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

No description provided.

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.

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

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

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.

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.

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.

Customer Behavior Analysis project using Python and SQL to analyze customer demographics, engagement, product reviews, and purchase journeys for valuable business insights.

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

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

Estatística aplicada à análise de dados de clientes e planos da operadora Megaline. Projeto prático com Python, visualização e insights para suporte à decisão em telecom.

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

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

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

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.

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

Customer Purchasing Behavior Analysis and Sales Prediction

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.

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

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.

A baseline report for ACE Superstore, a UK retail chain, analysing regional sales, customer behaviour and product profitability across online and in-store channels.

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.

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

With SQL queries I explore the Sakila DVD Rental database, to present insights about customer behavior and rental patterns.

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.