A sample churn prevention solution for an fintech app
Wrangling & Modeling of Duolingo Data (48-hour Analysis Challenge)
Clean and Apply RFM technique to rank and group clusters to identify the best customers and perform targeted marketing campaigns, using real online transaction data
Introducing the Ultimate A/B Testing and Experiment Analysis Dataset! 👀
Modern push notification demo app showcasing OneSignal + Firebase integration with beautiful Jetpack Compose UI
DANA Automation Data Scientist Take Home Test: Feature Creation + User Segmentation
🧠 Case study on data preprocessing and behavioral analysis of TechnoMagicLand visitors. Includes clustering, correlation, and visualization in R, with focus on identifying repeat visitors and improving engagement strategies.
# User Segmentation_Online Retail Data ## Ikhtisar Proyek ini bertujuan untuk melakukan segmentasi pengguna menggunakan data retail online. Dengan menggunakan teknik RFM (Recency, Frequency, Monetary) dan analisis data, kami mengidentifikasi berbagai segmen pelanggan untuk membantu dalam meningkatkan retensi pelanggan dan strategi pemasaran.
This research study focuses on analyzing user behavior data from Duolingo, a language learning app. The primary objective is to predict user churn using semi-supervised learning techniques.
DiscoverIQ is a Power BI analytics project that transforms large-scale ecommerce behavior data into product discovery insights, ranking opportunities, funnel analysis, and conversion optimization recommendations.
Braze lifecycle marketing analytics pipeline
User profiling refers to creating detailed profiles that represent the behaviors and preferences of users, and segmentation divides the user base into distinct groups with common characteristics, making it easier to target specific segments with personalized marketing, products, or services.
Analyzes user behavior and demographic data to create distinct user segments for targeted advertising and personalized experiences.
This project analyzes social media ad campaign performance using a dataset of user interactions. It uncovers which platforms, ad categories, and user segments drive the highest engagement, conversions, and simulated revenue.
사이드 프로젝트로 진행한 음식 배달 앱 로그 데이터 분석 프로젝트입니다.
End-to-end data analysis case study: K-Means segmentation and predictive modeling of 200,000 Instagram users vs. well-being indicators (stress, sleep, happiness). Client report, technical report and defense deck included.
User segmentation using a sort of classifiers (some quite uncommon like the Fuzzy K-Nearest Neighbours).
In-app survey SDK for Web, iOS, and Android.
Rule-Based User Segmentation
Segmentation comportementale d'utilisateurs - méthodologie hybride règles + clustering, données synthétiques
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