This repository contains data analytics projects that are important to our day to day Machine learning activities
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Updated
Mar 2, 2023 - Jupyter Notebook
This repository contains data analytics projects that are important to our day to day Machine learning activities
AmazonBuddy: Your Discord companion for instant product info extraction! Effortlessly retrieve ASIN/ISBN from links and access detailed reviews. Streamline your product research now!
It is a machine learning project compatible to provide a tentative price of laptop according to the user configurations. It needs lots of feature engineering, and pre- processing and at last Random Forest gave the best accuracy of 0.8837.
A basic NLP project on musical instruments reviews on Amazon.
Un progetto di confronto tra HADOOP, SPARK e HIVE su query simili per analisi distribuite su un dataset in formato CSV relativo a recensioni di prodotti gastronomici Amazon
This repository includes a web application that is connected to a product recommendation system developed with the comprehensive Amazon Review Data (2018) dataset, consisting of nearly 233.1 million records and occupying approximately 128 gigabytes (GB) of data storage, using MongoDB, PySpark, and Apache Kafka.
This repo contains multiple amazon review sentiment analysis models using different techniques, along with docker images and kubernetes templates to help you productionalize it.
This notebook will show you how to implement a deep leaning algorithm (LSTM) on the Amazon Alexa Reviews dataset
This ML model trains from data collected from Amazon product reviews and predicts whether the review is positive [1] or negative [-1].
Sentiment Analysis on Amazon Review using Deep Learning
Sentiment Analysis on Amazon Fine Food Reviews
Sentiment analyis of Amazon product reviews using SVM 'rbf':kernel classifier in which word vectorization is done using TF_IDF and CountVectorizer.
The objective of this project is to discover insights into consumer reviews.
Aspect-based sentiment analysis on Amazon reviews
Amazon Reviews Sentiment Classifications and Textual Similarities with Universal Sentence Encoder.
Design to build an ETL process for Amazon Review datasets. Two datasets are chosen: Amazon Japan and Amazon Kitchenware. Analyze each dataset and determine if Amazon's vine program is trustworthy or not.
Uncover what customers love & dislike with sentiment analysis & topic modeling. Benchmark products & gain actionable insights to improve customer experience! #ecommerce #datascience
This project focuses on sentiment analysis of Amazon product reviews using machine learning and natural language processing techniques. 💬🔍📈
Sentiment Analysis on Whirl Pool Washing Machine Based on its reviews from Amazon Website
This repository showcases the outcomes of an Exploratory Data Analysis (EDA), including visualisation, conducted on the comprehensive Amazon Review Data (2018) dataset, consisting of nearly 233.1 million records and occupying approximately 128 gigabytes (GB) of data storage, using MongoDB and PySpark.
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