Analyse customer segmentation, sentiment on product review, and built a product recommender system

Black Friday Sales Analysis explores customer demographics, purchasing behaviors, and product trends to uncover insights and patterns driving sales during Black Friday events.

Multivariate Time Series Classification for Human Activity Recognition with LSTM

Predicting whether users will click on a promotional email for laptops based on historical user data and browsing logs.

Customer journey analysis with PM4PY in Python.

Building a nearest-neighbor classifier to predict online shopping purchase completions based on user browsing behavior. The project uses a dataset of 12,000 sessions, analyzing features like pages visited, session duration, and bounce rates

This is a customer loyalty analysis based on historical purchase behavior in R language.

Customer Purchasing Behavior Analysis and Sales Prediction

This project explores customer behavior and sales trends to help this small restaurant thrive.

Predicts customer upgrade likelihood using logistic regression, random forest, and XGBoost. Features NLP techniques and memory optimization.

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

An interactive interface for performing CRUD operations (Create, Read, Update, Delete) on a MySQL database related to Zomato data.

This project utilizes machine learning to analyze and segment e-commerce customer behavior. It predicts purchases and clusters customers based on demographic data and product preferences, aiming to optimize marketing strategies and enhance customer satisfaction.

The "Store Sales Database" project analyzes 100K sales entries, leveraging Python, SQL, and Power BI to manage, analyze, and visualize store performance. It provides insights into sales trends, regional performance, and customer behavior through real-time analytics, detailed reporting, and dynamic dashboards to support data-driven decisions.

This repository contains Power BI projects showcasing data analysis and interactive dashboards. Each project includes detailed visualizations and insights on diverse topics such as loan analysis, sales performance, and customer behavior.

This project is focused on identifying key products that contribute significantly to revenue and analyzing customer purchase behavior

Analyzing customer ordering behavior and product performance to enhance demand forecasting and customer experience.

Cab Investment Strategy in the US examines market trends, customer demographics, and profitability for Pink Cab and Yellow Cab, offering insights to guide strategic investment decisions through data analysis, visualizations, and forecasting.

This project focuses on RFM (Recency, Frequency, and Monetary) Analysis, a powerful customer segmentation technique used in marketing and business analytics. The analysis helps businesses identify their most valuable customers, potential loyalists, at-risk customers, and churned users.