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churn-analysis

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Unlock actionable insights and boost customer retention with this Power BI project. Analyze and visualize risk factors to proactively prevent churn. ➡️

  • Updated Mar 14, 2024

Business Case Study to predict customer churn rate based on Artificial Neural Network (ANN), with TensorFlow and Keras in Python. This is a customer churn analysis that contains training, testing, and evaluation of an ANN model. (Includes: Case Study Paper, Code)

  • Updated May 4, 2021
  • Python

This project aims to conduct an analysis of costumers behavior and perception of the brand, by implementing different marketing analytics techniques and methods: RFM (recency, frequency, monetary) model, churn classification, MBA (market basket analysis) and sentiment analysis.

  • Updated May 7, 2024
  • Jupyter Notebook

Unified ML platform serving two production risk models behind one API, fraud detection using a Logistic Regression pipeline at 92.4 percent accuracy and 0.90 F1, and churn prediction using a five-estimator hard-voting ensemble at 85.6 percent accuracy, with SMOTE balancing, sub-0.5 second inference, and CI-enforced 90 percent test coverage.

  • Updated Aug 5, 2026
  • Jupyter Notebook

Customer churn analysis project using Excel and Power BI. This project investigates the exit patterns of banking customers using various demographics and behavior indicators such as age, gender, credit card status, geography, and credit score. Insights help identify key drivers of churn and guide retention strategies.

  • Updated Jul 2, 2025

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