The aim is to build a predictive model and find out the sales of each product at a particular store. Create a model by which Big Mart can analyse and predict the outlet production sales.
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.
- 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.
- Loading Packages and Data
- Data Structure and Content
- Exploratory Data Analysis
- Missing Value Treatment
- Feature Engineering
- Encoding Categorical Variables
- Label Encoding
- PreProcessing Data
- Modeling
- Linear Regression
- RandomForest Regressor
- XGBoost
- Deployment