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Build a Predictive Model for the Retail Industry using Machine Learning, Regression and Neural Nets

The aim of this project is to build a predictive model and find out the sales of each product at a particular store of Big Mart.
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What will you learn

  • Understanding of Retail Industry
  • How Regression Model helps in predicting sales
  • How to get Important factors that that drive the items Sales from regression
  • How to visualize the sales data
  • Basics steps involved in exploring the data (EDA)
  • Application of Machine Learning Techniques in sales Prediction in Python
  • Linear Regression VS Randomforest
  • GBM
  • Neural Network
  • Model Cross validation
  • Power ensemble of all models VS single model
  • Feature engineering for better accuracy

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Project Description

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.

Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.

Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

Instructors

 
Jeeban

Senior Statistical Analyst

"I enjoy 3 things in Analytics - Machine Learning, Image/Video Processing, Natural Language Processing."