Create README.md

README.md

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+# 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
+import joblib
+import pandas as pd
+
+# Load the trained model
+model = joblib.load("xgb_test_score_model.pkl")
+
+# 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}")
+```