ARCHIVES

Sales forecasting is a vital business activity because it assists businesses in planning, managing inventory as well as decision-making. Proper forecasting of sales helps companies to minimize losses, prevent overstocking or understocking and enhance customer satisfaction. The primary goal of this project is to create a machine learning-based system that is capable of forecasting future sales based on the previous data and give meaningful information to use in business planning. The data in this project is historical sales records that have details of various stores, items and their daily sales per store over time. The raw data is not usable to model train and therefore a data preprocessing step is done. In this step, the dataset is cleaned, and the date column is converted into a proper datetime format. Based on this date column, year, month and day are extracted as new features that would assist the model to learn about time based trends in sales. Such characteristics are significant as sales are usually determined by seasonal and periodic patterns. In order to enhance the performance of the model further, the feature engineering techniques are implemented. New features are developed like lag values (past sales information) and rolling mean (mean sales in a period). These attributes assist the model to learn the effect of the past sales on the future sales which is quite significant in predicting the problems. Once feature engineering has occurred, missing values that are created in the process are properly dealt with to maintain data quality. To ease the prediction process, the continuous values of sales are transformed into a categorical variable i.e. low, medium and high. This is possible due to this transformation that enables the application of machine learning algorithms based on classification to predict. In this project, four different models are implemented, including Random Forest, XGBoost, K- Nearest Neighbors (KNN), and Logistic Regression. To compare various methods and to realize which model works better in sales prediction, these models are chosen. The data is separated into the training and testing sets in such a way that the models can be trained on one half of the data and performance is analyzed on the unknown data. The various metrics used to measure the performance of each model include accuracy, confusion matrix and ROC curve. Such evaluation methods assist in the determination of the performance of the models and the extent to which they can categorize sales in various groups. The given project shows the effectiveness of the application of machine learning methods to analyze previous sales records and create a forecasting system. These systems have the potential to guide businesses towards making superior decisions, better inventory management and future strategies. Overall, this study highlights the importance of data-driven approaches in solving real-world business problems