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from sklearn.linear_model import LogisticRegression

import pandas as pd

import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')

df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:].values.astype('int')
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X, y)
y_hat = clf.predict(X)