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This is a submission for the : Crushing the Command Line
? WHAT I BUILT:
I created a Mood-Based Book Recommendation System that suggests books tailored to the user's current emotional state. The system combines collaborative filtering and content-based filtering techniques, enhanced with mood selection to deliver more emotionally relevant and personalized book recommendations.
KEY FEATURES:
?Mood-Based Recommendations: Get personalized book recommendations based on 7 different moods
?️Interactive UI: Colorful, user-friendly interface with visual feedback and animations
?Save Favorites: Save books you're interested in for later reference
Rate Books: Rate your saved books on a 5-star scale
Local Storage: Your saved books and ratings are stored in your browser
??Data Export/Import: Easily backup and restore your saved books
?Responsive Design: Works on desktop, tablet, and mobile devices
?️ LIVE DEMO:
?
--This version runs directly in your browser with no installation required!
DEMO:
Book Database:
The system includes 35 books (5 for each mood) covering various genres:
Technical Details:
Frontend: HTML5, CSS3, JavaScript (ES6+)
Storage: Browser Local Storage API
Design: Responsive design with CSS Grid and Flexbox
Fonts: Google Fonts (Roboto)
Customization:
Developers can easily extend the book database by modifying the books Database object in script.js. Each book requires:
?️ How I Used Amazon Q Developer Tool:
As a student building this book recommendation system, I used Amazon Q Developer to boost my productivity and streamline my development process. It helped me:
Understand unfamiliar code snippets quickly
Generate boilerplate code and function suggestions
Debug issues with smart, context-aware guidance
Ask real-time questions within my IDE without switching tools
Amazon Q acted like a coding mentor, especially helpful when I hit roadblocks or needed to accelerate certain parts of my workflow.
? Final Thoughts:
As a student, building this project was a great learning experience in both recommendation systems and practical tool usage. Integrating mood-based logic with collaborative and content-based filtering taught me how to blend user experience with data science fundamentals. Using Amazon Q Developer made the development journey smoother and more insightful.
I'm excited to keep improving this system and explore how AI tools can assist developers—especially learners like me—throughout the building process.
CODE REPOSITORY:
Lets connect:
? WHAT I BUILT:
I created a Mood-Based Book Recommendation System that suggests books tailored to the user's current emotional state. The system combines collaborative filtering and content-based filtering techniques, enhanced with mood selection to deliver more emotionally relevant and personalized book recommendations.
KEY FEATURES:?Mood-Based Recommendations: Get personalized book recommendations based on 7 different moods
?️Interactive UI: Colorful, user-friendly interface with visual feedback and animations
?Save Favorites: Save books you're interested in for later reference
Rate Books: Rate your saved books on a 5-star scaleLocal Storage: Your saved books and ratings are stored in your browser
??Data Export/Import: Easily backup and restore your saved books
?Responsive Design: Works on desktop, tablet, and mobile devices
?️ LIVE DEMO:
?
--This version runs directly in your browser with no installation required!
DEMO:
Book Database:
The system includes 35 books (5 for each mood) covering various genres:
- Fiction and fantasy
- Non-fiction and memoirs
- Science and philosophy
- Adventure and travel
- Self-help and personal development
Technical Details:
Frontend: HTML5, CSS3, JavaScript (ES6+)
Storage: Browser Local Storage API
Design: Responsive design with CSS Grid and Flexbox
Fonts: Google Fonts (Roboto)
Customization:
Developers can easily extend the book database by modifying the books Database object in script.js. Each book requires:
- Title
- Author
- Description
?️ How I Used Amazon Q Developer Tool:
As a student building this book recommendation system, I used Amazon Q Developer to boost my productivity and streamline my development process. It helped me:
Understand unfamiliar code snippets quickly
Generate boilerplate code and function suggestions
Debug issues with smart, context-aware guidance
Ask real-time questions within my IDE without switching tools
Amazon Q acted like a coding mentor, especially helpful when I hit roadblocks or needed to accelerate certain parts of my workflow.
? Final Thoughts:
As a student, building this project was a great learning experience in both recommendation systems and practical tool usage. Integrating mood-based logic with collaborative and content-based filtering taught me how to blend user experience with data science fundamentals. Using Amazon Q Developer made the development journey smoother and more insightful.
I'm excited to keep improving this system and explore how AI tools can assist developers—especially learners like me—throughout the building process.
CODE REPOSITORY:
Lets connect:
- ?️ Github:
- ? LinkedIn: