Assignment 3

Using the data from the2013_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 [47]:

    
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf



In [48]:

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



In [49]:

    
df.head()









    Out[49]:






  
    
      
      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 [51]:

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



In [52]:

    
lm.params









    Out[52]:





Intercept    0.074804
MdHHIncE     0.000002
dtype: float64



In [53]:

    
Intercept, Median_Income = lm.params



In [58]:

    
df['Predicted RecycleRate'] = Median_Income*df['MdHHIncE']+Intercept



In [59]:

    
df.head()









    Out[59]:






  
    
      
      CD_Name
      MdHHIncE
      RecycleRate
      Predicted RecycleRate
    
  
  
    
      0
      Battery Park City, Greenwich Village & Soho
      119596
      0.286771
      0.298402
    
    
      1
      Battery Park City, Greenwich Village & Soho
      119596
      0.264074
      0.298402
    
    
      2
      Chinatown & Lower East Side
      40919
      0.156485
      0.151307
    
    
      3
      Chelsea, Clinton & Midtown Business Distric
      92583
      0.235125
      0.247898
    
    
      4
      Chelsea, Clinton & Midtown Business Distric
      92583
      0.246725
      0.247898



In [56]:

    
intercept, slope = lm.params



In [57]:

    
df.plot(kind="scatter",x="MdHHIncE",y="RecycleRate")
plt.plot(df["MdHHIncE"],slope*df["MdHHIncE"]+intercept,"-",color="red")









    Out[57]:





[<matplotlib.lines.Line2D at 0x1111b3710>]



In [ ]:



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