Analytics Project | Gluu

Main Goal for This Project This project will apply data analytics tools and methodologies, centered around PostHog, to build, assess, and optimize the Gluu Admin Dashboard. The objective is to deliver actionable insights based on user data, content metrics, and onboarding funnels to enhance administrative decision-making and improve overall platform performance. Project Overview & Objective Statement Gluu is shifting its focus to develop a robust Admin Dashboard, a critical internal tool for managing the platform and its users. The key objective is to create a centralized interface that provides deep insights into user activity and content engagement. Objective: Analyze user behaviour, content trends, and system-wide statistics to: ● Identify user engagement patterns and pain points within the platform. ● Quantify the effectiveness of the user onboarding process. ● Recommend data-backed improvements for user management and content strategy. ● Create dashboards for monitoring key platform metrics.

Enhancing Peer-to-Peer Rental Transactions for Gluu.Repair

Gluu.Repair is transitioning from Version 1 to Version 2, aiming to enhance its Peer-to-Peer Rental Feature. The current system allows users to share specific collections with clients, but lacks a streamlined process for clients to select items, choose rental durations, and view the total value of their rental requests. The project focuses on designing a comprehensive transaction flow that addresses these gaps. Key tasks include developing a user-friendly interface for clients to select items and rental periods, integrating a system to display the total rental value, and creating a seamless approval and payment capture process. The goal is to ensure a smooth and efficient rental experience for both users and clients, aligning with Gluu.Repair's organizational change objectives.

Image Recognition Model Enhancement for Condition Reporting

The project focuses on enhancing Gluu's multi-modal image recognition model to improve the accuracy and efficiency of condition reporting for client products. The goal is to develop a custom Convolutional Neural Network (CNN) and integrate You Only Look Once (YOLO) object detection techniques to better identify and classify product conditions. This project will allow learners to apply their knowledge of machine learning, computer vision, and neural networks to a real-world application. By focusing on CNN and YOLO, the project aims to create a robust system capable of handling diverse product images and providing precise condition assessments. The project will involve tasks such as data preprocessing, model training, and performance evaluation, all of which are crucial for developing a reliable image recognition system.

AI Governance and Security Framework for Gluu.Repair

Gluu.Repair is committed to promoting sustainability and responsible consumer choices through the repair and repurposing of luxury items. As the company integrates AI into its operations, establishing a robust governance and security framework is essential to ensure the ethical and secure use of AI technologies. This project aims to develop a foundational layer of governance and security for Gluu.Repair's AI application. The project will involve researching best practices in AI governance, identifying potential security vulnerabilities, and proposing a comprehensive framework that aligns with industry standards. The goal is to create a secure and transparent AI environment that supports Gluu.Repair's mission while safeguarding user data and maintaining trust. - Research AI governance best practices and security protocols. - Identify potential security vulnerabilities in the current AI application. - Propose a governance and security framework tailored to Gluu.Repair's needs. - Ensure alignment with industry standards and ethical guidelines.