Retail Machine Learning

Team Members

• Rebecca Pham
• May Lacdao
• Elizabeth Salas Martinez
• Hanieh Babaee
• Ronald Clarke

Deploy: https://retail-ml.herokuapp.com/

Objective

For this project, we decided to build on our two previous projects (ETL Project and Project 2). We will be incorporating machine learning algorithms to:

1. Predict Walmart's stock price
2. Create a grocery recommendation system
3. Forecast sales

Sections

Stock Price Prediction

Data: https://www.kaggle.com/aayushkandpal/walmart-inc-stock-data-19722020-latest

Facebook Prophet

• run stock price vs date
• run stock volume vs date
• run (stock price vs volume) vs date
• graph stock data, prediction (line, area graph)

Grocery Basket Customer Recommendations

Data: https://www.kaggle.com/psparks/instacart-market-basket-analysis

A. Grocery List Recommendation:

• Load csv file, build a clustering/classification model
• Webscrape/API google images, use chrome driver searching the word/food item and just taking the copy link address of the first search results, adding the csv file
• HTML, search a member number and the product suggestion will load

B. Other Product Recommendations:

• Additional Feature: “You May Also Like..."
• Using Surprise Algorithm provide 3 products a user may also like to purhcase base on their order history

Sales Forecasting

Data: https://www.kaggle.com/naresh31/walmart-recruiting-store-sales-forecasting

A. Sales vs. SocioEconomic Target

• Regression on Sales vs SocioEconomic Variables (No Date)
• Regression to see if there’s relationship between sales and socioeconomic variables
• Graph against the variables to see if there’s any relationship

B. Facebook Prophet Sales vs Date

• If there’s no relationship above, you can use facebook prophet
• Graph a line/area graph with the sales and predictions