Using the data from the 2013_NYC_CD_MedianIncome_Recycle.xlsx file, create a predictor using the weights from the model. This time, use the built in attributes in your model rather than hard-coding them into your algorithm



In [1]:

    
import pandas as pd









    



/usr/local/lib/python3.5/site-packages/matplotlib/__init__.py:1035: UserWarning: Duplicate key in file "/Users/mercybenzaquen/.matplotlib/matplotlibrc", line #2
  (fname, cnt))



In [2]:

    
import statsmodels.formula.api as smf



In [3]:

    
df = pd.read_excel("2013_NYC_CD_MedianIncome_Recycle.xlsx")



In [4]:

    
df.head()









    Out[4]:






  
    
      
      CD_Name
      MdHHIncE
      RecycleRate
    
  
  
    
      0
      Battery Park City, Greenwich Village & Soho
      119596
      0.286771
    
    
      1
      Battery Park City, Greenwich Village & Soho
      119596
      0.264074
    
    
      2
      Chinatown & Lower East Side
      40919
      0.156485
    
    
      3
      Chelsea, Clinton & Midtown Business Distric
      92583
      0.235125
    
    
      4
      Chelsea, Clinton & Midtown Business Distric
      92583
      0.246725



In [22]:

    
lm = smf.ols(formula="RecycleRate~MdHHIncE",data=df).fit()



In [23]:

    
lm.params









    Out[23]:





Intercept    0.074804
MdHHIncE     0.000002
dtype: float64

y = ß0 + ß1x

Here:

y: is the variable that we want to predict

ß0: is intercept of the regression line i.e. value of y when x is 0

ß1: is coefficient of x i.e. variation in y with change in value of x

x: Variables that affects value of y i.e. already know variable whose effect we want to se on values of y



In [26]:

    
intercept, slope = lm.params
def RecycleRate_calculator(median_income): 
    return (0.074804 + (0.000002* float(median_income)))



In [27]:

    
RecycleRate_calculator(119596)









    Out[27]:





0.313996



In [ ]:

	CD_Name	MdHHIncE	RecycleRate
0	Battery Park City, Greenwich Village & Soho	119596	0.286771
1	Battery Park City, Greenwich Village & Soho	119596	0.264074
2	Chinatown & Lower East Side	40919	0.156485
3	Chelsea, Clinton & Midtown Business Distric	92583	0.235125
4	Chelsea, Clinton & Midtown Business Distric	92583	0.246725