User-Centric Adaptive User Interface Extended to Augmented Reality Application
User-Centric Adaptive User Interface Extended to Augmented Reality Application
Embedded Files
INTRODUCTION
INTRODUCTION
The project aims to develop a personalized adaptive user interface that can extend to augmented reality applications. It will collect user data like clicks, taps and navigation patterns to identify usage preferences. The goal is to increase user engagement and make AR experiences more seamless. By analyzing interactions, the most critical webpages and potential improvements can be identified. Overall, this technique can revolutionize user experiences and accessibility for both websites and emerging AR technologies.
KEY FEATURES
KEY FEATURES
Model: Kmeans Clustering The K-means clustering is valuable for web analysis, it provides an insight in the users behaviour and website optimization. The clustering can identify user patterns on the webpage such as frequency of visits and viewer for that. This is used to analyze the typical behavior of user journey through the website.
Usage: Identifies trends in attracting new users to specific webpages by analyzing unique views and average time spent, aiding in understanding user engagement patterns.
SKILLS
SKILLS
Language: Python
Technology: Data Analysis (Pandas, NumPy), Data Visualization (Matplotlib, Seaborn), Machine Learning (Scikit-learn), Data Transformation (Pandas), Statistical Analysis
Page updated
Google Sites
Report abuse