Time Series Forecasting of Sales Data

In this mini project, we will focus on time series forecasting of sales data using AI and machine learning techniques. The objective is to analyze historical sales data to predict future sales trends, which can aid businesses in inventory management and strategic planning. We will begin by preprocessing the dataset, which includes handling missing values, normalizing data, and transforming it into a suitable format for analysis. Next, we will explore various forecasting models, such as ARIMA (AutoRegressive Integrated Moving Average), Seasonal Decomposition of Time Series (STL), and machine learning approaches like LSTM (Long Short-Term Memory) networks. After training the models on the historical data, we will evaluate their performance using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Finally, we will visualize the forecasts alongside actual sales to interpret the results and provide actionable insights. This project will not only demonstrate the application of AI/ML in business contexts but also enhance our understanding of time series analysis techniques.