This project aims to predict the sales of products in Big Mart outlets based on various features and attributes. By leveraging machine learning and data analysis techniques, I seek to develop accurate models to forecast product sales, enabling Big Mart to make informed decisions regarding inventory management, pricing strategies, and store performance optimization.
Big Mart is a retail giant with multiple outlets across different cities. To enhance its operational efficiency and boost profitability, Big Mart aims to accurately predict its products' sales. This prediction task involves analyzing various variables, including product attributes, store details, and historical sales data.
The project's primary objectives include:
1. Developing robust regression models for sales prediction.
2. Conducting feature engineering to identify key variables influencing sales.
3. Evaluating model performance to ensure accuracy and reliability.
4. Providing actionable insights and recommendations to Big Mart's retail management team.
Training Data:
* Item_Identifier :
This is a unique product ID assigned to each product in the dataset.
It serves as a reference for identifying different products.
* Item_Weight:
This attribute represents the weight of the product. It provides information about the physical properties of the item.
* Item_Fat_Content:
This categorical variable indicates whether the product is labeled as "low fat" or not.
It helps in understanding the nutritional characteristics of the products.
* Item_Visibility:
This numeric attribute represents the percentage of the total display area in a store allocated to a particular product.
It can influence a product's sales as more visibility often leads to higher sales.
* Item_Type:
Item_Type is a categorical variable that specifies the category or type to which the product belongs.
It helps in grouping products by their nature, such as "Dairy," "Frozen Foods," or "Health and Hygiene."
* Item_MRP:
Item_MRP stands for Maximum Retail Price, which is the list price of the product.
It provides information about the pricing strategy for each product.
* Outlet_Identifier:
This is a unique store ID assigned to each outlet or store in the dataset.
It helps identify different stores where the products are sold.
* Outlet_Establishment_Year:
This attribute indicates the year in which each store in the dataset was established.
It provides information about the age of the store.
* Outlet_Size:
Outlet_Size is a categorical variable that describes the size of the store in terms of the ground area it covers.
It categorizes stores as "Small," "Medium," or "High."
* Outlet_Location_Type:
This categorical variable specifies the type of city or location in which each store is situated.
It helps in understanding the market and demographic characteristics of the store's location.
* Outlet_Type:
Outlet_Type is a categorical attribute that indicates the type of retail outlet.
It differentiates between different types of stores, such as "Grocery Store,", "Supermarket Type1," "Supermarket Type2," and "Supermarket Type3."
* Item_Outlet_Sales:
This is the target variable for prediction. It represents the sales of a specific product in a particular store.
The goal of the project is to predict this variable accurately.
To use the trained models for sales prediction or to explore the data analysis, please refer to the Jupyter notebooks provided in the notebooks directory.