Machine Learning

This repo consists of

Various algorithms of Machine Learning applied on Amazon Fine Food Reviews. Our task is to classify the reviews positive or negative. This is text data and we use different pre-processing techniques such as BOW, TF-IDF and Avg Word2Vec, Tfidf-Word2Vec.

StackOverflow Tag predictions using OneVsRest classifier with Logistic Regression and Linear SVM.

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Name		Name	Last commit message	Last commit date
Latest commit History 105 Commits
.idea		.idea
.ipynb_checkpoints		.ipynb_checkpoints
Amazon_LSTM		Amazon_LSTM
Apply Decision Trees on Amazon reviews data set		Apply Decision Trees on Amazon reviews data set
Apply K-means, Agglomerative, DBSCAN clustering algorithms on Amazon reviews data set		Apply K-means, Agglomerative, DBSCAN clustering algorithms on Amazon reviews data set
Apply Naive Bayes on Amazon reviews		Apply Naive Bayes on Amazon reviews
Apply Random Forests & GBDT on Amazon reviews data set		Apply Random Forests & GBDT on Amazon reviews data set
Apply SVM on Amazon reviews data set		Apply SVM on Amazon reviews data set
Apply Truncated SVD on Amazon reviews data set		Apply Truncated SVD on Amazon reviews data set
Apply k-NN on Amazon reviews data-set		Apply k-NN on Amazon reviews data-set
Apply t-SNE on Amazon reviews with polarity based color-coding		Apply t-SNE on Amazon reviews with polarity based color-coding
CNN_MNIST		CNN_MNIST
Data Visualization with Haberman Dataset		Data Visualization with Haberman Dataset
Different architectures on MNIST dataset		Different architectures on MNIST dataset
Implement SGD for linear regression		Implement SGD for linear regression
Logistic Regression on Amazon reviews data set		Logistic Regression on Amazon reviews data set
.gitignore		.gitignore
EDA.ipynb		EDA.ipynb
README.md		README.md