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  4. Correlation and Regression Lines - A Quick Recap #2
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Correlation and Regression Lines - A Quick Recap #2

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  • frankeast5
     + 0 comments

    Interesting problem! It's a great way to practice applying regression analysis with real data. Calculating the slope by hand gives good insight into how relationships between variables are analyzed. Thanks for sharing this! Cricbet99

  • ericstricker73
     + 1 comment
    import numpy as np
from sklearn.linear_model import LinearRegression

Physics = [  15,  12,  8,   8,   7,   7,   7,   6,   5,   3]
History = [ 10,  25,  17,  11,  13,  17,  20,  13,  9,   15]

#Sample data
X = np.array(Physics).reshape((-1, 1))
y = np.array(History)

#Create and train the model
model = LinearRegression()
model.fit(X, y)

#Get the slope (coefficient)
slope = model.coef_[0]
slope

  • anna_ning9
     + 0 comments

    Help !!

    new to hackerRank , how to debug here ?? what's the expected input and output format ? This code runs perfect locally....

    import numpy as np from sklearn.linear_model import LinearRegression

    def return_cof(x,Y): x=np.array(x).reshape(-1, 1) y = np.array(Y).reshape(-1, 1) reg=LinearRegression().fit(x,Y) ans=reg.coef_ return round(ans[0],3)

    a=[15,12,8,8,7,7,7,6,5,3] b=[10,25,17,11,13,17,20,13,9,15] ans=return_cof(a,b) print(ans)

  • imen_romdhane6
     + 0 comments

    Does anyone know why I am getting this error? Thank you

    import numpy as np ModuleNotFoundError: No module named 'numpy'

  • cafelatte
     + 0 comments

    Common irritation here:
    * no input is provided in this excercise, hardcode it if needed
    * "(your answer) in the textbox" just print your answer to stdout