Primary aim of this project is to build machine learning model that give the should able to predict the sales of the different stores of Big Mart according to the provided dataset.

[You can reach the application here] https://big-mart-pred.herokuapp.com/

Big mart sales prediction and deployment using heroku server

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.

The Data We have train (8523) and test (5681) data set, train data set has both input and output variable(s). You need to predict the sales for test data set.

Variable Description Item_Identifier Unique product ID
Item_Weight Weight of product Item_Fat_Content Whether the product is low fat or not Item_Visibility The % of total display area of all products in a store allocated to the particular product Item_Type The category to which the product belongs Item_MRP Maximum Retail Price (list price) of the product Outlet_Identifier Unique store ID Outlet_Establishment_Year The year in which store was established Outlet_Size The size of the store in terms of ground area covered Outlet_Location_Type The type of city in which the store is located Outlet_Type Whether the outlet is just a grocery store or some sort of supermarket Item_Outlet_Sales Sales of the product in the particulat store. This is the outcome variable to be predicted.

Below is the work flow if this project