Retail Sales Prediction

About the Customer & Challenges Faced:

Our client, a Florida-based retailer, was using qualitative techniques to forecast sales using limited available information: expert opinions and information about special events. Store sales are influenced by many more factors such as promotions, competition, school and state holidays, seasonality, locality, etc. They wanted to analyze historical data, to find patterns in the dynamics of the data like cyclical patterns, trends and growth rates. They also wanted to explore social factors influencing sales.

They needed a solution that would aid their store managers to predict the daily sales for up to six weeks in advance.

Solution and Approach:

  • The solution developed on offers predictions on daily sales for up to six weeks for each store
  • Historical data ingested was in CSV format
  • 2 Models was considered
    1. Baseline –xGBoost
    2. Challenger–Deep neural network
  • Inference service exposed as a flask based Rest API for one-time prediction
  • Prediction for 6 weeks stored in a CSV file for each store

Training Data Snapshot:

For this project, historical data For this project, historical data of each store with the following features were considered for Model Training:

  • Store -a unique Id for each store
  • Sales -the turnover for any given day
  • Customers -the number of customers on a given day
  • Open -an indicator for whether the store was open: 0 = closed, 1 = open
  • StateHoliday-indicates a state holiday. Normally all stores, with few exceptions, are closed on state holidays. All schools are closed on public holidays and weekends. a = public holiday, b = Easter holiday, c = Christmas, 0 = None
  • SchoolHoliday-indicates if the (Store, Date) was affected by the closure of public schools
  • StoreType-differentiates between 4 different store models: a, b, c, d
  • Assortment -describes an assortment level: a = basic, b = extra, c = extended
  • CompetitionDistance-distance in meters to the nearest competitor store
  • CompetitionOpenSince[Month/Year] -gives the approximate year and month of the time the nearest competitor was opened
  • Promo -indicates whether a store is running a promo on that day
  • Promo2 -Promo2 is a continuing and consecutive promotion for some stores: 0 = store is not participating, 1 = store is participating
  • Promo2Since[Year/Week] -describes the year and calendar week when the store started participating in Promo2
  • PromoInterval-describes the consecutive intervals Promo2 is started, naming the months the promotion is started anew. E.g. “Feb, May, Aug, Nov” means each round starts in February, May, August, November of any given year for that store


  • Better inventory management
  • More accurate reporting on target sales
  • Better resource planning
  • Improved customer satisfaction


  • By using, one can leverage high-end data connectivity, efficient data versioning, perform exploratory data analysis and generate inferences using an intuitive process and through an industry-standardized manner.
  • The unique, containerized platform-centric approach offered by can be used to employ required infrastructure, deploy rapidly to multiple high-availability environments while aligning with best-in-class DevSecOps practices.
  • also brings in-depth QA-QC testing and logging frameworks, synchronous and asynchronous monitoring, and performance tracking ability.
  • also has SSO (single-sign-on) for various in-built tools and subsystems that make the platform access seamless throughout.​
  • In a nutshell, all the above features in a single plate under the same hood make an unbeatable AI Ops framework.

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