MMPSS24 in Python statsmodels - OLS linear regression

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Python

I am having issues while running the below mentioned multi linear regression using stats model.

The below thing is given:

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())

Now it has been asked to do the following things:

- From the above output you can see the various attributes of the dataset.
- The 'target' column has the dependent values(housing prices) and rest of the colums are the independent values that influence the target values
- Lets find the relation between 'housing price' and 'average number of rooms per dwelling' using stats model
- Assign the values of column "RM"(average number of rooms per dwelling) to variable X
- similerly assign the values of 'target'(housing price) column to variable Y
- sample code: values = data_frame['attribute_name']
- import statsmodel.api as sm
- initialise the OLS model by passing target(Y) and attribute(X).Assign the model to variable 'statsModel'
- fit the model and assign it to variable 'fittedModel, make sure you add constant term to input X'
- sample code for initialization: sm.OLS(target, attribute)
- print the summary of fittedModel using the summary() function
- from the summary report note down the R-squared value and assign it to variable 'r_squared' in the below cell

Can some one pls help me to implement these items.

What I have tried:

i) X = dataset.drop('target', axis = 1)
ii) Y = dataset['target']
iii) X.corr()
iv) corr_value = <something>
v) import statsmodels.api as sm
Remaining not able to do..