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In this project I did a thorough analysis of the Email Campaign dataset where my primary goal is to develop a machine learning model to identify and monitor the mail that is read, acknowledged, and ignored.
This study involves employing machine learning models and anomaly detection approaches, such as over- and under-sampling, to detect fraud in online transactions.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.
In this project my opportunity to dive deep into the world of data analysis and gain practical experience with the tools and techniques that I have learned overall been learning.