Graduate Admission Prediction using ANN
This project focuses on predicting graduate admission probabilities using Artificial Neural Networks (ANN). The model is trained on historical data of graduate applicants, incorporating various features such as GRE scores, TOEFL scores, university ratings, and letters of recommendation. By leveraging ANN, it aims to provide accurate predictions that assist prospective graduate students in understanding their likelihood of admission.
The dataset used in this project is available in the data directory. It is in CSV (Comma-Separated Values) format.
This dataset is tailored for predicting graduate admissions from an Indian perspective, focusing on parameters typically considered crucial during applications for Masters programs.
Dataset Source Link: kaggle dataset
Note: Switch to T4 GPU for faster execution
This predictive model is intended for educational purposes only. Users should not rely on its predictions for actual admission decisions. Admission outcomes are influenced by numerous factors beyond the model's scope, such as individual qualifications, application strength, and university-specific criteria. The model's predictions should be used as educational guidance and not as definitive indicators of admission likelihood.
Contributions are welcome! If you have ideas for improving the model or adding new features, please feel free to fork the repository and submit a pull request.
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