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This comprehensive dataset is a goldmine for data scientists, analysts, and researchers interested in exploring a wide range of topics within the realm of online retail. It encompasses a rich collection of customer behavior and characteristics, making it a versatile resource for tackling multiple aspects of data analysis and prediction.
These dashboards provide insights across diverse domains, including cryptocurrency sales, workforce challenges, disease impact analysis, and retail trends. Leveraging tools like Power BI and Excel, they offer actionable insights for decision-making.
This project employs NLTK, Prowebscraper, and Python for sentiment analysis on online product reviews. Through web scraping, EDA, and NLP, it evaluates user satisfaction by comparing actual ratings and sentiment scores
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.
The ICDE-BuyAdvisor website is a user-friendly solution to help the buyers decide whether to buy products by evaluating the product reviews for them using web scraping and machine learning techniques. Once the evaluation is completed, the product analysis is shared with the buyer.
This is a comprehensive report that explores the performance of product sales and profit accross various KPIS using Power BI. This report uncovers insights and proffer reccomendations.