Admission

Overview

This project aims to predict the likelihood of a student being accepted into graduate school based on various application factors using deep learning techniques. By analyzing a dataset containing parameters such as test scores and other application factors, we employ TensorFlow with Keras to create a regression model. The goal is to gain insights into the graduate admissions process and to help prospective students improve their application strategies.

Dataset

The dataset, admissions_data.csv, includes several features relevant to graduate school applications:

• GRE Scores
• TOEFL Scores
• University Rating
• SOP (Statement of Purpose)
• LOR (Letter of Recommendation Strength)
• CGPA (Undergraduate GPA)
• Research Experience
• Chance of Admit (Target Variable)

Requirements

• Python 3.x
• TensorFlow 2.x
• Keras
• NumPy
• Pandas
• Matplotlib
• Scikit-learn

Installation

First, make sure Python 3 is installed on your system. Then, install the required libraries using pip:

pip install tensorflow numpy pandas matplotlib scikit-learn

Model Architecture

The regression model is designed as follows:

• An input layer that matches the number of features in the dataset.
• Two hidden layers with ReLU activation and dropout layers to prevent overfitting.
• An output layer with a single neuron for predicting the chance of admission.
• The model uses the Adam optimizer and Mean Squared Error (MSE) as the loss function.

Training

The model is trained with the following strategy:

• The data is split into training and test sets (67% training, 33% test).
• Features are standardized using StandardScaler.
• Early stopping is implemented to halt training when the validation loss stops improving, preventing overfitting.
• The model is evaluated using the test set, with results reported in terms of MSE and Mean Absolute Error (MAE).

Evaluation

The performance of the model is evaluated using:

• Mean Squared Error (MSE) and Mean Absolute Error (MAE) on the test set.
• R-squared score to determine how well the regression model predicts the target variable.
• Plots of MAE and loss over epochs for both training and validation sets to visualize the learning process.

Usage

Run the script to train the model and evaluate its performance. It will output the MSE, MAE, and R-squared score, along with plots showing the model's training history:

python admissions_prediction.py

Contributing

Contributions to the project are welcome! Please fork the repository, make your changes, and submit a pull request.