Name: Grocery Sales Prediction: A Step-by-Step ML Guide
Creator: Kaggle
License: https://opendatacommons.org/licenses/pddl/1.0/

About this Dataset

Grocery Sales Prediction: A Step-by-Step ML Guide

>Dataset Overview: Grocery Store Sales Prediction

This dataset contains historical sales data from a large grocery store located in Islamabad, Pakistan. With an average daily footfall of around 1,500 customers, the store serves a broad consumer base, making it ideal for analyzing and predicting sales trends.

In this project, we focus specifically on predicting the sale of rice by leveraging historical data from January 22, 2024, to October 14, 2024. Using this dataset, we trained a Random Forest Regressor model to forecast rice sales based on past patterns.

Columns Details

The dataset includes the following columns:

Date: The date on which the sale occurred.
Store: A unique store code to identify the location.
Item: The code representing the specific item (rice) sold.
Sale: The quantity of rice sold on each date.

The goal of this project is to predict future sales of rice at this store using historical data. By accurately forecasting sales, the store can optimize inventory and improve stock management for this essential product.

Tables

Train

@kaggle.alnafi_sore_sales_data_set.train

15 kB
1,009 rows
4 columns

CREATE TABLE train (
  "date" TIMESTAMP,
  "store" BIGINT,
  "item" BIGINT,
  "sales" BIGINT
);