Regression Analysis Hands-On 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.
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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).

nice page keep it up
ReplyDeleteHow did you get r_squared = 0.96 but in the actual summary r_squared = 0.74
ReplyDeleteI want some other hands on solutions.. devsecops
ReplyDeletex2 How did you get r_squared = 0.96
ReplyDeleteAre there solution for Advanced Regression Analysis fresco hands-on. The course id is 55946
ReplyDeletedo not use the line X = sm.add_constant(X), and use corr = dataset.corr()
ReplyDeletecorr_value = round(corr['CRIM']['PTRATIO'],2) for corr and use r_squared = round(fitted_model.rsquared,2) for rs