Project Overview
This project involves training and comparing various machine learning models to predict admission chances. The models use features such as GRE and TOEFL scores, CGPA, and research experience to estimate the probability of admission.
Key Outcomes
- Developed regression and ensemble models for admission prediction.
- Achieved the highest R-squared score (0.763) using Ridge Regression.
- Identified CGPA as the most influential feature with 85.36% importance.
Tools and Libraries
- Python
- Scikit-learn
- Pandas
- NumPy
- Matplotlib
- Seaborn
Additional Features
A detailed annotated file provides additional analysis and insights. Click the link below to access it:
View Annotated DetailsView the Code
Click the link below to view the full code and documentation for this project on GitHub:
View on GitHub
We see that CGPA is the most important factor
to decide whether a student goes to college or not.