GitHub topics: logisticregression-classifier
KellybLieu/UC_Berkeley_Module17_ComparingClassifiers
Practical Application 3. Assignment 17.1. Goal is to compare the performance of the classifiers, K Nearest Neighbor, Logistic Regression, Decision Trees, and Support Vector Machines utilizing a dataset related to marketing bank products over the telephone.
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Marlyn-Mayienga/Titanic-Survival-Prediction
Predicting passenger survival on the Titanic using an ensemble machine learning approach, achieving a Kaggle score of 0.77990. This project leverages stacking with Random Forest, Gradient Boosting, and SVM, enhanced by feature engineering and hyperparameter tuning, to model survival patterns effectively.
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ivanseldas/microcredit-churn-classifier Fork of ironhack-labs/project-1-ironhack-payments-es
Developed a machine learning pipeline to predict customer churn with over 90% accuracy, leveraging data preprocessing, feature engineering, and Random Forest modelling. Conducted exploratory data analysis to uncover key drivers of churn, such as customer recency and cohorts from first transations.
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tejasayya/Alzheimer-s-Disease-Analysis
A research study on How do factors like alcohol consumption, age, ethnic background, and medical history affect the risk of developing Alzheimer's disease?
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