A Natural Language Processing (NLP) project for analyzing customer reviews from e-commerce platforms like Amazon, Flipkart, eBay, Walmart, and BestBuy. Utilizes VADER sentiment analysis to classify reviews as positive, negative, or neutral, with visualizations (bar charts, pie charts, line graphs).

Streamlit app for Walmart customer sentiment analysis.

This is the project of Walmart's Customer Review Data Scrapping, Formatting, Sentiment Analysis and Visualization

This project focuses on scraping Walmart company reviews from Indeed and performing sentiment analysis using VADER.

This project focusses on web scraping Walmart company reviews from Indeed and performing sentiment analysis using VADER

This project performs a comparative sentiment analysis of customer reviews from Amazon and Walmart. The sentiment analysis uses Natural Language Processing (NLP) techniques to categorize reviews into positive, negative, or neutral sentiment. This project uses Pinecone for vector DB and Streamlit for front-end interface

This project explores the power of Natural Language Processing (NLP) and GenAI to enhance traditional sales forecasting. By analyzing customer sentiment from Amazon product reviews and aligning it with Walmart's historical sales data, we aim to uncover how public sentiment can influence purchasing behavior.

AI Engineering projects from Purdue University / Simplilearn's Microsoft AI Engineer certificate. Azure AI, NLP sentiment analysis, computer vision, GitHub Copilot-assisted development, Python, Walmart retail analytics, sales analysis & marketing campaign modeling. 5/23 complete.

This repository contains a comprehensive analysis of customer feedback for Walmart Canada, sourced exclusively from Trustpilot reviews. The goal of this project is to gain valuable insights into customer sentiments, preferences, and pain points, focusing on enhancing the overall customer experience.

This report provides a comprehensive analysis of Walmart store sales data, uncovering patterns and trends influenced by seasonal fluctuations. Recommendations are presented for inventory and marketing strategies, based on predictive analytics similar to the sentiment analysis methods used in a previous project.