چكيده به لاتين
Regression is a machine learning technique to predict numerical values based on input data. Non-linear regression is a regression variant in which the relationship between the input and output is not a straight line but a curve or some other complex function. Non-linear regression is instrumental when the input and output relationship is not well-defined or challenging to capture with a linear model. Examples include predicting sales in a company based on historical data, revenue, number of employees, and other factors. Engineering dashboards can be used to display the results of non-linear regression in an intuitive and easy-to-understand way. Using gauges, charts, and other visualizations, engineers can quickly assess the accuracy and reliability of the regression model, identify trends and anomalies, and make data-driven decisions. Overall, non-linear regression and engineering dashboards can be powerful tools for engineers making data-driven decisions and optimizing their processes. Engineers can gain valuable insights and improve their performance in various industries and applications by leveraging the latest advances in machine learning and data visualization technology. In this research, we developed a machine learning regression model to predict some parameters of our business model based on other parameters; then, the results are visualized via an engineering dashboard. We develop our dashboard using Python/Django, Django-Rest-framework, JavaScript libraries, and Application Programming Interfaces. In other words, we will follow an A-Z approach to generate a dataset, train a model, develop an API set and web application, and implement a two-way predicting approach for our Business Intelligence (BI) model.
