train_model.py

import xgboost as xgb
import pandas as pd
import pickle
from sklearn.model_selection import train_test_split

data = {
"study_hours": [1,2,3,4,5,6,7,8,2,3,5,6,7,1,4],
"attendance": [50,55,60,65,70,75,80,85,60,65,75,80,85,45,68],
"assignments_completed": [1,1,2,2,3,3,4,4,2,2,3,3,4,1,2],
"previous_marks": [30,35,40,45,50,55,60,65,42,48,53,58,62,28,47],
"pass": [0,0,0,0,1,1,1,1,0,0,1,1,1,0,0]
}

df = pd.DataFrame(data)

X = df.drop("pass", axis=1)
y = df["pass"]

X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)

model = xgb.XGBClassifier(
objective="binary:logistic",
eval_metric="logloss",
use_label_encoder=False
)

model.fit(X_train, y_train)
with open("model.pkl", "wb") as f:
    pickle.dump(model, f)

print("Model saved as model.pkl")