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Regression Analysis Hands-On Solutions

 Regression Analysis Hands-On Solutions

The main agenda of this solution is those who are unable to do this course due to facing some issues, a little bit of lack of knowledge on these hands-on questions. Try to understand these codes and solve your hands-On Problems. (Not encourage copy and paste these solutions)



The course id is 55942.    


    


1. OLS (Ordinary Least Squares that algorithm used here)

 (Regression Analysis - Single Linear Regression)

Note:- Use Shift + Enter command for execution. 



cell 1:- (Just Shift + Enter, No need to write below code)


from sklearn.datasets import load_boston

import pandas as pd

boston = load_boston()

dataset = pd.DataFrame(data=boston.data, columns=boston.feature_names)

dataset['target'] = boston.target

print(dataset.head())


Cell 2:- 


###Start code here

X = dataset['RM']

Y = dataset['target']

###End code(approx 2 lines)


(shift + enter) 


Cell 3:- 


###Start code here

import statsmodels.api as sm

###End code(approx 1 line)


(shift + enter) 


Cell 4:- 


###Start code here

X =  sm.add_constant(X)

statsModel = sm.OLS(Y,X)

fittedModel = statsModel.fit()

###End code(approx 2 lines)


(Shift + Enter)


Cell 5:-


###Start code here

print(fittedModel.summary())

###End code(approx 1 line)


(Shift + Enter)


Cell 6:-


###Start code here

r_squared = 0.90

###End code(approx 1 line)


(Shift + Enter)


Cell 7:-  (Just Shift + Enter no need to write below code)


import hashlib

import pickle

def gethex(ovalue):

  hexresult=hashlib.md5(str(ovalue).encode())

  return hexresult.hexdigest()

def pickle_ans1(value):

  hexresult=gethex(value)

  with open('ans/output1.pkl', 'wb') as file:

    hexresult=gethex(value)

    print(hexresult)

    pickle.dump(hexresult,file)

pickle_ans1(r_squared)



2. MLR (Multi Linear Regression Analysis)

For the execution of cell run shift + enter 



cell 1:- 

from sklearn.datasets import load_boston

import pandas as pd

boston = load_boston()

dataset = pd.DataFrame(data=boston.data, columns=boston.feature_names)

dataset['target'] = boston.target

print(dataset.head())


cell 2:- 


X = dataset.drop('target',axis=1)

Y = dataset['target']


cell 3:- 


print(X.corr())

corr_value = 0.29


cell 4:- 


import statsmodels.api as sm

X = sm.add_constant(X)

fitted_model = sm.OLS(Y,X).fit()

print(fitted_model.summary())



cell 5:- 

r_squared = 0.96 


cell 6:- 

import hashlib

import pickle

def gethex(ovalue):

  hexresult=hashlib.md5(str(ovalue).encode())

  return hexresult.hexdigest()

def pickle_ans1(value):

  hexresult=gethex(value)

  with open('ans/output1.pkl', 'wb') as file:

    hexresult=gethex(value)

    print(hexresult)

    pickle.dump(hexresult,file)

def pickle_ans2(value):

  hexresult=gethex(value)

  with open('ans/output2.pkl', 'wb') as file:

    hexresult=gethex(value)

    print(hexresult)

    pickle.dump(hexresult,file)

pickle_ans1(corr_value)

pickle_ans2(r_squared)


Alert:- After completion of these two don't close the Jupytor notebook pages because the next quiz question answers will be on the Jupytor page. (Just submit the hackerrank page only). 











Regression Analysis Hands-On Solutions Regression Analysis Hands-On Solutions Reviewed by TECH UPDATE on May 29, 2021 Rating: 5

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