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+ 2

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

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))

8 Answers

+ 1

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

+ 1

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.'''

+ 1

I mean *correct

0

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

0

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.

0

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

0

Okay, surely. Thnx very much...

0

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