Piyush090805/college-admit-predictor

🎓 College Admission Predictor (RandomForest)

This model predicts college admission chances based on GRE, TOEFL, CGPA, SOP, LOR, University Rating, and Research Experience.
Trained on a synthetic + processed dataset, optimized to achieve ~97% accuracy.

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📊 Results

Metric	Score
Accuracy	97%
Precision	0.93
Recall	0.92

Confusion Matrix

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📂 Files in this repo

• rf_best_test_clean.pkl → Trained RandomForest model
• test_clean_with_label.csv → Processed dataset with labels
• README.md → Model card
• LICENSE → Apache 2.0 License

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🚀 How to Use

from huggingface_hub import hf_hub_download
import joblib
import pandas as pd

# Download model
model_path = hf_hub_download(repo_id="your-username/college-admit-predictor", filename="rf_best_test_clean.pkl")

# Load model
model = joblib.load(model_path)

# Example prediction
sample = pd.DataFrame([{
    "gre": 325, "toefl": 112, "uni_rating": 4,
    "sop": 4.5, "lor": 4.0, "cgpa": 9.0, "research": 1,
    "academic_score": 2.5, "gre_toefl": 36400, "cgpa_times_rating": 36.0
}])

print(model.predict(sample))