Sales Prediction using Linear Regression

Project Overview

This project utilizes Linear Regression, a fundamental machine learning algorithm, to predict the number of items sold in various stores. We use a comprehensive training dataset that includes 900,000 data points, each detailing the date, item, store, and the quantity of items sold. The goal is to provide accurate sales forecasts that can help in inventory management and marketing strategies.

Dataset

The training dataset comprises the following fields:

• Date: The date on which the sales data was recorded.
• Item: The identifier for the type of item sold.
• Store: The identifier for the store where the items were sold.
• Items Sold: The quantity of items sold.

Prerequisites

Before running the model, ensure you have the following software/packages installed:

• Python 3.8 or newer
• NumPy
• pandas
• scikit-learn

You can install the required packages using the following command:

pip install numpy pandas scikit-learn