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Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy) and Plotly Library

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Implementation of Decision Tree Classifier, Esemble Learning, Association Rule Mining and Clustering models(Kmodes & Kprototypes) for Customer attrition analysis of telecommunication company to identify the cause and conditions of the churn.

Use Multiple Linear Regression, Python, Pandas, and Matplotlib to analyze the lifetime value and the key factors of the ‘Telco Customer Churn’ dataset.

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Predict and prevent customer churn in the telecom industry with data-driven insights. This project explores customer behavior, builds predictive models, and offers recommendations to reduce attrition rates. Explore the code for analysis, model building, and more.

This interactive web application leverages machine learning to predict whether a telecom customer is likely to churn. Users can input customer details for real-time predictions or upload a CSV file for batch analysis.

Understanding why customers are leaving an online e-commerce company.

Predict the customers who are likely to churn for an European bank using various models- KNN, logistic regression, decision tree and random forest