Student_Performance_Prediction/model.py at main · Riya-Rani655/Student_Performance_Prediction · GitHub

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# ============================================
# Student Performance Prediction Model
# ============================================

import pandas as pd
import joblib

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# ============================================
# Load Dataset
# ============================================

df = pd.read_csv("Student_Performance.csv")

# ============================================
# Data Preprocessing
# ============================================

df["Extracurricular Activities"] = df["Extracurricular Activities"].map({
    "Yes": 1,
    "No": 0
})

# ============================================
# Features and Target
# ============================================

X = df[
    [
        "Hours Studied",
        "Previous Scores",
        "Extracurricular Activities",
        "Sleep Hours",
        "Sample Question Papers Practiced"
    ]
]

y = df["Performance Index"]

# ============================================
# Split Dataset
# ============================================

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

# ============================================
# Train Model
# ============================================

model = LinearRegression()

model.fit(X_train, y_train)

# ============================================
# Save Model
# ============================================

joblib.dump(model, "model.pkl")

print("✅ Model trained successfully!")
print("✅ model.pkl created successfully!")