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. So, We have build a solution which is able to predict the sales of the different stores of Big Mart according to the provided dataset.
Dataset Link: - https://www.kaggle.com/brijbhushannanda1979/bigmart-sales-data
We have train (8523) and test (5681) data set, train data set has both input and output variable(s). We need to predict the sales for test data set.
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.
Clone the project
git clone https://github.com/scorpion-varun/Store-Sales-Prediction-Project
Go to the project directory
cd StoreSalesPrediction
Install dependencies
pip install -r requirements.txt
Start the server
python manage.py runserver
Client: HTML, CSS, JavaScript
Server: Python
Framework: Django
Hosting Platform: Heroku