End-to-End Customer Churn Prediction ML Pipeline Built a reusable end-to-end machine learning pip...End-to-End Customer Churn Prediction ML Pipeline Built a reusable end-to-end machine learning pip...
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Muhammad Haseeb
• Sep 14
End-to-End Customer Churn Prediction ML Pipeline
Built a reusable end-to-end machine learning pipeline for predicting customer churn using the Telco Customer Churn dataset and Scikit-learn's Pipeline API.
The project combines data preprocessing, feature engineering, model training, hyperparameter optimization, and evaluation into a single reproducible workflow. I implemented separate preprocessing for numerical and categorical features using StandardScaler and OneHotEncoder, then integrated the preprocessing with Logistic Regression and Random Forest models.
Key work:
Built reusable Scikit-learn pipelines using Pipeline and ColumnTransformer
Implemented numerical scaling and categorical feature encoding
Trained and compared Logistic Regression and Random Forest models
Performed hyperparameter optimization using GridSearchCV and StratifiedKFold
Evaluated models on held-out test data
Serialized the complete trained pipeline using Joblib for reuse in applications
Selected the best-performing model based on test performance
Completed work
AI Engineer Data AnalysisNumerous.aipandasMachine Learning Engineeringscikit-learn
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