Code, code, code! Asking ChatGPT to handle the heavy lifting.

“The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.” - Albert Einstein
“It is our choices, Harry, that show what we truly are, far more than our abilities.” - J.K. Rowling
“There are only two ways to live your life. One is as though nothing is a miracle. The other is as though everything is a miracle.” - Albert Einstein
“The person, be it gentleman or lady, who has not pleasure in a good novel, must be intolerably stupid.” - Jane Austen
“Imperfection is beauty, madness is genius and it's better to be absolutely ridiculous than absolutely boring.” - Marilyn Monroe
“Try not to become a man of success. Rather become a man of value.” - Albert Einstein
“It is better to be hated for what you are than to be loved for what you are not.” - André Gide
“I have not failed. I've just found 10,000 ways that won't work.” - Thomas A. Edison
“A woman is like a tea bag; you never know how strong it is until it's in hot water.” - Eleanor Roosevelt
“A day without sunshine is like, you know, night.” - Steve Martin

import numpy as np
import matplotlib.pyplot as plt

# Generate random data
data = np.random.rand(100, 2)

# Number of clusters
k = 3

# Number of training data
n = data.shape[0]

# Number of features in the data
c = data.shape[1]

# Generate random centers for the clusters
mean = np.mean(data, axis = 0)
std = np.std(data, axis = 0)
centers = np.random.randn(k,c)*std + mean

# Plot the data and the centers generated as random
plt.scatter(data[:,0], data[:,1], s=7)
plt.scatter(centers[:,0], centers[:,1], marker='*', c='g', s=150)

# Function to calculate distance between two points
def dist(a, b, ax=1):
    return np.linalg.norm(a - b, axis=ax)

# Loop for the number of iterations
for i in range(10):
    # Assign each value to its closest center
    clusters = np.zeros(n)
    for i in range(n):
        distances = dist(data[i], centers)
        cluster = np.argmin(distances)
        clusters[i] = cluster
        
    # Store the old centers
    centers_old = np.copy(centers)
    
    # Find the new centers
    for i in range(k):
        points = [data[j] for j in range(n) if clusters[j] == i]
        centers[i] = np.mean(points, axis=0)
        
    # Calculate the distance between the old and the new centers
    error = dist(centers, centers_old, None)
    
    # If the distance is zero, the clusters are optimal
    if error == 0:
        break

import csv
import random

# Set the number of rows in the dataset
num_rows = 1000

# Set the probability that a given row will be a survivor
survival_prob = 0.5

# Set the column names
columns = ['passenger_id', 'survived', 'class', 'name', 'gender', 'age', 'siblings_spouses', 'parents_children', 'fare']

# Initialize an empty list to store the rows
rows = []

# Add rows to the list
for i in range(num_rows):
  passenger_id = i
  survived = random.random() < survival_prob
  passenger_class = random.randint(1, 3)
  name = f'Person {i}'
  gender = random.choice(['male', 'female'])
  age = random.randint(0, 100)
  siblings_spouses = random.randint(0, 8)
  parents_children = random.randint(0, 8)
  fare = random.uniform(0, 500)
  rows.append([passenger_id, survived, passenger_class, name, gender, age, siblings_spouses, parents_children, fare])

# Write the rows to a CSV file
with open('titanic_dataset.csv', 'w') as f:
  writer = csv.writer(f)
  writer.writerow(columns)
  writer.writerows(rows)