nish700 / regularization-code-along

Big mart sales regularization

Project Overview

The project entails the implemtation of following concepts to predict the output sales value:

• Linear Regression

• Lasso Regularisatiion

• Ridge Regularisation

• heteroscedasticity

• Cross Validation

• Feature Selection

About the DataSet :

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.

Features Description

**Item_Identifier **Unique product ID

**Item_Weight **Weight of product

**ItemFatContent **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

**OutletEstablishmentYear **The year in which store was established

**Outlet_Size **The size of the store in terms of ground area covered

**OutletLocationType **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

**ItemOutletSales **Sales of the product in the particulat store. This is the outcome variable to be predicted.

**source **is it a train data or test data point