| --- |
| language: |
| - en |
| tags: |
| - xgboost |
| - regression |
| - tabular |
| - education |
| - test-scores |
| license: mit |
| datasets: |
| - synthetic |
| model-index: |
| - name: test-score-predictor |
| results: |
| - task: |
| type: regression |
| name: Regression |
| dataset: |
| name: Synthetic Test Score Dataset |
| type: tabular |
| metrics: |
| - type: mean_squared_error |
| value: 38.25 |
| - type: r2 |
| value: 0.79 |
| --- |
| |
| # Test Score Predictor (XGBoost) |
|
|
| This model predicts **final test scores** for students based on previous performance and study habits. |
| It was trained on a **synthetic dataset of 1,000 rows** generated to reflect average realism (balanced distribution of student profiles). |
|
|
| --- |
|
|
| ## 📊 Input Features |
|
|
| The model expects the following features: |
|
|
| - **`previous_test_score`** → Student’s most recent test score (0–100) |
| - **`motivation_level`** → Self-reported motivation (1–10) |
| - **`self_confidence`** → Confidence in academic ability (1–10) |
| - **`study_environment_quality`** → Quality of study environment (1–10, quiet & focused = higher) |
| - **`time_management_skill`** → Time management ability (1–10) |
| - **`last_minute_cram_hours`** → Hours crammed the night before the test (0–12) |
| |
| --- |
| |
| ## 🎯 Output |
| |
| - **Predicted final test score** (0–100) |
| - Can also be mapped to a **letter grade (A–F)** |
| |
| | Score Range | Grade | |
| |-------------|-------| |
| | 90–100 | A | |
| | 80–89 | B | |
| | 70–79 | C | |
| | 60–69 | D | |
| | < 60 | F | |
| |
| --- |
| |
| ## 🛠️ Usage |
| |
| ### 1. Install dependencies |
| |
| ```bash |
| pip install xgboost scikit-learn pandas joblib huggingface_hub |
| import joblib |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| # Download model file from Hugging Face repo |
| model_path = hf_hub_download( |
| repo_id="mjpsm/test-score-predictor", |
| filename="xgb_test_score_model.pkl" |
| ) |
| |
| # Load the model |
| model = joblib.load(model_path) |
| |
| # Example student |
| student = pd.DataFrame([{ |
| "previous_test_score": 72, |
| "motivation_level": 8, |
| "self_confidence": 7, |
| "study_environment_quality": 6, |
| "time_management_skill": 5, |
| "last_minute_cram_hours": 3 |
| }]) |
| |
| # Predict final score |
| prediction = model.predict(student)[0] |
| print(f"Predicted final test score: {prediction:.2f}") |
| ``` |
| |
| ## 📈 Training |
| - Algorithm: XGBoost Regressor |
| - Dataset: 1,000 rows synthetic (realistic student performance simulation) |
| |
| Metrics on test set: |
| - MSE: ~38.25 |
| - R²: ~0.79 |
| |
| ## 📌 Notes |
| - Predictions are continuous values, so results may slightly exceed 100 — clamp to [0, 100] if needed. |
| - The dataset reflects average realism: some students improve, some decline, depending on habits and prior scores. |
