LR on sklearn

lr.py

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

X = []
y = []
lines = open('data.csv').readlines()
for line in lines[1:]:
    line = line.strip()
    if not line:
        continue
    id, _y, _x1, _x2 = line.split(',')
    X.append([int(_x1), int(_x2)])
    y.append(int(_y))

X_train, y_train = X, y
X_test, y_test = X, y
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = LogisticRegression(max_iter=50)
model.fit(X_train, y_train)

score = model.score(X_test, y_test)
print(score)

print(dir(model))
print(model.coef_)
print(model.intercept_)
print(model.max_iter)
print(model.n_iter_)


print('--------------------')

y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))


print('------------')
xx = (21, 52)
print(model.predict([xx]))
print(model.predict_proba([xx]))