from functools import lru_cache

@lru_cache(maxsize=10)
def add(a, b):
    print('add method called')
    return a+b

print(add(7, 4))

add method called
11

print(add(7, 4))

11

print(add(5, 2))

add method called …

def square(a):
    return a * a

print(square(5))

25

def square(a:int) -> int:
    return a * a

print(square(4))

16

def square_1(a:int) -> str:
    return a * a

print(square_1(15))

225 …

from fuzzywuzzy import fuzz

print(fuzz.ratio("this is a test", "this is a test!"))

97

print(fuzz.ratio("this is a cup", "this is a world cup"))

81

from sklearn.naive_bayes import GaussianNB
import numpy as np

#assigning predictor and target variables
x= np.array([[-3,7],[1,5], [1,2], [-2,0], [2,3], [-4,0], [-1,1], [1,1], [-2,2], [2,7], [-4,1 …

from sklearn.naive_bayes import GaussianNB
import numpy as np

#assigning predictor and target variables
x= np.array([[-3,7],[1,5], [1,2], [-2,0], [2,3], [-4,0], [-1,1], [1,1], [-2,2], [2,7], [-4,1], [-2,7]])
y = np.array([3, 3, 3, 3, 4, 3 …

!python --version

Python 3.12.4

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

# Load dataset
data = load_breast_cancer()

type(data)

sklearn.utils._bunch.Bunch

# Organize our data
label_names = data['target_names']
labels = data['target']
feature_names = data['feature_names']
features = data …

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

# Load dataset
data = load_breast_cancer()

type(data)

sklearn.utils.Bunch

# Organize our data
label_names = data['target_names']
labels …

from sklearn import datasets
from sklearn.naive_bayes import GaussianNB

iris = datasets.load_iris()

gnb = GaussianNB()
y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)

print("Number of mislabeled points out of a total %d points : %d" % (iris …

import nltk

tokenizer = nltk.tokenize.RegexpTokenizer(r'\w+|[^\w\s]+')

content_text = """They’re interested in local government, free TV licences, pension credits and child 
trust fund, Carrousel Capital, run by local Liberal Democrats. TV Exclusive Trouser 
Clegg Nick …

abc = [6, 8, 23, 2, 19]

abc

[6, 8, 23, 2, 19]

result = [x for x in abc if(x > 6)]

result

[8, 23, 19]

square = [x*x for x in result]

square

[64, 529, 361]

# More …