Trainable Neural Networks Modelling for a Forecasting of Start-Up Product Development

Abstract

In this article, components of the model of forecasting of IT products development of innovative start-up projects are considered based on the analysis of formed datasets of the interactions of prospective clients. We offered the algorithm of formation of initial datasets based on Customer Journey Map (CJM), which are the tool of fixing of events of the interaction of clients with the system. We propose to use the trainable neural network as a mechanism for processing big data sets and building IT product development strategies. We used a simple linear regression analysis to model the relationship between a single explanatory variable and a continuous response variable (dependent variable). An exploratory data analysis method was applied to the available data to find repetitive patterns and anomalies. In the course of the research, we constructed a model of linear regression implementation using the gradient optimisation approach. The linear models of the scikit-learn library for the regression task were also applied, and the stabilisation regression method was implemented. We have modelled and analysed the obtained results.