import matplotlib.pyplot as plt
import pandas as pd
from sklearn.linear_model import LogisticRegression
import numpy as np
dataset = pandas.DataFrame(Demand, Date, Temperature)
dataset['Date'] = pd.to_datetime(dataset['Date'])
dataset.set_index('Date', inplace=True)
dataset = dataset.resample('W').sum()#.ffill()
dataset.index.freq = 'W'
train, test = dataset.iloc[:300, 0], dataset.iloc[300:, 0]
#X_train = train.drop('Demand', axis=1)
#X_test = test.drop('Demand', axis=1)
#y_train = train.Demand
#y_test = test.Demand
model = LogisticRegression()
model.fit(train.drop('Demand', axis=1), train.Demand)
Хорошо, я решил это, удалив [0] в
train, test = dataset.iloc[:300, 0], dataset.iloc[300:,0]
в
train, test = dataset.iloc[:300], dataset.iloc[300:]