<jupyter_start><jupyter_text>survived,pclass,age,sibsp,parch,fare<jupyter_code>x = df[['pclass','sibsp','parch','fare']]
y = df['survived']
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(x,y)
X_train.shape ,X_test.shape ,Y_train.shape ,Y_test.shape 
from sklearn.linear_model import LogisticRegression
logi_r = LogisticRegression()
logi_r.fit(X_train,Y_train)
logi_r.classes_,logi_r.coef_,logi_r.intercept_
logi_r.score(x,y)
logi_r.predict(X_train)<jupyter_output><empty_output><jupyter_text>logi_r.predict_proba(X_train)<jupyter_code>logi_r.predict_proba(X_train)
print('Hello')
from sklearn import metrics
metrics.confusion_matrics(X_train,Y_train)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12601626016260162, 'fraction_numerical': 0.0, 'mean_word_length': 12.943396226415095, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '63176', 'license': 'Apache-2.0', 'n_tokens_mistral': 317, 'n_tokens_neox': 313, 'n_words': 33, 'path': 'titanic_classification.ipynb', 'repo_name': 'rcm2000/test_machinelearning', 'size': '738'}