Graduate Student Admission Project

This repository contains the code and resources for the Graduate Student Admission Project, aimed at predicting the likelihood of admission for graduate school applicants based on various factors.

Table of Contents

• Overview
• Features
• Tech Stack
• Dataset
• Installation
• Usage
• Model Performance
• Contributing
• License

Overview

The Graduate Student Admission Project leverages machine learning techniques to predict admission probabilities based on applicant data such as GRE scores, TOEFL scores, undergraduate GPA, research experience, and more. This project is designed to assist admission committees in making data-driven decisions.

Features

• Data Preprocessing: Handles missing values, feature scaling, and one-hot encoding.
• Machine Learning Models: Includes multiple algorithms like Linear Regression, Decision Trees, and Random Forest.
• Feature Importance: Highlights the most influential factors in admission decisions.
• Interactive Visualizations: Provides insights through charts and graphs.

Tech Stack

• Programming Language: Python
• Libraries:
  • Pandas
  • NumPy
  • Scikit-learn
  • Matplotlib
  • Seaborn
  • Jupyter Notebook

Dataset

The dataset includes the following features:

• GRE Score
• TOEFL Score
• University Rating
• Statement of Purpose (SOP) Strength
• Letter of Recommendation (LOR) Strength
• CGPA (Undergraduate GPA)
• Research Experience (0 or 1)
• Chance of Admission

Source

Dataset Link (Add the actual link or source if available)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/graduate-admission-project.git

2. Navigate to the project directory:

   cd graduate-admission-project

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Launch the Jupyter Notebook:

   jupyter notebook

2. Open the main project notebook (e.g., Graduate_Admission_Analysis.ipynb).
3. Follow the steps in the notebook to preprocess data, train models, and evaluate performance.

Model Performance

The following table summarizes the performance of different models used in this project:

Model Performance

Model	Parameters	MSE	RMSE	R² Score
Linear Regression	Default parameters	0.003705	0.060866	0.818843
Lasso	alpha=0.1	0.015120	0.122964	0.260630
SVR	C=10, gamma='scale'	0.005675	0.075333	0.722489
Decision Tree	max_depth=5, criterion='squared_error',	0.006486	0.080534	0.682846
KNN	n_neighbors=10	0.004463	0.066803	0.781779
Random Forest	n_estimators=200, max_depth=10, random_state=42	0.004228	0.065026	0.793234
Voting Regressor	Combination of Linear Regression, Lasso, SVR, etc.	0.004194	0.064762	0.794910
Stacking Regressor	Combination of Linear Regression, Lasso, SVR, etc.	0.004195	0.064771	0.794849

Visualizations of model performance and feature importance can be found in the notebook.

• Linear Regression provides a good baseline with an R² Score of 0.818843.
• Lasso underperforms relative to other models, with an R² Score of 0.260630.
• Voting Regressor and Stacking Regressor achieve the best performance, both with an R² Score close to 0.7949.
• Random Forest demonstrates strong performance with an R² Score of 0.793234 and RMSE of 0.065026.

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Description of changes"

4. Push to the branch:

   git push origin feature-name

5. Open a pull request.

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Feel free to reach out with any questions or suggestions!