Students are often worried about their chances of admission to University. The aim of this project is to help students in shortlisting universities with their profiles. The predicted output gives them a fair idea about their admission chances in a particular university. This analysis should also help students who are currently preparing or will be preparing to get a better idea.
- To understand regression and classification problems
- To grab insights from data through visualization.
- Applying different ML algorithms to determine the probability of acceptance in a particular university.
- Evaluation metrics
- Build a web application using the Flask framework.
About: The graduate studies dataset is a dataset which describes the acceptance probability of a student based on the following parameters:
1. GRE Score (out of 340)
2. TOEFL Score (out of 120)
3. IELTS Score (out of 9)
4. University Rating (out of 5)
5. Statement of Purpose/ SOP (out of 5)
6. Letter of Recommendation/ LOR (out of 5)
7. Research Experience (either 0 or 1)
8. Chance of Admittance (ranging from 0 to 1)
This dataset was built with the purpose of helping students to shortlist universities based on their profiles. The predicted output gives them a fair idea about their chances for a particular university. This dataset is inspired by the UCLA Graduate Dataset from Kaggle.
