📊 Dive into Amazon Grocery Data: Explore the dataset's features and structure. 🛠️ Data Preprocessing: Learn how to prepare data for analysis. 📈 Predictive Modeling: Build and fine-tune ML models for sales and revenue prediction.

AMAZON Grocery Review Analysis using BERT model

Apache Spark (Pyspark) was used to perform ETL process to extract amazon US grocery review dataset, transform the data, connect to an AWS RDS instance, and load the transformed data into PostgreSQL database, then perform an analysis to determine if there is any bias towards reviews that were written as part of the Amazon vine program

Dive into the world of grocery sales with this dynamic Power BI dashboard! Featuring meticulously cleaned and preprocessed data using Python, it showcases vital metrics like sales, revenue, and pricing trends for Amazon UK Grocery products.

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A sentiment analysis project using Amazon grocery review data. Explores consumer opinions on Asian grocery products through natural language processing, frequency analysis, and sentiment classification with machine learning models.

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The Amazon Grocery Sales Dashboard visualizes key metrics like sales, ratings, and orders across outlets and item types. It enables quick, interactive analysis to support data-driven decisions.

Python-based analysis of amazon grocery reviews and ratings

Text Mining project. Data Science degree.

Sentiment Analysis and Topic Modelling approach to get insights on Grocery Dataset by Amazon

Amazon grocery shoppers are able to submit feedback on products which allow business improvement through sentiment analysis to identify common issues and themes.

I have built sentiment predictive models for Amazon grocery and home and kitchen products reviews, utilizing sentiment analysis and machine learning. These models provide accurate sentiment analysis, aiding businesses in understanding customer feedback and making informed decisions.

Neural network for sentiment analysis on Amazon grocery reviews. Compared learned embeddings with pretrained Word2Vec to assess performance. Includes preprocessing, training, evaluation (accuracy, precision, recall, F1, confusion matrix), and reproducible Jupyter notebook.

🔍 A simple Java + Selenium-based automation script to search grocery items like Megi, Oil, Rice, etc., on Amazon.in, extract product images, and save them locally. 🗂️ Each keyword gets its own folder inside C:\Users\U-23\Downloads\ImageDown, and 3 images per keyword are downloaded for offline reference, analysis, or dataset creation.