Code Lab — Remesys AI Lab

# scikit-learn — Machine Learning
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

# Generate dataset
X, y = make_classification(n_samples=500, n_features=10,
                            n_informative=6, random_state=42)

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

# Preprocess
scaler = StandardScaler()
X_train_s = scaler.fit_transform(X_train)
X_test_s  = scaler.transform(X_test)

# Train & evaluate both models
for name, clf in [
    ("Logistic Regression", LogisticRegression()),
    ("Random Forest",       RandomForestClassifier(n_estimators=50, random_state=42))
]:
    clf.fit(X_train_s, y_train)
    acc = accuracy_score(y_test, clf.predict(X_test_s))
    cv  = cross_val_score(clf, X_train_s, y_train, cv=5).mean()
    print(f"{name}: Test Acc={acc*100:.1f}%  CV={cv*100:.1f}%")

OUTPUT Ready

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