Context

Supplement Sales Prediction

• Your Client WOMart is a leading nutrition and supplement retail chain that offers a comprehensive range of products for all your wellness and fitness needs.

• WOMart follows a multi-channel distribution strategy with 350+ retail stores spread across 100+ cities.

• Effective forecasting for store sales gives essential insight into upcoming cash flow, meaning WOMart can more accurately plan the cashflow at the store level.

• Sales data for 18 months from 365 stores of WOMart is available along with information on Store Type, Location Type for each store, Region Code for every store, Discount provided by the store on every day, Number of Orders everyday etc.

• Your task is to predict the store sales for each store in the test set for the next two months.

Content

Train Data |Variable |Definition | |-------------------------------|-------------------------------| |ID |Unique Identifier for a row | |Store_id |Unique id for each Store| |Store_Type |Type of the Store| |Location_Type |Type of the location where Store is located| |Region_Code |Code of the Region where Store is located| |Date |Information about the Date| |Holiday |If there is holiday on the given Date, 1 : Yes, 0 : No| |Discount |If discount is offered by store on the given Date, Yes/ No| |#Orders |Number of Orders received by the Store on the given Day| |Sales |Total Sale for the Store on the given Day|

Test Data |Variable |Definition | |-----------------------------|-------------------------| |ID |Unique Identifier for a row | |Store_id |Unique id for each Store | |Store_Type |Type of the Store | |Location_Type |Type of the location where Store is located | |Region_Code |Code of the Region where Store is located | |Date |Information about the Date | |Holiday |If there is holiday on the given Date, 1 : Yes, 0 : No | |Discount |If discount is offered by store on the given Date, Yes/ No |

Sample_Submission |Variable |Definition | |------------------------|----------------| |ID |Unique Identifier for a row | |Sales |Total Sale for the Store on the given Day |

Evaluation

• The evaluation metric for this competition is MSLE * 1000 across all entries in the test set.

Public and Private Split

• Test data is further divided into Public (First 20 Days) and Private (Last 41 Days). You will make the prediction for two months (61 days).
• Your initial responses will be checked and scored on the Public data.
• The final rankings would be based on your private score which will be published once the competition is over.

The sales column that we submit would be compared to the actual answer similar to the following. Instead of 8 items it is 22266 items(the function is avable in sklearn).

Sample Input :

actual = [27.5, 55.9, 25.8, 17.7, 27.6, 55.9, 25.7, 17.8] predicted = 24.0, 49.1, 21.0, 16.2, 23.3, 47.0, 12.1, 15.2*1000

Sample Output:

82.9949678377161

Public and Private Split

• Test data is further divided into Public (First 20 Days) and Private (Last 41 Days). You will make the prediction for two months (61 days).

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

About Data

The sales in the mega marts are critical to make them sustainable. As a matter of fact, the rise of several marts has created buyers of different categories who are critical about the quality of product at the right price.

Here, the data science & machine learning community has been challenged to build an ML model and predict the sales of each product from each outlet. The participants also need to use the model to analyse the properties of the product in the stores and find ways to increase sales.

Data Attributes

Item_ID: Item Identification Number Item_W: Item Weight Item_Type: Item Item_MRP: MRP of the Product Outlet_ID: Outlet ID Outlet_Year: Outlet Establishment year Outlet_Size: Size of the outlet Outlet_Type: Type of the outlet Sales: Total sales from the outlet

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Dataset

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Contents

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