pavithraams/student-pass-fail-model

Student Pass/Fail Prediction (Open Elective Project)

Decision Tree trained on our own student dataset (1000 rows), then fine-tuned with GridSearchCV.

Model	Test Accuracy
Base Decision Tree	74.50%
Fine-tuned Decision Tree	78.00%

Best parameters: {'criterion': 'entropy', 'max_depth': 8, 'min_samples_leaf': 10, 'min_samples_split': 2}

Features: study_hours, attendance, sleep_hours, previous_marks, assignments_done

Usage

import joblib, pandas as pd
from huggingface_hub import hf_hub_download
model = joblib.load(hf_hub_download("pavithraams/student-pass-fail-model", "model.joblib"))
x = pd.DataFrame([{"study_hours": 7, "attendance": 85, "sleep_hours": 7.5, "previous_marks": 75, "assignments_done": 8}])
print("Pass" if model.predict(x)[0] == 1 else "Fail")