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---
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. |