance their pricing strategies and improve profitability.