Multiple Regression

Simple Linear Regression: $$ y = \beta_0 + \beta_1 X $$ Multiple Linear Regression: $$ y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + ... $$ Well studied field in statistics Focus will be on what is relevant for Data Science - practical and relevant for predection



In [1]:

    
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns



In [2]:

    
from sklearn.datasets import load_boston



In [3]:

    
boston_data = load_boston()



In [4]:

    
df = pd.DataFrame(boston_data.data, columns=boston_data.feature_names)



In [5]:

    
df.head()



In [6]:

    
df.shape









    Out[6]:





(506, 13)



In [7]:

    
X = df



In [8]:

    
y = boston_data.target

Statsmodels



In [10]:

    
import statsmodels.api as sm
import statsmodels.formula.api as smf









    



---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-10-93c018dacf9a> in <module>()
----> 1 import statsmodels.api as sm
      2 import statsmodels.formula.api as smf

ModuleNotFoundError: No module named 'statsmodels'



In [ ]:

	CRIM	ZN	INDUS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT
0	0.00632	18.0	2.31	0.538	6.575	65.2	4.0900	1.0	296.0	15.3	396.90	4.98
1	0.02731	0.0	7.07	0.469	6.421	78.9	4.9671	2.0	242.0	17.8	396.90	9.14
2	0.02729	0.0	7.07	0.469	7.185	61.1	4.9671	2.0	242.0	17.8	392.83	4.03
3	0.03237	0.0	2.18	0.458	6.998	45.8	6.0622	3.0	222.0	18.7	394.63	2.94
4	0.06905	0.0	2.18	0.458	7.147	54.2	6.0622	3.0	222.0	18.7	396.90	5.33