This project focuses on understanding the key factors influencing graduate admissions and how these factors interrelate. By analyzing historical admission data, the goal is to predict an applicant's chances of admission based on variables such as:
GRE Score TOEFL Score University Rating Statement of Purpose (SOP) Letter of Recommendation (LOR) Strength Undergraduate GPA Research Experience
Identify important factors: Explore which variables play a significant role in determining admission decisions. Interrelationship Analysis: Understand how these factors influence each other and their combined impact on admissions. Predict Admission Chances: Build a predictive model to estimate an applicant's chances of admission based on the other variables.
Data Collection & Preparation: The dataset consists of several applicant profiles with various attributes. We preprocess the data and engineer features to ensure optimal model performance. Exploratory Data Analysis: We explore the relationships between different variables and visualize key insights. Model Building: Using regression and classification algorithms, we develop a model to predict admission chances.
How strongly each factor correlates with admission outcomes. The importance of research experience and university rating in driving admission success. Predicting admission probabilities based on multiple applicant attributes.
The resulting model provides a percentage-based prediction for admission likelihood, helping future applicants understand the weight of different factors in their application process.
Admission Probability Estimation: Users can estimate their chances of being admitted into Ivy League or other top-tier universities by inputting their profile data (GRE score, GPA, research experience, etc.). This can help applicants set realistic expectations and plan accordingly.
Identifying Areas for Improvement: The model can highlight key areas where a user might need to improve, such as their GPA, GRE/TOEFL scores, or gaining research experience. By focusing on these areas, users can increase their chances of admission.
Personalized Course Recommendations: Based on the applicant's profile, the system can recommend specific courses, test preparation programs, or academic resources tailored to boost their admission chances. These recommendations can be targeted to areas that require the most improvement.
These features not only help users make informed decisions but also offer educational institutions and admissions consultants a data-driven approach to guide their clients more effectively.