AcceptifyAI

Accuracy

Our College Acceptance Predictor has achieved an accuracy of 90.99% on our testing dataset. This accuracy is calculated based on the Mean Absolute Error (MAE) of 0.3604 across four possible prediction classes.

Data Collection and Preprocessing

We collected a large dataset from r/collegeresults, which was then preprocessed and categorized using GPT-4o. This involved extracting relevant features such as GPA, test scores, extracurricular activities, and more.

Feature Engineering

We applied various feature engineering techniques, including:

• One-hot encoding for categorical variables
• Interaction terms for key features
• Polynomial features for important numerical variables
• Aggregated features to capture overall academic and extracurricular performance

Model Architecture

Our prediction system uses an ensemble of two models:

1. XGBoost: A gradient boosting model optimized for tabular data.
2. Neural Network: A deep learning model with multiple dense layers, batch normalization, and dropout for regularization.

Training Process

We used k-fold cross-validation and hyperparameter tuning via Optuna to optimize our models. The training process involved handling class imbalance through techniques like SMOTE (Synthetic Minority Over-sampling Technique).

Prediction Pipeline

When a user submits an application, the following steps occur:

1. GPT-4o processes the input to extract relevant features.
2. These features are preprocessed and engineered to match our training data format.
3. Both XGBoost and Neural Network models make predictions.
4. The predictions are ensembled (averaged) for a final score.
5. This score is adjusted based on the specific college's acceptance rate.
6. The final probability is calculated and presented to the user.

Note: While our model shows high accuracy on our testing dataset, individual predictions may vary. Many factors in college admissions are subjective and can't be perfectly predicted by any model. Use this tool as a guide, not as a definitive answer.

Are you just using ChatGPT?

While we use GPT-4o for labeling our training and testing data, the actual prediction process is fundamentally different and more sophisticated:

• We use GPT-4o to extract structured information from unstructured application data, creating a standardized dataset.
• Our prediction model is an ensemble of two advanced machine learning models: XGBoost and a Neural Network.
• The XGBoost model is optimized using Optuna for hyperparameter tuning.
• Our Neural Network uses multiple dense layers with batch normalization and dropout for regularization.
• We apply advanced feature engineering techniques, including one-hot encoding, interaction terms, and polynomial features.
• The training process involves k-fold cross-validation and techniques like SMOTE to handle class imbalance.
• Final predictions are made by combining the outputs of both models and adjusting based on specific college acceptance rates.

This approach allows us to leverage the strength of GPT-4o in natural language understanding while using specialized machine learning models for the actual prediction task.

Why aren't you releasing the code?

We've made the decision not to release our source code for several reasons:

• To prevent misuse by college counseling services that might exploit the system for commercial gain.
• To ensure that the tool is used as intended - as an educational resource rather than a definitive predictor of college admissions outcomes.

Where is the paper?

We are currently in the process of writing a comprehensive paper detailing our methodology, results, and findings. We anticipate releasing this paper in the coming months. The paper will provide in-depth insights into our approach, including:

• Detailed explanation of our data collection and preprocessing methods
• In-depth analysis of our feature engineering techniques
• Comprehensive overview of our model architecture and training process
• Thorough evaluation of our model's performance and limitations
• Discussion of ethical considerations and potential impacts on college admissions

We're excited to share our findings with the academic community and contribute to the ongoing discussion about predictive models in education.

Interested in sponsoring us?

If you're interested in sponsoring our project or collaborating with us, please reach out to John Tian at [email protected]. We're always open to partnerships that can help us improve our tool and make it more accessible to students worldwide.

Demographics

• Gender: Male
• Race/Ethnicity: Asian
• Residence: Competitive East Coast area
• Income Bracket: $500k+
• Type of School: Competitive public
• Hooks: First-Gen

Academics

• GPA (UW/W): 4.0
• Rank (or percentile): 2/~550
• # of Honors/AP/IB/Dual Enrollment/etc.: 19 APs
• Senior Year Course Load: Multivariable Calc, Differential equations, Linear algebra, Real Analysis, AP PhysicsE&M, AP Lang, AP Gov, Choir, Ap Psych, AP Spanish

Standardized Testing

• SAT I: 1580 (800 Math)
• ACT: 36
• AP/IB: thirteen 5's and one 4 at time of application (submitted all tests to colleges)

Extracurriculars/Activities

1. Computational Bio Research with prof - three years, published in reputed journal, presented at conferences
2. Nonprofit helping underprivileged kids in STEM across three countries - high impact, multiple awards
3. Research summer program - competitive, no cost
4. Self-conducted math research - published in math journal, presented at T50 university
5. CS internship - 2 years, paid, building AI models for social causes
6. Cultural singer - teaching younger kids, performing at festivals
7. Math club president - organized competitions, taught Olympiad topics
8. Medical device research - designed prototype, filed for patent, donated devices
9. Congressional intern - lobbied for state legislation to improve STEM education
10. Homeless shelter volunteer - regular volunteer

Awards/Honors

• 2 well-known national awards for nonprofit
• USAMO (2x), USAJMO (1x) qualifier
• 1st place state science fair + ISEF
• 3 state level + 1 national hackathon winner
• Best student researcher award from university bio department