BIG_MART_SALES_PREDICTION
Problem Statement:-
The data scientists at BigMart have collected 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.
Dataset description.
The dataset has 8524 entries with 13 columns.The description of each columns is given below:
Item_Identifier - Unique product id.
Item _Weight - Weight of product.
Item_Fat_Content.
Item_Visibility.
Item_Type.
Item_MRP.
Outlet_identifier.
Outlet_Establishment_Year.
Outlet_size.
Outlet_Type.
Outlet_Location_type.
Item_outlet sales.
I have followed simplest techniques to solve the problem,Steps involved in solving were as follows:
Data Exploration – looking at categorical and continuous feature summaries and making inferences about the data.
Data Cleaning – imputing missing values in the data and checking for outliers.
Feature Engineering – modifying existing variables and creating new ones for analysis.
Model Building – making predictive models on the data.
Saving the result into a new file.