Can somebody please help me out with the "Bob The Builder" question. Here's my code:

Question

import numpy as np

from sklearn.linear_model import LinearRegression
model=LinearRegression()
n = int(input())
X = []
for i in range(n):
X.append([float(x) for x in input().split()])
y = [int(x) for x in input().split()]
datapoint = [float(x) for x in input().split()]
y_array=np.asarray(y)
datapoint_array=np.asarray(datapoint)
datapoint_array.reshape(-1,1)
y_array.reshape(-1,1)
model.fit(datapoint_array,y_array)
print(model.predict(datapoint_array))

Answer 1

Well, can you please give an explanation of your own code...

Answer 2

from sklearn.linear_model import LinearRegression
model=LinearRegression()
n = int(input())
X = []
for i in range(n):
X.append([float(x) for x in input().split()])
y = [int(x) for x in input().split()]
datapoint = [float(x) for x in input().split()]
model.fit(X,y)
print(int(model.predict([datapoint])))
'''See I did these changes but can't understand why I can't get the test case 3 and 6 incorrect.'''

Answer 3

I mean *correct

Answer 4

model.fit(X,y)
print(model.predict(datapoint )[0])

Answer 5

You have to output a single value not list. Otherwise sololearn will not accept.

For example "[9]" will be invalid but "9" will be okey. so print only first element using [0].

using numpy is not neccessary here. so you can safely delete datapoint_array and y_array.

Answer 6

fix your last statement like this. do not use int()
print(model.predict(datapoint )[0])

Answer 7

Okay, surely. Thnx very much...

Answer 8

you can solve this using Logistic regression model, you will get pass the test case correctly