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from sklearn.linear_model import LinearRegression

from sklearn.linear_model import LinearRegression

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

dataf = pd.read_csv('../input/bike_share.csv')

dataf.describe().T

dataf.duplicated().sum()

dataf.shape

dataf.drop_duplicates(inplace=True)

dataf.duplicated().sum()

dataf.isna().sum()

dataf.corr()

train_X = dataf.drop(columns=['temp', 'casual', 'registered'])

train1_Y = dataf['casual']
train2_Y = dataf['registered']
train3_Y = dataf['count']

model = LinearRegression()

model.fit(train_X, train1_Y)
dtrain_predictions = model.predict(train_X)

model.fit(X_train, Y_train)