Prediction Analysis Sales for Corporate Services Telecommunications Company using Gradient Boost Algorithm

Abstract

Sales prediction analysis requires smart data mining techniques with accurate prediction models and high reliability. Essentially, most market segments rely on the knowhow base and the demand trend forecast for analysis of Business To Business (B2B) sales data. Data are provided by sales on how Telecommunication Company should manage its sales team, its products and also its budgeting flows. Precise estimates make it possible for Telecommunication Company to survive the market war and increase its market growth. In this research, the study and analysis of comprehensible predictive models use machine learning techniques to improve future sales predictions. Traditional forecasting systems are difficult to deal with big data and the accuracy of sales forecasting. In this paper, a brief analysis of the reliability of B2B sales using machine learning techniques. The latter part of this research explains a range of sales prediction strategies and interventions. Based on the performance assessment, a best-adapted predictive model for the B2B sales trend forecast is suggested. Projection, estimation and analysis findings are summarized in terms of reliability and consistency of efficient prediction and forecasting techniques. The results of this analysis are expected to generate reliable, accurate and effective forecasting data, a valuable resource for sales predictions. Research has shown that Gradient Boost Algorithm shows good accuracy in forecasting and future B2B sales prediction with MSE = 24,743,000,000.00, and MAPE: 0.18.

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