The document outlines the development of a machine learning pipeline for retail sales forecasting at Corporacion Favorita, a major Ecuadorian grocery retailer. It emphasizes the importance of sales forecasting for better business decisions, detailing the model development, feature engineering, and deployment process. Future improvements include enhancing model features, scaling using Spark, and creating a monitoring framework for model consistency.

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Retail Sales Forecasting: Corporacion Favorita
Performing Better Business Decisions
https://github.com/amjadraza/retail-sales-prediction

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Who am I and What am I doing?
Muhammad A. Raza (Ph.D.)
A production ready, Scalable retail sales forecasting machine learning pipeline
Corporacion Favorita data
Model to forecast
Why Business need Model
Retail Sales Forecasting
Why we need Model
How do I approach it?
Proposed Model
Future Improvements
Tools/Technologies Used
Machine Learning Approach

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Corporacion Favorita & Julian Assange
Corporacion Favorita-
TOTAL ASSETS
$1,440,2 Millions
Revenue
$2045 Millions
Net Income
$154 Millions
• large Ecuadorian-based grocery retailer
• Public Listed Company
• Founded in 1945
• A group of 20+ companies

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03. Promo optimization
• Store sales planning
• Hot promotions planning
04. Consumer behavior
• What local consumer like the most
• Personalized consumer demand
• Meet individual consumer demand
even before they ask
01. Logistics Planning
• Optimal logistic transportation
• Optimal storage capacity
• Staff rostering
02. Better Business
Forecast
• Understand future revenue
• Helps to find demand and supply
• Better revenue predictions over
short periods as well as long period
How Sales Forecasting helps in Business?

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Deploy the Model for
Production
› Deploy the Model on Cloud or in-
house for production.
› Keep tracking of real time
performance of model
Design
Cycle
Feature Engineering
› Relationship between data tables
› Extracting the features from
existing data
› Creating new features using
domain specific knowledge
Model Training
› Train the model
› Find optimal/best model
› Save the model in pickle
Preparing Data for Model
› Preparing the training, validation
and test data
› Imputing missing values if
necessary with some logic
Exploratory Data Analysis
› Data Engineering & Governess
› Distributions of data
› Can we believe the data?
An Overview of Forecasting ML Framework

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Exploratory Data Analysis
Store Data Table1
Items Data Table2
Transactions Data Table3
Holidays Data Table4
Oil Data Table5
EDA-Part 1/1 EDA-Part 2/2

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Feature Engineering
U n d erstan d in g th e Train in g D ata
Size of training data1 Identify Target variable2 Problem Identified3
P rep arin g th e D ata F o r Train in g
Training data set1 Validation set2 Test Set3
Feature Engineering notebook

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Machine Learning Model
Model Training notebook
L ig h t GB M
What I chose Light GBM1 Industry Use Cases2 Fast and Scalable3
Model Comparison
Max
Round
Num_leaves Metric
Validation
MSE
Weight MSE
Model-Winner 200 3 l2 0.392 0.6266
Model-Proposed_v1 200 3 l2 0.2206 0.4699
Model-Proposed_v2
Comments

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Future work to complete the pipeline
One of the interesting task, I would like to complete is creating new features.
Feature Engineering
01
Adding options for more models. Use linear Bayesian model to combine the predictions from individual models
ML/DL Models
02
With the increase of data, we can scale this pipeline using spark capabilities. Light GBM already has support to run with
pyspark.
Distributed Computation
03
Designing a framework to monitor the consistency of Model(s). Rolling training and validation is one option.
Consistency check of Model
04
Deploying the model for production. Set up CI/CD and add the test coverage.
Deploying with CI/CD
05

10. Python
PycharmPlotly Express
Jupyter
NotebooksUbuntu
Machine
Data Science Tools/Technologies
Sphnix Documentation
Git/GitHub