<jupyter_start><jupyter_text>Fonctions importants en machine learning<jupyter_code>x =  -5
y = abs(x)
y
a =3.14
round(a)
liste =[1, 6, -9, 12, -21]
max(liste)
min(liste)
sum(liste)
len(liste)
str(x)
type(x)
int(str(x))
float(x)
bin(x)
hex(x)
oct(x)
entree = input()
entree = int(entree)
transport = 200
somme = entree + transport
somme
s = 1998
l ='Kaolack'
naissance = '<NAME> est né à {} en {}'.format(s,l)
print(naissance)
s = 1998
l ='Kaolack'
naissance = f'<NAME> est né à {s} en {l}'
print(naissance)
with open('fichier.txt' , 'w') as file :
    file.write('Michel Nassalang')
with open('fichier.txt' , 'r') as file :
    r= file.read()
    print(r)<jupyter_output><NAME>
{'fraction_non_alphanumeric': 0.1831610044313146, 'fraction_numerical': 0.03249630723781388, 'mean_word_length': 4.794871794871795, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '209629', 'license': 'Apache-2.0', 'n_tokens_mistral': 325, 'n_tokens_neox': 303, 'n_words': 79, 'path': 'fonctions_machine_learning.ipynb', 'repo_name': 'Michel-Nassalang/python', 'size': '707'}