This repository contains analysis and implementation of sales forecasting for an Online Retail company. The data provided is open source and can be found here

Quick Start

Since the colab environment was used for development please click below and open the respective notebooks for quick start.

• Exploratory Analysis Notebook

• Sales Forecast Experiment

Sales Forecast Project Report

Overview

An online retail historical sales transactions were analyzed for future sales forecasting. To forecast these sales, I employed five machine learning models: Light Gradient Boosted Model (LightGBM), Auto-Regressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX), Prophet (a Generative Additive Model), and Recurrent Neural Network (RNN).

Upon testing these models, I found that the Prophet model outperformed the others in delivering a reliable sales forecast over an extended period, even with a limited dataset. The other models faced challenges in generating reasonable and noise-free predictions for such long time horizons.

In this project, I focused solely on univariate analysis. For future work, I recommend exploring multivariate and multiple time series analysis. Additionally, ensemble models could offer improved accuracy in sales forecasting.