Abstract: The new discipline of the twenty-first century is business analytics. A rising number of company operations, including business intelligence, are now managed by machine learning algorithms. The majority of BI systems offer more functionality than just data gathering and reporting. Using the capabilities of predictive analytics, they could potentially offer insights or optimization ideas. In this paper, data collecting comes first. Any gathered or provided data can be examined, and conclusions can be made as necessary. The gathered or provided data is typically in its unprocessed or raw form. Pre-processing data helps to format the data into a usable form by removing noise and redundancy, as well as missing values and non-numerical values. Data analysis and visualization are carried out to improve the statistical analysis of given data. Logistic regression is carried out on the data since it contains lot of columns with categorical values. Accuracy, precision, and f1 score of the model have been measured. Various conclusions can be drawn from this interdependent data set and can be stored as historical data for future analysis. Linear Regression is also carried out on the data set and r-squared values noted. R-squared is a statistical measure of how close the data are to the fitted regression line. For the automotive business, an ML model is created using both logistic regression and linear regression. The manufacturers and sales department can identify their product in the market of the twenty-first century thanks to the help of this business intelligence model.

Keywords: Business Analytics (BA)/ BI (Business Intelligence), Machine Learning, Data pre-processing, Logistic regression, accuracy, precision, and f1 score, linear regression, data analysis and visualization, R-squared, Business Intelligence.

PDF | DOI: 10.17148/IJARCCE.2023.12135

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