Learning neural network and saving the result - python-3.x

Question

I think this is a simple question, but not for me( There is a table in df:
Date X1 X2 Y1
07.02.2019 5 1 1
08.02.2019 6 2 1
09.02.2019 1 3 0
10.02.2019 4 4 1
11.02.2019 1 1 0
12.02.2019 4 2 1
13.02.2019 5 5 1
14.02.2019 6 5 1
15.02.2019 1 1 0
16.02.2019 4 5 1
17.02.2019 1 2 0
18.02.2019 1 1
19.02.2019 2 1
20.02.2019 3 2
21.02.2019 4 14
I need to build a neural network for Y1 from the parameters X1 and X2 and then apply it to the lines with a date greater than 17.02.2019, And save the network prediction result in a separate df2
import pandas as pd
import numpy as np
import re
from sklearn.neural_network import MLPClassifier
df = pd.read_csv("ob.csv", encoding = 'cp1251', sep = ';')
df['Date'] = pd.to_datetime(df['Date'], format='%d.%m.%Y')
startdate = pd.to_datetime('2019-02-17')
X = ['X1', 'X2'] ????
y = ['Y1'] ????
clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
clf.fit(x, y)
clf.predict(???????) ????? df2 = ????
Where ???? - I do not know how to set the conditions correctly

Answer 1

import pandas as pd
import numpy as np
import re
from sklearn.neural_network import MLPClassifier
df = pd.read_csv("ob.csv", encoding = 'cp1251', sep = ';')
df['Date'] = pd.to_datetime(df['Date'], format='%d.%m.%Y')
startdate = pd.to_datetime('2019-02-17')
train = df[df['Date'] <= '2019-02-17']
test = df[df['Date'] > '2019-02-17']
X_train = train[['X1', 'X2']]
y_train = train[['Y1']]
X_test = test[['X1', 'X2']]
y_test = test[['Y1']]
clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
clf.fit(X_train, y_train)
df2 = pd.DataFrame(clf.predict(X_test))
df2.to_csv('prediction.csv')