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Surendra Vagvala's Projects

credit-card-fraud-detection icon credit-card-fraud-detection

Isolation Forest anomaly detection algorithm on a highly skewed dataset with only 0.17% fraudulent transactions to detect credit card fraud.

document-classification icon document-classification

Authorship identification on lines of poetry written by Emily Bronte and William Shakespeare, using the Nave Bayes classier provided. Used ngrams, POS tagging, Lemmatization techniques for accuracy.

hand-written-digit-recognizer icon hand-written-digit-recognizer

Recognize digits from MNIST dataset of tens of thousands of handwritten digit images through ML algorithms - Multinomial logistic regression, Convolutional Neural Networks and MLP.

movie-recommender icon movie-recommender

A movie recommender system based on Item-Item collaborative filtering using MovieLens dataset. Item-based techniques first analyze the user-item matrix to identify relationships between different items, and then use these relationships to compute recommendations for users.

ner-tagger icon ner-tagger

A supervised named entity recognizer for twitter dataset. The training dataset are tweets annotated with name entities in BIO (Beginning of an entity, Inside an entity or Outside of entities) format. (10 entity types, 21 possible classes)

search-engine icon search-engine

Search engine based on page rank algorithm using Apache Solr, on crawled data of 50,000 news webpages. Improvised with auto suggest, spell correction and snippet features for better user experience.

stock-price-prediction-with-sentiment-analysis icon stock-price-prediction-with-sentiment-analysis

Multi-Layer Perceptron model to predict a company’s stock price. The program performs sentiment analysis on company’s news headlines from NY times articles, combines these results with historical price data from the Dow Jones to predict future prices.

text-summarizer- icon text-summarizer-

Deep learning model to generate headlines for news articles. A sequence-to-sequence RNN encoder-decoder using Keras to perform abstraction summarization and generate a headline keeping original intent.

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