The study examined an analysis of customer behaviour for an e-commerce website. The main analytic methodologies employed are descriptive statistics, multiple linear regression and classification regression tree model. The analysis debunked a clearer picture of customer profiles, identified business and high-revenue customer clusters, which can help the company set up next more effective marketing strategies.

The study is the second part of the Diabetes Prediction Machine Learning Model research. A new model, Random Forest Model, is used here for the same datasets to compare the prediction accuracy and performance with the previous Logistic Regression Model. The result is, as expected, intriguing and insightful.

All patients included in this dataset are Pima Indians (a subgroup of Native Americans) and are females aged 21 years and older. The objective is to leverage a robust machine learning logistic regression model to predict whether a patient has diabetes based on diagnostic measurements. We would like to how accurate we can predict whether the subject has diabetes utilizing the given diagnostic measurement variables.

A restaurant chain, seeking to understand which aspects of their offering hold the greatest importance to their customers. This research aimed to identify the key metrics that drive customer satisfaction and assess the restaurant's performance in these areas by implementing a so-called Importance-Performance Matrix based on multiple linear regression.