Machine Learning - Bob the builder task - How to make???

Question

Im stucked. Please help!!!

Task to build a LogisticRegression model.

My code looks working.
But anyway Im passing only 4 test. 5th and 6th - not passed. It is hidded. And I dont know whats wrong and how to fix.
Tried different things, but not successful.

Who has passed this? Pls give advice

import pandas as pd
from sklearn.linear_model import LogisticRegression

n = input()
n_split= n.split()
n_int = int(n_split[0])

X = []

for i in range(n_int):
first, second = input().split()
first= int(first)
second = int(second)
X.append([first,second])

y= []

for i in input().split():
i = int(i)
y.append(i)

x1,x2 = input().split()
x1,x2 = int(x1), int(x2)

x_pred = [x1, x2]

model = LogisticRegression()
model.fit(X,y)

print(model.predict([x_pred])[0])

Answer 1

import numpy as np
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()]
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X,y)
datapoint = np.array(datapoint).reshape(1,-1)
print(model.predict(datapoint[[0]])[0])

I got answer with this

Answer 2

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()]
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X, y)
Pr = model.predict([datapoint])
print(Pr[0])

Answer 3

Thank you guys for your codes. But is it possible to check where does mine code is wrong? How to improve it?

Answer 4

solved
i tried similar code as yours and had the same problem : test 1:2:3m4 would pass test 5 and 6 would fail .

i found the solution quite accidently.

i reset code and found that sololearn has modified the starting code.
they have introduced float instead of int
also they have introduced a loop to read x values

now the code works

Answer 5

Shubhi Srivastava

Did you know what inputs data?

Inputs, in this case, are points on the graph. So these points can be like (1.2, 3.1).
Do you get it?

Answer 6

Kindly Explain this Priyanshu mam , use comments to " highlight the workflow in the code"

Answer 7

Okay great!

Happy to help you! Shubhi Srivastava

Answer 8

from sklearn.linear_model import LogisticRegression

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()]

# make your model.
model = LogisticRegression()
model.fit(X,y)
y_pred = model.predict([datapoint])
print(y_pred[0])

Answer 9

I have tried with this and it's giving answer

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()]

from sklearn.linear_model import LogisticRegression
model=LogisticRegression()
model.fit(X,y)
prediction=model.predict([datapoint])
print(prediction[0])

Answer 10

In the first loop try to change int(first) to float(first)

same for x_pred

Answer 11

I have tried, but result is the same.. anyway, thanks for concern.

Answer 12

Thank you!!!

Answer 13

why we have used float variable? I mean what's the use of it? anybody please explain

Answer 14

Abhishek Kumar okay I get it... Thank you so much

Answer 15

None of the following answers was working for current version. After whole day of trying, copying, deleting, and trying again I've found the answer!
Here's the code:

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()]

from sklearn.linear_model import LinearRegression

model = LinearRegression()

model.fit(X, y)
pred = model.predict([datapoint])
print(int(pred[0] >= 0.41 or pred[0] <= -0.41))

To solve case 3, and 6 I had to tweak the parameter in the last print point by point. The thing is, this predict returned float numbers, and I had to find where were the breaking point between 0, and 1.
I wasn't fun, but after all I'm pretty proud of myself. ^^

Answer 16

Guys, can someone explain why we put "datapoint" in [ ] and then why we do this [0]? Don't understand;(

Answer 17

Алексей Шиканов , show your code so somebody can help you.

Answer 18

I would expect a value error for case 5 (too many values to unpack), but I don't know, why it wouldn't pass case 6.

Answer 19

from sklearn.linear_model import LogisticRegression

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 = LogisticRegression()
model.fit(X, y)
print(*model.predict([datapoint]))