کد تصویر بالا

# Import necessary libraries
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
import matplotlib.pyplot as plt
import numpy as np

# Create a 2D classification dataset with 2 informative features
X, y = make_classification(
    n_samples=200,
    n_features=2,
    n_redundant=0,
    n_informative=2,
    n_clusters_per_class=1,
    random_state=42
)

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

# Train a Gaussian Naive Bayes classifier
model = GaussianNB()
model.fit(X_train, y_train)

# Create a mesh grid for visualization of decision boundary
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(
    np.linspace(x_min, x_max, 500),
    np.linspace(y_min, y_max, 500)
)

# Predict class labels for each point in the mesh grid
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

# Plot the decision boundary and data points
plt.figure(figsize=(8, 6))
plt.contourf(xx, yy, Z, alpha=0.3)  # Background classification regions
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k')  # Original data points
plt.noscript("Naive Bayes Classification (GaussianNB)")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.tight_layout()
plt.show()

در ادامه کد تصویر بالا رو قرار داده ام می توانید کپی کنید تغییر بدید و استفاده کنید.

# Import required libraries
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
import matplotlib.pyplot as plt
import numpy as np

# Create a non-linear 2D dataset shaped like moons
X, y = make_moons(n_samples=300, noise=0.2, random_state=42)

# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

# Initialize a neural network model with 2 hidden layers (10 and 5 neurons)
mlp = MLPClassifier(hidden_layer_sizes=(10, 5), max_iter=2000, random_state=42)

# Train the model using the training data
mlp.fit(X_train, y_train)

# Create a mesh grid to visualize decision boundaries
x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
xx, yy = np.meshgrid(
    np.linspace(x_min, x_max, 500),
    np.linspace(y_min, y_max, 500)
)

# Predict the class for each point in the mesh grid
Z = mlp.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

# Plot the decision boundary and original data points
plt.figure(figsize=(8, 6))
plt.contourf(xx, yy, Z, alpha=0.3)  # decision regions
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', s=50)  # input points
plt.noscript("Neural Network Classification (MLPClassifier)")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Define tasks with their optimistic, most likely, and pessimistic durations (in days)
tasks = [
    {"name": "Requirements Analysis", "optimistic": 2, "most_likely": 4, "pessimistic": 6},
    {"name": "Software Design", "optimistic": 3, "most_likely": 5, "pessimistic": 9},
    {"name": "Implementation", "optimistic": 5, "most_likely": 10, "pessimistic": 15},
    {"name": "Testing & Review", "optimistic": 2, "most_likely": 3, "pessimistic": 5}
]

# Number of Monte Carlo simulations
n_simulations = 10000
project_durations = []

# Run the Monte Carlo simulation
for _ in range(n_simulations):
    total_duration = 0
    for task in tasks:
        o = task["optimistic"]
        m = task["most_likely"]
        p = task["pessimistic"]
        # Generate a duration using triangular distribution
        duration = np.random.triangular(o, m, p)
        total_duration += duration
    project_durations.append(total_duration)

# Calculate probability of finishing under 25 days
deadline = 25
prob = np.mean(np.array(project_durations) <= deadline)

# Plot the histogram
plt.figure(figsize=(10, 6))
plt.hist(project_durations, bins=50, color="#336699", alpha=0.85, edgecolor='black')
plt.noscript("Monte Carlo Simulation – Estimated Project Duration (10K Runs)", fontsize=14)
plt.xlabel("Project Duration (days)")
plt.ylabel("Frequency")
plt.grid(True)
plt.axvline(x=deadline, color='red', linestyle='--',
            label=f'Deadline = {deadline} days\nP ≈ {prob*100:.2f}%')
plt.legend()
plt.tight_layout()
plt.show()

✅💡تصویر بالا نتیجه خروجی کد است.

📌 خروجی چی می‌ده؟

یه نمودار هیستوگرام که نشون می‌ده پراکندگی زمان تحویل پروژه چطوریه

عددی که احتمال رسیدن به ددلاین ۲۵ روزه رو می‌گه
با افزایش تعداد شبیه‌سازی (مثلاً 100,000)، دقت بالاتر می‌ره

# Creating a list
fruits = ["apple", "banana", "cherry"]
print(fruits)  # Output: ['apple', 'banana', 'cherry']

# Accessing elements by index
print(fruits[1])  # Output: banana

# Changing an element
fruits[0] = "grape"
print(fruits)  # Output: ['grape', 'banana', 'cherry']

# Adding an item
fruits.append("orange")
print(fruits)  # Output: ['grape', 'banana', 'cherry', 'orange']

# Removing an item
fruits.remove("banana")
print(fruits)  # Output: ['grape', 'cherry', 'orange']

# Looping through a list
for fruit in fruits:
    print(fruit)  # Output: grape \n cherry \n orange

# Checking membership
print("cherry" in fruits)  # Output: True

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