A data analysis project exploring consumer behavior and sales trends through EDA using Python. Includes visualizations and insights derived from retail shopping data.

A data analysis project exploring online consumer behavior and FOMO effects using EDA on survey data.

An interactive Excel Dashboard analyzing bike sales trends using pivot tables and slicers. Visualizes customer demographics including income , commute distance , and age brackets to drive marketing insights.

Large-scale sentiment analysis on Amazon beauty reviews revealing psychophysical threshold effects

Career Foundry data analytics project to provide a client recommendations on a marketing strategy. Jupyter notebooks include cleaning and merging data along with creating new columns to offer the best understanding of consumers' interactions with products.

Dự án này cung cấp một nền tảng phân tích dữ liệu dựa trên thông tin nhân sinh để giúp các nhà nghiên cứu và doanh nghiệp có cái nhìn sâu sắc hơn về hành vi và xu hướng của người tiêu dùng tại Việt Nam.

Research Project

Effort–value dissociation: liking vs. price in consumer valuation

Dự đoán hành vi người tiêu dùng dựa trên dữ liệu lịch sử mua sắm và tương tác trực tuyến tại Việt Nam.

🧘‍♀️ Strategic marketing analysis of smart device fitness data (Fitbit). Identifies consumer behavioral trends to optimize product positioning and user retention for Bellabeat. Part of the Google Data Analytics Professional Certificate.

Analysis of smart device usage trends to identify growth opportunities for Bellabeat using R.

This repository presents a comprehensive exploratory data analysis (EDA) project that investigates customer shopping trends and sales performance using Python.

Survey-based data analysis of online shopping behavior, advertisement influence, and platform trust. Data collected via Google Forms and analyzed using Power BI to uncover consumer trends, preferences, and trust drivers across e-commerce platforms.

Smartphone Purchase Prediction Dashboard - An interactive web application for predicting and analyzing smartphone purchase behavior.

Analytical code for pulses 2021 data

Customer segmentation analysis for MixITup using demographic, behavioral, and preference data. Includes preprocessing, exploratory data analysis, standardization, categorical encoding, clustering using K-Means++ and K-Medoids, and strategic marketing recommendations based on customer profiles.

Paper-only case study on consumer preference prediction and false consensus; includes executive summary.

Personal Consumer Behavior Project, Display and Interpretation of Consumer Analytics Using Data From Kaggle.