Sales Forecasting Project using ARIMA

Project Overview

This project focuses on forecasting sales data using time series analysis and ARIMA (AutoRegressive Integrated Moving Average). The main objective is to predict future sales for a retail company based on historical sales data. The ARIMA model has been implemented to provide accurate sales predictions, which can help in business decision-making and inventory management.

Key Features

• Sales Data Processing: Clean and preprocess historical sales data.
• ARIMA Model: Implemented ARIMA model for time series forecasting.
• Visualization: Graphical representation of historical sales and predicted sales.
• Forecasting: Predicted sales for the next 12 months.

Tools and Technologies

• Python: The main programming language used for data analysis and model implementation.
• Libraries Used:
  • Pandas: For data manipulation and preprocessing.
  • Matplotlib: For data visualization.
  • ARIMA (Statsmodels): For time series forecasting.
  • Numpy: For numerical computations.

Data

The data used in this project is sourced from retail sales information, including the following columns:

• Invoice: Unique invoice identifier.
• StockCode: Product code.
• Description: Product description.
• Quantity: Quantity sold.
• InvoiceDate: Date of purchase.
• Price: Price of the product.
• Customer ID: Unique identifier for each customer.
• Country: The country of the customer.

How to Use

1. Clone the Repository:

   git clone https://github.com/yourusername/sales-forecasting.git