Aditya

            
            
            
            
            
            
            
            
            
            
            
            
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

def train_model(df):
    # Feature engineering
    X = df.drop('target', axis=1)
    y = df['target']
    
    # 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 = RandomForestClassifier(n_estimators=100)
    model.fit(X_train, y_train)
    
    # Evaluate
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    
    return model, accuracy