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Built classifiers using logistic regression and decision trees to classify product reviews and used machine learning techniques such as boosting, precision and recall, and stochastic gradient descent for optimization in Python

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Name		Name	Last commit message	Last commit date
Latest commit History 14 Commits
amazon_baby.gl		amazon_baby.gl
amazon_baby_subset.gl		amazon_baby_subset.gl
lending-club-data.gl		lending-club-data.gl
README.md		README.md
boosted-decision-stump-ensemble.ipynb		boosted-decision-stump-ensemble.ipynb
decision-tree-overfitting.ipynb		decision-tree-overfitting.ipynb
gradient-boosted-tree-classifier.ipynb		gradient-boosted-tree-classifier.ipynb
implementing-binary-decision-trees.ipynb		implementing-binary-decision-trees.ipynb
important_words.json		important_words.json
linear-classifier-feature-engineering.ipynb		linear-classifier-feature-engineering.ipynb
linear-classifier-learning.ipynb		linear-classifier-learning.ipynb
linear-classifier-regularization.ipynb		linear-classifier-regularization.ipynb
precision-recall.ipynb		precision-recall.ipynb
safe-loan-decision-tree-classifier.ipynb		safe-loan-decision-tree-classifier.ipynb
stochastic-gradient-ascent.ipynb		stochastic-gradient-ascent.ipynb