Regression Analysis Hands-on Solution | TCS Fresco Play
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Make an effort to understand these solutions and apply them to your Hands-On difficulties. (It is not advisable that copy and paste these solutions).
Regression Analysis MCQ solution
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)
Regression Analysis MCQ solution
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)
Regression Analysis MCQ solution
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).
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