Electromagnetic relays used for power system .pptx
Electricity_Monitoring_Presentation.pptx
1. Project Title
Design & Development of Book Recommendation
System.
Project Group Number: 07
Group Member Details: 1. Disha Vishwakarma
2. Yuvika Sinha
3. Niharika Ninnoriya
Guide Details: Prof. Abhuday Tripathi
MINOR PROJECT-I
AD-508
2. Idea/Approach Details
Describe your idea Solution/Prototype here:
2
Describe your Technology stack here:
Abstract:
⮚ Our project aims to provide personalized book
recommendations to enhance the reading
experience for users.
⮚ To cater to individual preferences, we offer a
recommendation query page where users can
input book titles, authors, or years of publication for
tailored book suggestions.
⮚ Flask Integration: Our machine
learning model, developed using Jupyter
Notebook, was seamlessly integrated
with Flask for web deployment.
• Objective: Create a personalized book
recommendation system using collaborative filtering.
Features: The system offers top 50 book
recommendations on the homepage and a query page
for personalized suggestions based on user input.
Deployment: “Flask” a Python web framework, is used
for deployment of Machine Learning Model, providing a
user-friendly interface by HTML5 and CSS.
Goal: The project aims user to get meaningful book
recommendations accessible as per his/her Genre to a
wider audience.
3. Project Requirements
functional requirements
• Enable user registration and profile creation
with reading preferences.
• Implement collaborative filtering for
personalized book recommendations.
• Collect and preprocess book data and create
a user-item matrix.
• Allow user feedback and interactions,
including search and filtering options.
3
non functional requirements
Usability:
•The system should have an intuitive and user-
friendly interface for easy navigation and
interaction.
Performance:
•The system should respond to user requests
within seconds to provide a seamless user
experience.
Compatibility:
•Ensure that the system is compatible with a range
of web browsers and devices, including mobile
devices and desktop computers.
5. Design
Describe your Use Cases here
⮚
6
Describe your Dependencies / Show stopper here
⮚ Web Data Sources and Quality: The system
depends on reliable and up-to-date data sources
for book information and user preferences.
Ensuring data quality and maintenance is crucial
for accurate recommendations.
⮚ Machine Learning Frameworks: Dependency on
machine learning frameworks like Scikit-learn,
Numpy, or Pandas for implementing collaborative
filtering algorithms.
⮚ Web Framework and Hosting: The project relies on
the Flask web framework for the user interface
and a stable hosting environment, whether cloud-
based or on a dedicated server.
6. Deployment Details
Describe Deployment Details here
7
Server Environment Selection:
Choose a server environment for hosting your web
application and machine learning model, such as a cloud
service or a dedicated server.
Web Application Deployment:
Deploy the web application, built with Flask, to the selected
server environment, making it accessible to users.
Database Setup:
Configure the database to store book-related data and
user profiles, ensuring it aligns with your project's
requirements.
Security and Monitoring Implementation:
Implement security measures to protect user data and
system integrity.
Set up monitoring and logging tools to track system
performance and security.
