Python Machine Learning

Scikit-learn basics, model training, and ML patterns.

Overview

Learn ML fundamentals with Python.

Scikit-learn Basics

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Sample data
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
y = [0, 0, 1, 1, 1]

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

# Scale features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train_scaled, y_train)

# Predict
predictions = model.predict(X_test_scaled)
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2f}")

Practice

Build a classification model for a real-world dataset.