The FOREIGN 5_final year project on AI.pptx

All' about final year project.

1. FOREIGN UNIVERSITY ADMISSION PREDICTION DASARI SRI LAKSHMI CHITTALA ESWAR RAO KADA SRI RAM KHANDAVALLI SIRISHA KURUPUDI ANANTHA LAKSHMI UNDER THE GUIDANCE OF CH.ANURADHA

2. CONTENTS ABSTRACT INTRODUCTION EXISTING SYSTEM & DISADVANTAGES PROPOSED SYSTEM & ADVANTAGES REQUIREMENT ANALYSIS MODULES TECHNOLOGIES USED SYSTEM DESIGN OUTPUT SCREENS TEST CASE CONCLUSION REFERENCES

3. ABSTRACT In an era of global education, the demand for accurate and efficient methods to predict foreign university admissions is paramount. This project explores the application of machine learning algorithms to predict the likelihood of admission based on key features such as GRE Score, TOEFL score, University rating, Statement of Purpose (SOP), Letter of Recommendation (LOR), CGPA, and Research experience. The dataset, comprising a diverse set of applicants' profiles, serves as the foundation for model development and evaluation. Various machine learning algorithms, including XGBoost , Decision Tree, Random Forest, Gradient Boosting, k-nearest Neighbors (KNN), Linear Regression , and Support Vector Machine (SVM), were employed to identify the most effective model for predicting admission outcomes. Ultimately, the project not only offers a practical solution for foreign university admission prediction but also underscores the transformative impact of machine learning in optimizing critical decision-making processes in education.

4. INTRODUCTION In the dynamic landscape of global education, the process of foreign university admissions stands as a critical gateway for aspiring students and a complex decision-making challenge for institutions. The aim of this project is to harness the power of machine learning to streamline and optimize this intricate process. By leveraging a dataset enriched with key applicant attributes such as GRE Score, TOEFL score, University rating, SOP, LOR, CGPA, and Research experience, the project seeks to develop a predictive model for foreign university admission outcomes. This project holds significant implications for both prospective students and academic institutions. For applicants, it provides a tool for assessing their chances of admission based on quantifiable metrics, fostering informed decision-making in the pursuit of higher education abroad. Meanwhile, universities stand to benefit from a more streamlined and data-driven admissions process, optimizing resources and enhancing the overall efficiency of their academic intake procedures. The journey of this project involves not only the development of a high-accuracy predictive model but also an insightful comparison of various machine learning techniques

5. EXISTING SYSTEM & DISADVANTAGES The current system for foreign university admissions relies predominantly on manual processes and human judgment, involving extensive reviews of application materials such as GRE scores, TOEFL scores, and recommendation letters. Prospective students submit their credentials, and selection committees manually assess these materials to make admission decisions. This manual approach is time-consuming, prone to errors, and can introduce biases. DISADVANTAGES: Resource Intensiveness Inconsistency Across Institutions Inefficiency in Handling Large Volumes Neglect of Diverse Data Limited Predictive Power Subjectivity and Bias

6. PROPOSED SYSTEM & ADVANTAGES The proposed system aims to overcome the limitations of the existing manual admission process by leveraging machine learning for foreign university admissions. The proposed system makes use of automatic application evaluation, algorithm selection & optimization, and transparent decision-making to ensure the security of the applicant’s data ADVANTAGES: Increased Accuracy Objective Decision-Making Efficiency and Time Savings Versatility with Multiple Algorithms Empowered Decision-Making for Applicants Transparency in Evaluation

7. REQUIREMENT ANALYSIS: HARDWARE REQUIREMENTS : Processor : i3 RAM : 4 GB(min) Hard Disk : 20 GB KeyBoard : Standard Windows Keyboard Mouse : Two or ThreeButton Mouse Monitor :SVGA SOFTWARE REQUIREMENTS: Operating system : Windows 10/11 Coding Language : Python. Front-End : HTML5, CSS3,Javascript. Back-End : Django Data Base : MySQL (XAMP Server).

8. MODULES : Data Ingestion Module: Responsible for importing and loading datasets. May include functions to handle different data formats (CSV, Excel, etc.). Data Preprocessing Module: Includes functions for cleaning and preparing the dataset. Handles tasks like handling missing values, scaling, encoding categorical variables, and other preprocessing steps. Feature Engineering Module: Focuses on creating new features or modifying existing ones to enhance the model's predictive capabilities. May include functions for deriving insights from the existing dataset. Model Training Module: Contains functions for training machine learning models. Involves selecting algorithms, splitting data, training the model, and potentially performing hyperparameter tuning.

9. User Interface Module: Develops the interface for users to interact with the model. Allows users to input their data and receive predictions. Deployment Module: Prepares the model for deployment in a production environment. Integrates the model with other systems and ensures scalability. Logging and Monitoring Module: Implements logging mechanisms to record model performance and user interactions. Monitors the deployed model for potential issues and updates. Security Module: Incorporates security measures to protect user data and ensure compliance with privacy regulations. Addresses potential vulnerabilities in the system.

10. TECHNOLOGIES USED 1) WEB DEVELOPMENT TECHNOLOGIES : HTML, CSS, and JAVASCRIPT for Frontend Development. PYTHON Programming language DJANGO for Backend framework. MACHINE LEARNING : Linear Regression, XGBoost , Decision Trees, Random Forest, Gradient Boosting, k-nearest Neighbors (KNN), and Support Vector Machine (SVM). 2) DATABASE MANAGEMENT SYSTEMS : SQL

11. SYSTEM DESIGN Admin Use Case Diagram User Use Case Diagram

12. SYSTEM DESIGN Admin Sequence Diagram User Sequence Diagram

13. SYSTEM DESIGN Class Diagram Admin Activity Diagram

14. SYSTEM DESIGN User Activity Diagram Data Flow Context Diagram

15. OUTPUT SCREENS

18. TESTCASES TEST CASE ID TEST CASE NAME TEST CASE DESCRIP  -ION STEP EXECUTE D RESULTS ACTUAL RESULTS TEST CASE   STATUS 1 Login new user Validate login To verify login page on name login page Click new user, enter user name, and password Enter the login form View the form pass 2 Login old user Validate login page To verify   login name on page Enter the name and password Validate user Validate user pass 3     Upload details Upload user details Verify Admission prediction Entering   User details Validate details Validate details Pass 4 Admin login Validate login Verifying login page Enter admin name and password Validate login Validate login Pass 5 View users validating & manage users Verify users Verify & manage the users Display user login page Display user login page pass 6 Check for all algorithms Validate marks Verifying marks Applying best algorithm for prediction Displaying the accuracy of algorithm Displaying the accuracy of algorithm Pass 7 Log out Log Out Logging out form Click on logout button Successful logout Log out Pass

19. TESTCASES TEST CASE ID TEST CASE NAME TEST CASE DESCRIP  -ION STEP EXECUTE D RESULTS ACTUAL RESULTS TEST CASE   STATUS 1 Login invalid Invalid login credentials When the user enters wrong credentials it will display invalid login Enter correct credentials Invalid User View the form Fail 2 Admin invalid Validate admin page When the user enters wrong credentials it will display invalid admin login Enter the name and password Invalid Admin Validate Admin Fail 3 Otp Generation Otp validation Enter mobile no & check for the otp Enter mobile number Otp not generated due to incorrect mobile no Otp generated successfully Fail

20. REFERENCES 1. Sridhar et al. (2020) developed a University Admission Prediction System using Stacked Ensemble Learning. 2. Haythorhwaithe et al. (2013) introduced a special issue on learning analytics. 3. Ragab et al. (2012) proposed HRSPCA, a Hybrid Recommender System for predicting college admission. 4. Sivasangari et al. (2021) worked on predicting the probability of university admission using Machine Learning. 5. Türker et al. (2020) presented a Deep Hybrid Recommender System. 6. Pandian (2019) reviewed machine learning techniques for managing voluminous information. 7. Kumar (2021) constructed a Hybrid Deep Learning Model for predicting children's behavior based on their emotional reactions .

21. This project uses machine learning to predict foreign university admissions, making the process fairer and more efficient. By analyzing applicant data like test scores, recommendations, and GPA, it built a predictive model using Linear Regression. With 81% accuracy, it helps students understand their admission chances and universities improve their selection process. The user-friendly interface benefits students and institutions alike, reducing bias and subjectivity. Future improvements could include advanced algorithms and more features, advancing data-driven decision-making in global education. This project showcases the power of machine learning in transforming university admissions. Conclusion

22. Thank You