Sales prediction using machine learning approaches

For the successful business, several factors are considered and prediction is made for the sales of the product. Here, the sales prediction is proposed to forecast the sales of Rossamann stores using machine learning algorithms. Sales forecasting is done by analyzing customer purchasing behaviour and it plays an important role in modern business intelligence. Forecasting future sales demand is key to business and business planning activities. Forecasting helps business organizations to make improvements, to make changes to business plans and to provide a stock storage solution. Forecast is determined by the use of data or information from past works and the consideration of recognized feature in future. Sales forecasting plays a vital role in strategic planning and market strategy for every company to assess past and present sales statistics and predict potential results. Overall, accurate sales forecasting helps the company to run more productively and efficiently, to save money on forecasts or predictions. In the proposed study, the linear regression and logistic regression model are analyzed and Simple Linear Regression (SLR) and Multiple Linear Regression (MLR) are trained and tested for our dataset. The data is processed to select the features and extract those features. Accurate projections make it easier for the shop to boost demand growth and a higher degree of sales generation. It produces better prediction rate.