customer-behavior-analysis

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

End-to-end data analytics project using Python, SQL, and Power BI to analyze customer shopping behavior, uncover insights, and visualize trends through an interactive dashboard.

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

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.

📊 Analyze customer churn, segment behavior, and uncover insights to improve retention for telecom firms using data visualization and advanced analysis techniques.

A data analysis project using Python, SQL, and Power BI to study customer purchasing patterns. Python was used for data cleaning and analysis, SQL for querying transactional data, and Power BI for building interactive dashboards to visualize customer segments, sales trends, and product performance.

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

Helping Trips & Travel.Com reduce marketing costs by predicting which customers are most likely to purchase a holiday package — powered by Random Forest. Click Below Link to see ML Pipeline

Hotel Booking EDA Project -- Exploratory Data Analysis of hotel booking demand data (city & resort hotels) to uncover booking trends, cancellation behavior, customer preferences, and insights for hotel management.

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.

Customer behavior analytics project using Python, Pandas, and SQL to analyze purchasing patterns, customer segmentation, and discount impact through real-world business queries.

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.

Analyzed Cyclistic’s bike-share data to uncover behavioral differences between casual riders and annual members. Used SQL, MySQL, DuckDB, Python, and Power BI across an end-to-end APPASA workflow to validate, process, analyze, visualize, and translate findings into membership growth recommendations. Focused on casual-to-member conversion.

SQL-based Comprehensive Analysis of E-commerce Customer Behavior and Revenue using Google BigQuery, uncovering Key Insights on Retention, Segmentation, Product Performance and Revenue Drivers

Pizza Sales Analytics is an interactive Power BI dashboard that analyzes pizza orders and sales trends. It identifies peak ordering times, most popular pizzas, top-selling categories, and total revenue, helping businesses make data-driven decisions and optimize operations.

End-to-End Customer Shopping Behavior Analysis using Python, Pandas, PostgreSQL, SQL, and Power BI with data cleaning, feature engineering, business analysis, and interactive dashboards.