DeepSeek vs. ChatGPT vs. Gemini: Which AI Model is Right for You?

Now, if you flip two coins together in a normal computer, they behave independently. But in quantum computing, the two coins can be entangled, meaning their outcomes are linked no matter how far apart they are.

These two principles—superposition and entanglement—allow quantum computers to solve complex problems much faster than regular computers, making them useful for things like secure encryption, simulations, and artificial intelligence.

Another key feature is entanglement, where qubits become interconnected, allowing for faster problem-solving.

Quantum computers have the potential to break encryption, optimize logistics, and simulate complex molecules for medical research. While still in early development, they promise to revolutionize industries by solving problems that classical computers struggle with.

def is_prime(n):
    if n < 2:
        return False
    for i in range(2, int(n ** 0.5) + 1):
        if n % i == 0:
            return False
    return True

print(is_prime(17))  # True
print(is_prime(18))  # False
            

def is_prime(n):
    """Check if a number is prime."""
    if n < 2:
        return False
    for i in range(2, int(n ** 0.5) + 1):
        if n % i == 0:
            return False
    return True

# Example usage
num = 29
if is_prime(num):
    print(f"{num} is a prime number.")
else:
    print(f"{num} is not a prime number.")
            

def is_prime(n):
    """Returns True if n is a prime number, """
"""otherwise False."""
    if n < 2:
        return False
    if n in (2, 3):
        return True
    if n % 2 == 0 or n % 3 == 0:
        return False
    i = 5
    while i * i <= n:
        if n % i == 0 or n % (i + 2) == 0:
            return False
        i += 6
    return True

# Test cases
print(is_prime(11))  # True
print(is_prime(25))  # False
            

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load dataset
iris = load_iris()
X, y = iris.data, iris.target

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

# Train model
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

# Evaluate model
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

# Standardize the features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Train a Logistic Regression model
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate the model
print("Model Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))
print("\nConfusion Matrix:\n", confusion_matrix(y_test, y_pred))

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import seaborn as sns

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target
feature_names = iris.feature_names
target_names = iris.target_names

# Convert to DataFrame for better visualization
df = pd.DataFrame(X, columns=feature_names)
df['target'] = y

# Data visualization
plt.figure(figsize=(8, 6))
sns.pairplot(df, hue="target", palette="coolwarm")
plt.show()

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

# Standardize the features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Train Logistic Regression model
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate model
print("Model Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))
print("\nConfusion Matrix:")
sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, cmap="coolwarm", fmt="d")
plt.show()