This tool models and optimizes user tasks based on real-world behaviors. It transforms individual task models into unified, constraint-driven representations, using examples like Wordle to demonstrate its effectiveness. The tool visualizes task flows for better design and efficiency.

The goal is to develop a system that predicts which movies a user is likely to enjoy based on their preferences and rating history. The project leverages collaborative filtering and exploratory data analysis to understand user behavior and recommend movies in various genres accordingly.

This repository contains the data and experimental code accompanying the paper: Lüdemann, R., Schulz, A., & Kuhl, U. (2024, November). Generation Gap or Diffusion Trap? How Age Affects the Detection of Personalized AI-Generated Images. In International Conference on Computer-Human Interaction Research and Applications (pp. 359-381)

A movie recommendation system is a type of intelligent system that suggests movies to users based on their past preferences and behavior. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Python-based User Behavior Analysis Project conducted in Google Colab. Explore, analyze, and optimize user experiences. 📊🚀 #DataScience #ProductAnalytics

Live viewership analytics using Kafka, PySpark, forecasting, and Grafana dashboards