Designed an end-to-end analysis pipeline using MySQL, Python, and Power BI. Analyzed purchase patterns and customer sentiment using SQL and Python (TextBlob, pandas). Built an interactive Power BI dashboard for sales, trends, and sentiment insights.

Analyze customer behavior using SQL and Python to extract insights on purchase patterns, sentiment analysis, and marketing effectiveness.

End-to-end analytics project using SQL, Python, and Power BI to uncover customer purchase patterns and review sentiments.

Designed an end-to-end analysis pipeline using MySQL, Python, and Power BI. Analyzed purchase patterns and customer sentiment using SQL and Python (TextBlob, pandas). Built an interactive Power BI dashboard for sales, trends, and sentiment insights.

Developed a Recommendation Engine to suggest relevant products based on user behavior and purchase history, and built a Sentiment Analysis System to classify customer reviews into positive, negative, or neutral sentiments for actionable insights.

Data science Project for e-commerce clothing fashion brand, analyzing customer feedback data using text mining, sentiment analysis and natural language processing to identify purchasing behavior of users

1. EDA on Retail Sales Data Perform exploratory data analysis (EDA) on retail sales data to uncover patterns, trends, and insights that can help businesses improve decision-making. 2. Customer Segmentation Divide customers into different groups based on purchasing behavior and demographics to improve marketing strategies. 3. Sentiment Analysis

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📊 Customer Purchase Behavior and Sentiment Analysis using data visualization and NLP techniques to uncover patterns, customer preferences, and insights for better decision-making.

Customer purchase behavior and sentiment analysis, covering product names, categories, quantities, and prices, revealed insights into buying patterns and customer feedback. Reporting supported refined marketing strategies and optimized inventory management.

The primary objective of this project is to identify patterns in customer purchase behavior and analyze customer sentiment based on product reviews. These insights will empower businesses to make informed decisions about marketing, product development, and customer engagement strategies.

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Customer Purchase Behavior Analysis: Streamlit app for customer segmentation, churn prediction, revenue visualization, and review sentiment insights.

Analyze customer behavior using SQL and Python to extract insights on purchase patterns, sentiment analysis, and marketing effectiveness.

End-to-end customer purchase behavior & sentiment analysis pipeline using MySQL, Python (pandas, TextBlob), and Power BI. Delivers actionable insights on sales trends, customer engagement, and product sentiment.

This dataset captures customer purchasing behavior, feedback, sentiment, and complaint resolution patterns across multiple online shopping platforms. It is ideal for sentiment analysis, customer experience research, and operational performance insights.

Created a database and imported purchase and review data using SQL, transforming and normalizing it. Analyzed data with Python, performing sentiment analysis on review texts and calculating key metrics. Developed an interactive PowerBI dashboard to visualize purchase trends, customer behavior, product performance, and sentiment analysis results.

DeepCSAT – Ecommerce is a deep learning project that predicts customer satisfaction scores using behavioral, transactional, and feedback data. It leverages NLP and sentiment analysis to enhance post-purchase experience and improve customer retention.

This project aims to analyze customer behavior, sentiment, and purchasing patterns for ShopEasy, an online retail business. Using SQL and Python, the analysis helps identify key factors affecting customer engagement, product performance, and marketing effectiveness.

To apply K-Means clustering to segment customers based on their purchase behavior and use Natural Language Processing (NLP) techniques to preprocess text data and build a sentiment analysis model.

Amazon sales datasets are cleaned using Pandas—handling duplicates, missing values, text standardization, data types, and outliers—followed by EDA, sentiment analysis via AWS Comprehend, and customer segmentation based on purchase behavior, sentiment scores, and RFM (Recency, Frequency, Monetary) metrics.

We have Applied K-Means clustering to segment customers based on their purchase behavior by using Natural Language Processing (NLP) techniques to preprocess text data and build a sentiment analysis model

data analysis is performed using amazon customer reviews stored in an sqlite database. in this program, the goal was to clean the data, perform exploratory data analysis, analyze user behavior based on purchase frequency, and conduct a basic sentiment analysis