Retail Machine Learning

Overview

The Retail Machine Learning project is a fascinating venture that combines data analysis and advanced algorithms to improve various aspects of retail operations. It encompasses a range of features, including stock price prediction, grocery recommendations, and sales forecasting – all aimed at providing actionable insights for better decision-making. By building on previous projects, the team showcases innovative approaches to deeply analyze consumer behavior and market trends, particularly in the grocery sector.

This project not only addresses the complexities of predicting Walmart's stock price but also enhances the shopper's experience through personalized recommendations. The integration of machine learning algorithms offers potential improvements in inventory management and customer satisfaction, making it a noteworthy solution in the retail landscape.

Features

• Stock Price Prediction: Utilizes Facebook Prophet to analyze historical stock data and predict future stock prices based on established trends.
• Grocery Basket Recommendations: Implements clustering and classification models to suggest grocery items based on customer data, enhancing shopping convenience.
• Web Scraping Functionality: Uses automated tools to gather relevant product images and information from the internet, optimizing user engagement.
• "You May Also Like" Feature: Employs the Surprise Algorithm to recommend additional products based on a user's purchase history, driving upsell opportunities.
• Sales Forecasting: Analyzes the relationship between sales and socioeconomic factors with regression models, contributing to more accurate sales predictions.
• User-Friendly Interface: Designed to facilitate seamless navigation through grocery login, landing pages, and carts, ensuring a smooth shopping experience.
• Data Visualization: Includes graphical representations of sales, predictions, and various metrics, providing a clear view of key performance indicators for easier analysis.