Customer segmentation allows us to learn much more about the customers. By understanding the characteristics of a particular segment, we can tailor to each group's unique needs and challenges and be able to create targeted campaigns and advertisements that resonate with and convert certain segments of customers.
Utilizing unsupervised learning, this project identifies distinct customer segments in the retail market, enabling tailored strategies that resonate with specific groups. This approach enhances revenue by understanding target audiences and refining marketing strategies
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Temperature is a crucial climate metric that significantly impacts human life and ecosystems. Rising temperatures contribute to more severe heat waves, posing health risks, especially to vulnerable populations. Temperature fluctuations influence the survival of various species, and rapid changes can disrupt natural processes, challenging species adaptation.
The project aims to understand the temperature change trends for 196 countries across the globe from 1961 to 2019. Furthermore, various predictive models were utilized with state-of-the-art methods for feature reduction and variable importance analysis to accurately forecast future trends in temperature change.
Cardiovascular Disease is claimed to be the leading cause of death globally, out of which 85% (15.2 M ) recorded death is due to heart attack. According to the Centers for Disease Control and Prevention (CDC), approximately every 40 seconds, an individual in the United States is reported to experience a heart attack.
My project focused on delving into existing works in the field of machine learning models for heart failure, identifying relevant biomarkers leveraging exploratory data analysis and machine learning methods, and deploying predictive models to forecast the risk of heart attack.
In the U.S. telecom industry, including major carriers like AT&T, Verizon, T-Mobile, and Sprint, 1.9% of their 400 million subscribers switch carriers. Despite spending $10.39 billion to acquire new customers, they lose about $780 million annually due to customer churn. In essence, retaining customers costs only about 1/13th of acquiring new ones.
This project aimed to determine the significance of variables, integrate these predictors to improve customer relationship management and to make prediction of potential churner utilzing data mining techniques.