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).

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). 



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