The purpose of this repository is to showcase a time-series forecasting model that predicts store sales of a major supermarket corporation based in South America. This Kaggle "Getting Started" competition runs indefinitely with a rolling leaderboard. My goal is to build a model that more accurately predicts the unit sales for thousands of products sold at various Favorita stores. I am also looking to gain a deeper understanding of the data and the corporation through a full statistical analysis. This will give me the opportunity to practice my machine-learning skills, enter my first competition on Kaggle, and gain real-world experience.
Data
As an individual participating in this competition, I will need to predict sales for the thousands of product families sold at Favorita stores located in Ecuador. The training dataset includes information on the dates, stores, and products sold, whether a product was being promoted, and the sales numbers. Additionally, there are supplementary files that may be useful in building my models. These are the datasets used:
1. train.csv dataset:
Column Name
Description
store_nbr
Identifies the store at which the products are sold
family
Identifies the type of product sold
sales
Provides the total sales for a product family at a particular store on a given date. Note that fractional values are possible since products can be sold in fractional units (e.g., 1.5 kg of cheese)
onpromotion
Indicates the total number of items in a product family that were being promoted at a store on a given date
2. test.csv dataset:
Column name
Description
id
Unique ID for the row
date
Date of the sales forecast
store_nbr
Identifies the store at which the products are sold
family
Identifies the type of product sold
onpromotion
Total number of items in a product family that were being promoted at a store on a given date
3. sample_submission.csv dataset:
Column name
Description
id
Unique ID for the row
sales
Predicted sales for the corresponding id in test.csv
4. stores.csv dataset:
Column name
Description
store_nbr
Identifies the store
city
City where the store is located
state
State where the store is located
type
Type of store (A, B, or C)
cluster
Grouping of similar stores
5. oil.csv dataset:
Column name
Description
date
Date of the oil price
dcoilwtico
Daily oil prices (West Texas Intermediate)
6. holidays_events.csv dataset:
Column name
Description
date
Date of the holiday/event
type
Type of holiday/event (Holiday, Additional, Bridge, or Transfer)
locale
Locale of the holiday/event (National, Regional, or Local)
locale_name
Name of the holiday/event locale
description
Description of the holiday/event
transferred
Indicates whether a holiday was officially transferred to another date by the government
Citation Alexis Cook, DanB, inversion, Ryan Holbrook. (2021). Store Sales - Time Series Forecasting. Kaggle.
source: https://marcasecuador.club/corporacion-favorita/
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent