In the present world of networking, people connect with one another from the nook and corner of the world. When it comes to student connections, it is essential for all the students to have a database accessible about the details of fellow students in their class to connect. There are few opportunities for overseas students at a university like Northeastern to meet classmates who will enroll in the same course or intake as them and form professional connections rather than connections on social media. In order to overcome this, our project “Northeastern Student Hub“ will help in connecting students from various colleges such as CPS, COE, KHOURY, and streams under one roof with the required data. From this data, a student can find his/her roommates and students of similar interests. This will help students connect with genuine students and not get scammed or dragged into third-party sources that collect potential student data.
Business Problem
Our project aims to create a holistic system for students who aspire to study abroad. The project's primary goal is to provide a student database that will help students access their peers who have applied to a particular university and their background, which includes their priorities. This system will largely help international students as most of them suffer due to the lack of a proper channel with legitimate information. This system will help students be connected well before their college start date which in a way helps students act like a bridge before they even meet in person.
Objective:
To desgin a student data model which facilatates on how the university functions:
- Data Extraction from created google forms
- EEE Model
- UML diagram
- Database Access via Python
- SQL Implementation
- NoSQL Mongo DB implementation
EER MODEL
UML Diagram
Relational Model
Student (Studentid, Name, Ph_no, Email, dateofbirth, gender)
Application_details (App_id, Stud_id, College_id, departmentid, status)
University_and_college (College_id, college_name, campus, email)
Department (Department_id, College_id, Depart_name, email)
Offers(departmentid, courseid)
Course (Course_id, Course_name, Professor_id)
Class_room (CRN_NO, course_id, studentid, Classschedule, Class_type, groupname)
Professor (Professor_id, Professor_name, email, Office_hours)
Teaching_assistant(taid, taname, email, officehours)
Sends(studentid, message_id)
Stores(crnno, message_id)
Message (message_id, content, Message_time, conversation_id)
Housing_and_food(Stud_id, House_type, Food_choice, rent_preference, Zipcode_preference)
Database Access via Python:
The database is accessed using Python and visualization of analyzed data is shown below. The connection of MySQL to Python is done using the MySQL connector, followed by the cursor. Execute to run and fetch from the query, followed by converting the list into a data frame using pandas’ library and using matplotlib to plot the graphs for the analytics.
Graph1:
The plot shows the distribution of students living in a particular zip code based on a
particular course. (Here course ‘AST’ was chosen)
Graph 2:
This bar graph shows the number of students in each college from Boston campus.
Graph3:
The pie chart shows the distribution of students living in each zip code based on their
spending preferences. Here student spending is divided into three groups ‘Economical’,
Budget Friendly, and Luxury.
