Update README.md

README.md

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----
-license: mit
----
+---
+language:
+- en
+license: apache-2.0
+tags:
+- text-classification
+- student-stress
+- mental-health
+- education
+- tensorflow
+- keras
+pipeline_tag: text-classification
+metrics:
+- accuracy
+- f1
+---
+
+# Student Stress Prediction Model
+
+## Model Summary
+
+This model predicts stress levels in students based on behavioral, academic, and psychological indicators. It was developed as part of research into the implications of AI on student stress — supporting a forthcoming peer-reviewed publication on the topic.
+
+The model is built with TensorFlow/Keras and is designed for binary or multi-class classification of student stress levels from structured survey/feature data.
+
+---
+
+## Model Details
+
+- **Developed by:** Chandrasekar Adhithya Pasumarthi ([@Adhithpasu](https://github.com/Adhithpasu))
+- **Affiliation:** Frisco ISD, TX | AI Club Leader | Class of 2027
+- **Model type:** Deep Neural Network (TensorFlow/Keras)
+- **Language:** English
+- **License:** Apache 2.0
+- **Related research:** *"AI's Implications on Student Stress Levels"* — forthcoming publication (expected April 2026)
+- **Related prior work:** *"Comparing Vision Transformers and Convolutional Neural Networks: A Systematic Analysis"*, JCSTS Vol. 8(2), January 2026
+
+---
+
+## Intended Uses
+
+**Direct use:**
+- Predicting stress risk levels in students based on academic and behavioral features
+- Supporting school counselors or ed-tech platforms in early identification of at-risk students
+- Research into AI-driven mental health monitoring in educational settings
+
+**Out-of-scope use:**
+- Clinical diagnosis of mental health conditions
+- Use without informed consent from students
+- Deployment in high-stakes decision-making without human oversight
+
+---
+
+## Training Data
+
+The model was trained on a student stress dataset containing features such as:
+- Academic performance indicators (GPA, study hours, deadlines)
+- Behavioral signals (sleep patterns, extracurricular load)
+- Self-reported psychological indicators (anxiety levels, social pressure)
+
+> **Note:** Dataset details and preprocessing steps are documented in the associated research paper.
+
+---
+
+## Evaluation
+
+| Metric    | Value  |
+|-----------|--------|
+| Accuracy  | TBD    |
+| F1 Score  | TBD    |
+| Precision | TBD    |
+| Recall    | TBD    |
+
+*(Fill in with your actual results before publishing)*
+
+---
+
+## How to Use
+
+```python
+import tensorflow as tf
+import numpy as np
+
+# Load the model
+model = tf.keras.models.load_model("student_stress_model")
+
+# Example input — replace with your actual feature vector
+# Features: [study_hours, sleep_hours, gpa, extracurriculars, anxiety_score, ...]
+sample_input = np.array([[6.0, 5.5, 3.8, 3, 7]])
+
+# Predict
+prediction = model.predict(sample_input)
+stress_label = np.argmax(prediction, axis=1)
+
+labels = {0: "Low Stress", 1: "Moderate Stress", 2: "High Stress"}
+print(f"Predicted stress level: {labels[stress_label[0]]}")
+```
+
+---
+
+## Model Architecture
+
+```
+Input Layer  →  Dense(128, relu)  →  Dropout(0.3)
+             →  Dense(64, relu)   →  Dropout(0.2)
+             →  Dense(num_classes, softmax)
+```
+
+*(Update this section to match your actual architecture)*
+
+---
+
+## Limitations & Bias
+
+- The model is trained on a specific student population and may not generalize across different demographics, school systems, or cultures.
+- Self-reported features are subject to response bias.
+- Stress is a complex, multifactorial construct — model predictions should be treated as probabilistic indicators, not ground truth.
+
+---
+
+## Ethical Considerations
+
+Student mental health data is sensitive. This model is intended for research and educational tool development only. Any real-world deployment should:
+- Obtain explicit informed consent from students (and guardians for minors)
+- Be reviewed by a qualified mental health professional
+- Comply with FERPA and applicable data privacy regulations
+
+---
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{pasumarthi2026studentstress,
+  author    = {Chandrasekar Adhithya Pasumarthi},
+  title     = {Student Stress Prediction Model},
+  year      = {2026},
+  publisher = {Hugging Face},
+  url       = {https://huggingface.co/Adhithpasu/student-stress-prediction}
+}
+```
+
+---
+
+## Contact
+
+- GitHub: [@Adhithpasu](https://github.com/Adhithpasu)
+- Research: See linked publication for full methodology and dataset details.