UW-Machine-Learning---Classification

Machine learning projects using classification models( regularized logistic regression, CART, random forest, boosted models)

Implement classification model (logistic regression model, decision trees, RF, boosting, stochastic gradient ascent) on real-world, large-scale machine learning tasks
Handling missing data
Evaluate models using metrics (precision and recall, F1 score, ROC, AUC)

Name		Name	Last commit message	Last commit date
Latest commit History 14 Commits
1. Predicting_sentiment_from_product_reviews- Linear_classifier.ipynb		1. Predicting_sentiment_from_product_reviews- Linear_classifier.ipynb
10.Training_Logistic_Regression_via_Stochastic_Gradient_Ascent.ipynb		10.Training_Logistic_Regression_via_Stochastic_Gradient_Ascent.ipynb
2. Implementing logistic regression from scratch - Amazon Product Review Sentiment Analysis.ipynb		2. Implementing logistic regression from scratch - Amazon Product Review Sentiment Analysis.ipynb
3. Logistic Regression with L2 regularization - Amazon Product Review Sentiment Analysis.ipynb		3. Logistic Regression with L2 regularization - Amazon Product Review Sentiment Analysis.ipynb
4. Identifying safe loans with decision trees - Lending Club Default Risk Prediction.ipynb		4. Identifying safe loans with decision trees - Lending Club Default Risk Prediction.ipynb
5. Implementing binary decision trees.ipynb		5. Implementing binary decision trees.ipynb
6. Decision Trees in Practice - Lending Club Risk Prediction.ipynb		6. Decision Trees in Practice - Lending Club Risk Prediction.ipynb
7_Explore_Ensemble_Methods.ipynb		7_Explore_Ensemble_Methods.ipynb
8. implement_Boosting_module-decision_stump.ipynb		8. implement_Boosting_module-decision_stump.ipynb
9. precision-recall_for_Amazon_product_review.ipynb		9. precision-recall_for_Amazon_product_review.ipynb
README.md		README.md

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