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## titanic.ipynb

      
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                codistwa
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              Created
              May 25, 2023 14:10
            
          
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## fraud-detection.ipynb

      
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                codistwa
                / fraud-detection.ipynb
            
            
              Created
              December 21, 2022 16:02
            
              
                Machine Learning Project : Credit Card Fraud Detection
              
          
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## variables.js
// ============================================================
// Definition of a variable
// ============================================================

const welcome = 'Hello';
console.log(typeof(welcome)); // string

// ============================================================
// Types
// ============================================================

## sets.py
# ============================================================
# Definition
# ============================================================

set([0, 1, 2, 3])

# ============================================================
# Add an item
# ============================================================

## recursion.js
// ============================================================
// Recursion with one parameter
// ============================================================

const factorial = (num) => {
   if (num === 1) {
      return 1;
   }

  return num * factorial(num - 1);

## loops.c
// ============================================================
// Simple for loop
// ============================================================

int fruits [3] = {1, 2, 3};

for (int i = 0; i < 3; i++)
  printf("%d\n", fruits[i] * 2);

// ============================================================

## logarithm.py
# ============================================================
# The 3 types of logarithms
# ============================================================

x = np.linspace(-3,3,100)

ln = np.log(x)
decimal = np.log10(x)
binary = np.log2(x)

## linear-algebra.py
# ============================================================
# What is a vector?
# ============================================================

scalar = [3] # scalar
row = np.array([2,1], dtype=object) # row matrice / vector
col = np.array([ [3], [-1] ], dtype=object) # column matrice / vector

display(Math(sym.latex(sym.sympify(scalar))))
display(Math(sym.latex(sym.sympify(row))))

## functions-in-math.py
# ============================================================
# Definition of fonction
# ============================================================

def area(x):
    return 2 * x

print(area(4))

x = np.linspace(-6,6)

## functions.js
// ============================================================
// Without parameter
// ============================================================

function double () {
  console.log('Hello world')
}

console.log(double()) // Hello world
	// ============================================================
	// Definition of a variable
	// ============================================================

	const welcome = 'Hello';
	console.log(typeof(welcome)); // string

	// ============================================================
	// Types
	// ============================================================
	# ============================================================
	# Definition
	# ============================================================

	set([0, 1, 2, 3])

	# ============================================================
	# Add an item
	# ============================================================
	// ============================================================
	// Recursion with one parameter
	// ============================================================

	const factorial = (num) => {
	if (num === 1) {
	return 1;
	}

	return num * factorial(num - 1);
	// ============================================================
	// Simple for loop
	// ============================================================

	int fruits [3] = {1, 2, 3};

	for (int i = 0; i < 3; i++)
	printf("%d\n", fruits[i] * 2);

	// ============================================================
	# ============================================================
	# The 3 types of logarithms
	# ============================================================

	x = np.linspace(-3,3,100)

	ln = np.log(x)
	decimal = np.log10(x)
	binary = np.log2(x)
	# ============================================================
	# What is a vector?
	# ============================================================

	scalar = [3] # scalar
	row = np.array([2,1], dtype=object) # row matrice / vector
	col = np.array([ [3], [-1] ], dtype=object) # column matrice / vector

	display(Math(sym.latex(sym.sympify(scalar))))
	display(Math(sym.latex(sym.sympify(row))))
	# ============================================================
	# Definition of fonction
	# ============================================================

	def area(x):
	return 2 * x

	print(area(4))

	x = np.linspace(-6,6)
	// ============================================================
	// Without parameter
	// ============================================================

	function double () {
	console.log('Hello world')
	}

	console.log(double()) // Hello world