def calculate_total(items): return sum(item.price * item.quantity for item in items)
SELECT users.name, COUNT(orders.id) FROM users LEFT JOIN orders ON users.id = orders.user_id GROUP BY users.id;
.hero-section { background: linear-gradient(135deg, #0a0a0a 0%, #1a1a1a 100%); }
<div className="container"><h1>Welcome to Innovation</h1></div>
# Machine learning model training with scikit-learn and tensorflow
UPDATE products SET stock = stock - 1 WHERE id = ? AND stock > 0;
const fetchData = async () => { const response = await fetch('/api/data'); return response.json(); }
<nav className="navbar"><ul><li><a href="#home">Home</a></li></ul></nav>
import pandas as pd; df = pd.read_csv('data.csv'); print(df.head())
@media (max-width: 768px) { .hero-title { font-size: 2.5rem; } }
CREATE TABLE users (id SERIAL PRIMARY KEY, email VARCHAR(255) UNIQUE, created_at TIMESTAMP);
class DatabaseManager: def __init__(self, connection_string): self.conn = connection_string
for i in range(len(data)): processed_data.append(transform(data[i]))
INSERT INTO analytics (user_id, event_type, timestamp) VALUES (?, ?, NOW());
.container { max-width: 1200px; margin: 0 auto; padding: 0 20px; }
<button onClick={handleSubmit} className="btn-primary">Submit</button>
try: result = api_call() except Exception as e: logger.error(f"API failed: {e}")
DELETE FROM sessions WHERE expires_at < NOW() AND user_id IS NOT NULL;
const [state, setState] = useState(initialState); useEffect(() => { fetchData(); }, []);
<form onSubmit={handleSubmit}><input type="email" required /></form>
npm install express mongoose cors dotenv bcryptjs jsonwebtoken
git add . && git commit -m "feat: implement user authentication system"
docker run -d -p 3000:3000 --name myapp-container myapp:latest
ALTER TABLE products ADD COLUMN category_id INT REFERENCES categories(id);
from sklearn.ensemble import RandomForestClassifier; model = RandomForestClassifier()
kubectl apply -f deployment.yaml && kubectl get pods
app.use(cors()); app.use(express.json()); app.listen(3000);
SELECT COUNT(*) FROM orders WHERE created_at >= CURRENT_DATE;
import tensorflow as tf; model = tf.keras.Sequential([tf.keras.layers.Dense(128)])
const handleClick = (e) => { e.preventDefault(); setLoading(true); }
pip install numpy matplotlib seaborn scikit-learn jupyter
function debounce(func, wait) { let timeout; return function executedFunction(...args) {
GRANT SELECT, INSERT, UPDATE ON database.* TO 'user'@'localhost';
export default function Component({ children, className }) { return (<div className={cn(className)}>{children}</div>) }
curl -X POST https://api.example.com/users -H "Content-Type: application/json" -d '{"name":"John"}'
const server = http.createServer((req, res) => { res.writeHead(200); res.end('Hello World'); });
db.collection('users').find({ status: 'active' }).toArray((err, docs) => { console.log(docs); });
FROM node:18-alpine AS builder WORKDIR /app COPY package*.json ./ RUN npm install
CREATE INDEX idx_product_name ON products (name);
const router = express.Router(); router.get('/users', (req, res) => { res.json([]); });
def train_model(data, labels): model.fit(data, labels); return model
console.log('Application started successfully on port 3000');
const app = express(); app.use(bodyParser.json());
SELECT AVG(price) FROM products WHERE category = 'electronics';
function validateEmail(email) { return /^[^\s@]+@[^\s@]+\.[^\s@]+$/.test(email); }