OBJECTIVE :
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://bigmartsalesprediction.herokuapp.com/
Project video :
PROBLEM STATEMENT :
Nowadays, shopping malls and Big Marts keep track of individual item sales data in order to forecast future client demand and adjust inventory management. In a data warehouse, these data stores hold a significant amount of consumer information and particular item details. By mining the data store from the data warehouse, more anomalies and common patterns can be discovered.
DATASET DESCRIPTION :
BigMart has collected sales data for 1559 products across 10 stores in different cities. Where the dataset consists of 12 attributes like Item Fat, Item Type, Item MRP, Outlet Type, Item Visibility, Item Weight, Outlet Identifier, Outlet Size, Outlet Establishment Year, Outlet Location Type, Item Identifier and Item Outlet Sales. Out of these attributes target variable is the Item Outlet Sales attribute and remaining attributes are refer as independent variables. The data-set is also based on hypotheses of store level and product level. Where store level involves attributes like: city, population density, store capacity, location, etc and the product level hypotheses involves attributes like: brand, advertisement, promotional offer, etc.
STEPS INVOLVED IN MODEL BUILDING :
(i)Data Loading
(ii) Treating Missing values
(iii)Data transformation
(iv)New feature Generation
(v)Feature Engineering
(vi)Model Building
(vii)Evaluating Model
(viii)UI setup
(ix)Push to Github
(x)Deployment