Abstract: Business Analytics is the emerging domain of the 21st century. Machine learning algorithms control a growing range of business functions once governed by humans, including business intelligence. Most BI products go further than just enabling data aggregation and reporting. They may also provide insights or optimization suggestions using predictive analytics functions. In this paper we start with data acquisition. Any acquired/ given data can be analysed and conclusions drawn accordingly. The acquired or given data usually exists in its crude or raw state. Data pre-processing helps to format the data into useful form by removing redundancy and noise, eliminating missing and non-numerical values, and also by normalization. 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. A ML model is built by employing both logistic regression and linear regression for the automobile industry. This Business Intelligence model is a boon to the manufacturers and sales department in identifying their product in the 21st century market
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.
| DOI: 10.17148/IJARCCE.2021.10803