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In this assignment I will put my ETL skills to the test. Many of Amazon's shoppers depend on product reviews to make a purchase. Amazon makes these datasets publicly available. However, they are quite large and can exceed the capacity of local machines to handle. One dataset alone contains over 1.5 million rows; with over 40 datasets, this can b…
Sentiment analysis using different types of Bidirectional Recurrent Neural Networks on Amazon reviews dataset. The results are confronted with two baseline models which are an SVM and a RF model.
This project demonstrates how to perform sentiment analysis using deep learning on Amazon product reviews dataset. The dataset used for the project is obtained from Kaggle and consists of nearly 3000 reviews of amazon users regarding various amazon Alexa products like Alexa echo, Alexa dot etc. Exploratory data analysis is performed on the datas…
Text Classification Problem : Wrote a module to classify Amazon-Product Reviews as favourable/unfavourable. Achieved accuracy of 78% and an F1 score of .81 using Logistic Regression on a test-train split of 20%, where total records were around 50000.
Built a recommender system using Apache Mahout machine learning library carried out data analysis using Hadoop, Apache Hive & Pig on Amazon Customer Reviews Data set(130M+ reviews))
This project crawls Amazon reviews and extracts features and opinions to calculate a feature based rating of every product (mainly smartphones) Done with python, pyqt5