💡[Feature]: Retail Price Optimization Model #1309
Description
Is there an existing issue for this?
- I have searched the existing issues
Feature Description
Hello,
I would like to propose the addition of a Retail Price Optimization Machine Learning Model to this repository. This model leverages historical sales data, product attributes, and unstructured data (like text and images) to dynamically optimize retail pricing in real-time. It uses a machine learning algorithm (such as Random Forest or XGBoost) to process numerous pricing scenarios and determine the optimal price to maximize sales and profits.
Please assign this to me under gssoc , hacktoberfest . I hope you will assign a appropriate level to me . please do consider sir
Thank you
Use Case
Use Case:
A retailer wants to maximize profits and remain competitive by optimizing product prices in real-time. Using machine learning, the retailer analyzes historical sales data, product features, and market trends to dynamically adjust pricing, ensuring increased sales, improved profit margins, and better customer satisfaction.
Benefits
The importance of this model lies in its potential to help businesses, especially retailers, adapt to the highly competitive and price-sensitive environment. Dynamic pricing is key in industries like e-commerce, where giants like Amazon have already seen significant returns by integrating machine learning for pricing optimization. This model could be an excellent addition, aiding companies in boosting ROI and efficiency.
Add ScreenShots
Here’s a short step-by-step process for completing this :
Data Collection: Gather product, pricing, and sales data (CSV format or from public datasets).
Data Preprocessing: Clean and preprocess data (handle missing values, encode categorical variables).
Feature Engineering: Extract relevant features (product attributes, historical sales, etc.).
Model Selection: Choose a machine learning model (Random Forest, XGBoost, etc.).
Model Training: Split data into training/testing sets and train the model.
Model Evaluation: Evaluate model performance using metrics like RMSE.
Fine-tuning: Optimize the model using hyperparameter tuning (e.g., Grid Search).
Real-Time Predictions: Simulate real-time price predictions with new data.
Save and Deploy: Save the model and prepare it for deployment in a live system.
I will use google collab
Priority
High
Record
- I have read the Contributing Guidelines
- I'm a GSSOC'24 contributor
- I want to work on this issue