University Recomendation system
This project presents a recommendation system for undergraduate admission seekers, which can help students choose the best graduate university matching their academic profile. It proposes a way to recommend universities based on the users profile and his preferences, which can likely increase his ch
2025-06-28 16:29:53 - Adil Khan
University Recomendation system
Project Area of Specialization Computer ScienceProject SummaryThis project presents a recommendation system for undergraduate admission seekers, which can help students choose the best graduate university matching their academic profile. It proposes a way to recommend universities based on the users profile and his preferences, which can likely increase his chance of getting an admit and avoid having to invest a huge amount in third parties. In this project, a web-based system will be designed and deployed where students will search for the university and give some input to the system. This input is matched with the trained dataset to measure the accuracy. It also uses reviews of students for the proposed recommendation system, which will process the student requirements and search from the database to give the student perfect advice and recommend a university to the user. We can improve the system by reviewing the user reviews given by the university.
Project ObjectivesFor an aspiring student who wants to apply for higher studies, university selection process is a challenging task as lot of different criteria need to consider during application process based on individuals requirement. Some of them succeed and get admission into their desired programs in desired universities, but some are not because of the academic level of colleges, which they have applied. This problem can be addressed by modeling a recommender system based on various classification algorithms. In this project based on the student data set and the student profile who is looking for the admit, various models will be trained and a list of 10 best universities will be suggested such that it maximizes the chances of a student getting admit from that university list we presented users a set of features that could possibly play an important role when making a decision on which university to choose for ones study In the main application, participants were asked to provide reviews for universities that might be familiar to them. that they had either attended for education or had sufficient experience with to make a judgment. Our project will mainly focus on the following objectives:
- To Get the Graduate University recommendation list, User need to provide his/her percentage and fee limitations, department of the undergraduate University.
- User can also search best universities in specific city, without giving much information, for specific department by just select a city and department.
- User can see all universities for specific program in specific city
- User can visit any universities website directly.
- User can provide reviews on universities.
- For posting reviews user need to sign-up and login...
Here we have used Knowledge based recommendation System where User inputs are taken into account and compare with the training data. we used different models like K-Nearest Neighbors and Feature weighted algorithms. K Nearest Neighbor: In KNN, the trained data is compared with test data and distances are calculated using Euclidean distance. It then classifies an instance by finding its nearest neighbors and recommend the top n nearest neighbor universities. Algorithm is stated as below. Input: undergraduate university, department, CGPA, GRE Scores of User
1. Initialize the value of k
2. For getting recommendation, iterate from 1 to number of
3. Calculate distance between test data and each row in the trained data.
4. Sort the distances in ascending order
5. Get top k rows and recommend to the user
Feature weighted algorithm: The weightage of all the features are taken and find the similarity score. Based on the similarity score, the universities with highest similarities will be recommended to student. Suppose w1, w2 are weights and f1 and f2 are features the similarity is calculated by formula Similarity score = w1* f1+w2*(1-f2)
Benefits of the ProjectUniversity selection process is a challenging task as lot of different criteria need to consider during application process based on individuals requirement. Some of them succeed and get admission into their desired programs in desired universities, but some are not because of the academic level of colleges, which they have applied. This problem can be addressed by modeling a recommender system based on various classification algorithms.
Student can get easily admission in their desire universities without any struggle.
Students dont need to go in every university and give entry test.
This system makes it easy for students to get admission based on academic performance.
Students can see which program offered by which university they dont need to go on every universitys website and search.
This system can also suggest universities Based on students reviews.
The final deliverable of " University Recommendation system" will be an web based application along with documentation manual having all technical details of the web application. It will be developed in bootstrap, PHP along with Mysql database. We will develop a machine using different algorithm in python and python's api that we will integrate with our web application.
Final Deliverable of the Project Software SystemCore Industry ITOther Industries Education Core Technology Artificial Intelligence(AI)Other Technologies Big DataSustainable Development Goals Quality EducationRequired Resources| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 75000 | |||
| Laptop(core I7) | Equipment | 1 | 40000 | 40000 |
| GPU(4GB) | Equipment | 1 | 30000 | 30000 |
| SSD | Miscellaneous | 1 | 5000 | 5000 |