Project Title:
[Your Project Title]
Subtitle:
[A short, eye-catching description of your experience]
Pathway:
[Your Pathway]
Mentor Chains® Role:
[Your Role]
Goals for the Internship:
- [Your first goal]
- [Your second goal]
- [Your third goal]
Be concise. Draw people in. Have 5 goals max.
Achievement Highlights:
- [First highlight]
- [Second highlight]
- [Third highlight]
Be concise. Draw people in. Have 5 highlights max
Challenges Faced:
- [First challenge]
- [Second challenge]
- [Third challenge]
Detailed Statement:
- This is your project report. Focus on technical and soft skills. Make this as detailed as possible to accurately capture your contributions to the project.
Project Title: AI Code Assistants
Subtitle: Empowering Developers: Your AI-Powered Code Crafting Companion
Pathway: Machine Learning Models
Mentor Chains® Role: Mentor Chain Participant
Goals for the Internship:
- Data Cleaning and Preprocessing: One crucial aspect of this project is ensuring the quality and consistency of the data obtained from various sources. The third goal is to implement robust data cleaning and preprocessing techniques to handle noise, missing values, and format inconsistencies in the scraped data. This will involve tasks such as text normalization, removing duplicates, handling special characters, and ensuring data integrity to improve the accuracy of the recommendation chatbot.
- User Engagement Analysis: To measure the effectiveness of the AI recommendation chatbot, it’s essential to track user engagement and satisfaction. The fourth goal is to implement analytics and tracking mechanisms to monitor user interactions, user feedback, and the success rate of recommendations. By analyzing user behavior and feedback, you can iteratively improve the chatbot’s performance and enhance the user experience.
- Integration and Deployment: The fifth goal is to integrate the AI recommendation chatbot into relevant platforms and systems. This involves deploying the chatbot on a user-friendly interface and ensuring it can seamlessly interact with users on platforms like Facebook, a website, or a dedicated application. Additionally, you’ll need to implement security measures to protect user data and ensure the chatbot’s scalability to handle a growing user base.
Achievement Highlights:
- Extracting Extensive Data from Facebook Pages:
Successfully collecting a substantial volume of data from Facebook Pages involved overcoming technical challenges related to data structure, access limitations, and efficient scraping. This achievement demonstrates adept navigation of complex web interfaces, ensuring data integrity, and extracting valuable insights from a platform with diverse content types. You can find more details here.
- Collaborative Recommendation-Based Chatbot Creation:
Co-creating a recommendation based chatbot showcased effective teamwork, communication, and technical prowess. Developing a functional chatbot necessitated harmonizing various components such as natural language processing, user interaction flow, and backend integration. This achievement underscores the ability to collaborate seamlessly while contributing essential skills to a project with the end goal of enhancing user engagement and support.
Challenges Faced:
- Starting with the creation of a script from scratch for Facebook scraping, the challenge lay in understanding the intricacies of the Facebook platform’s structure and data organization. Navigating through various data elements like posts, comments, reactions, and timestamps required a comprehensive grasp of the underlying data structure. Ensuring the script was efficient, error-resistant, and adaptable to potential changes in Facebook’s interface demanded careful coding and testing. You can find more details here: Twitter and Facebook Scrapping.docx
- Deploying a ready-made library on a Mac M1 device introduced compatibility challenges. As the Mac M1 architecture differs from conventional systems, ensuring that the chosen library was optimized for this platform was crucial. Compatibility issues could arise, affecting the library’s performance or even rendering it non-functional. This required thorough research, potentially necessitating adjustments to the library’s codebase to ensure seamless operation on the Mac M1. You can find more details here: Twitter and Facebook Scrapping.docx
- Scraping a large amount of data further compounded the challenges. Handling substantial data volumes often strains system resources, leading to potential slowdowns or crashes. Optimizing the scraping process to efficiently manage memory, processing power, and network resources was essential to prevent performance bottlenecks and data loss.
Detailed Statement:
· This is your project report. Focus on technical and soft skills: Progress Report - AI Code Assistants
Link to self assessment: AI Code Assistants Report - Israa Bashir - Google Docs