<jupyter_start><jupyter_text><jupyter_code>import os
import pandas as pd

os.listdir('sample_data')
pd = pd.read_csv('sample_data/diabetes.csv')
pd.head()

pd.info()
# memisahkan atribut pada dataset dan menyimpannya pada sebuah variabel
X = pd[pd.columns[:8]]

# memisahkan label pada dataset dan menyimpan nya pada sebuah variabel 
y = pd['Outcome']


from sklearn.preprocessing import StandardScaler


# standarisasi nilai-nilai dari dataset
scaler = StandardScaler()
scaler.fit(X)
X = scaler.transform(X)
from sklearn.model_selection import train_test_split

X_train , X_test , y_train,y_test = train_test_split(X,y,test_size=0.33, random_state=42)
from sklearn.svm import SVC


clf = SVC()
clf.fit(X_train, y_train)
clf.score(X_test,y_test)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10451612903225807, 'fraction_numerical': 0.007741935483870968, 'mean_word_length': 6.461538461538462, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '405003', 'license': 'MIT', 'n_tokens_mistral': 312, 'n_tokens_neox': 303, 'n_words': 65, 'path': 'TrainSVM.ipynb', 'repo_name': 'ibnuhajar/TrainingMachineLearning', 'size': '775'}