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End-to-end machine learning project for Telco Customer Churn Prediction. Includes data preprocessing, feature engineering, model evaluation, hyperparameter tuning, cross-validation, and final model deployment-ready pipeline.
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7
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Completed steps 12-14, added README with project overview, data, and modeling details
d3b3a2eView on GitHubStage 11: Train Logistic Regression & Decision Tree models, evaluate baseline results, and save trained models for next steps
48cf774View on GitHubModel selection complete: tested Logistic Regression, Decision Tree, and Naive Bayes. ChurnFlag removed due to data leakage. Logistic Regression chosen as baseline.
5a36e06View on GitHubData Cleaning (Step 5), Data Integration (Step 6), Feature Engineering (Step 7), and Final Preprocessing (Step 8) completed: handled categorical encoding, scaling, feature selection, and saved final dataset
9d91480View on GitHubComplete stages 1-4: Problem definition, business understanding, data collection, and initial EDA for Telco Customer Churn project
24094feView on GitHub