In [7]:

    

#importing the graphlab
import graphlab as gl


    

In [8]:

    

#importing the input data into graphlab SFrame dataset
crimeData = gl.SFrame.read_csv("Philadelphia_Crime_Rate_noNA.csv")
crimeData


    

    





Finished parsing file C:\learn\ML\Regression\week1\Philadelphia_Crime_Rate_noNA.csv






    





Parsing completed. Parsed 99 lines in 0.036002 secs.






    





Finished parsing file C:\learn\ML\Regression\week1\Philadelphia_Crime_Rate_noNA.csv






    





Parsing completed. Parsed 99 lines in 0.032002 secs.






    




------------------------------------------------------
Inferred types from first 100 line(s) of file as 
column_type_hints=[long,float,float,float,float,str,str]
If parsing fails due to incorrect types, you can correct
the inferred type list above and pass it to read_csv in
the column_type_hints argument
------------------------------------------------------






    
Out[8]:





    

        
HousePrice
        
HsPrc ($10,000)
        
CrimeRate
        
MilesPhila
        
PopChg
        
Name
        
County
    
    

        
140463
        
14.0463
        
29.7
        
10.0
        
-1.0
        
Abington
        
Montgome
    
    

        
113033
        
11.3033
        
24.1
        
18.0
        
4.0
        
Ambler
        
Montgome
    
    

        
124186
        
12.4186
        
19.5
        
25.0
        
8.0
        
Aston
        
Delaware
    
    

        
110490
        
11.049
        
49.4
        
25.0
        
2.7
        
Bensalem
        
Bucks
    
    

        
79124
        
7.9124
        
54.1
        
19.0
        
3.9
        
Bristol B.
        
Bucks
    
    

        
92634
        
9.2634
        
48.6
        
20.0
        
0.6
        
Bristol T.
        
Bucks
    
    

        
89246
        
8.9246
        
30.8
        
15.0
        
-2.6
        
Brookhaven
        
Delaware
    
    

        
195145
        
19.5145
        
10.8
        
20.0
        
-3.5
        
Bryn Athyn
        
Montgome
    
    

        
297342
        
29.7342
        
20.2
        
14.0
        
0.6
        
Bryn Mawr
        
Montgome
    
    

        
264298
        
26.4298
        
20.4
        
26.0
        
6.0
        
Buckingham
        
Bucks
    

[99 rows x 7 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.

In [9]:

    

# setting plot canvas to this IPython notebook
gl.canvas.set_target('ipynb')

# plotting scatter plot 
crimeData.show(view="Scatter Plot", x="CrimeRate", y="HousePrice")


    

In [10]:

    

# Calculating Linear Regression model

crimeData_model = gl.linear_regression.create(crimeData, target='HousePrice', features=['CrimeRate'],validation_set=None,verbose=False)


    

In [11]:

    

crimeData_model.coefficients


    

    
Out[11]:





    

        
name
        
index
        
value
        
stderr
    
    

        
(intercept)
        
None
        
176626.046881
        
11245.5882194
    
    

        
CrimeRate
        
None
        
-576.804949058
        
226.90225951
    

[2 rows x 4 columns]

In [12]:

    

#importing matplotlib library for plotting

import matplotlib.pyplot as plt
%matplotlib inline


    

In [15]:

    

plt.plot(crimeData['CrimeRate'],crimeData['HousePrice'],'.',
        crimeData['CrimeRate'],crimeData_model.predict(crimeData),'-')


    

    
Out[15]:





[<matplotlib.lines.Line2D at 0x224efac8>,
 <matplotlib.lines.Line2D at 0x224efb70>]






    

In [17]:

    

# Tryting the same linear model withput high influencial point

crimeData_woHI = crimeData[crimeData['HousePrice'] != 96200]


    

In [18]:

    

crimeData_woHI_model = gl.linear_regression.create(crimeData_woHI, target='HousePrice', features=['CrimeRate'],validation_set=None,verbose=False)


    

In [20]:

    

# getting linear model coefficients
crimeData_woHI_model.get('coefficients')


    

    
Out[20]:





    

        
name
        
index
        
value
        
stderr
    
    

        
(intercept)
        
None
        
225204.604303
        
16404.0247514
    
    

        
CrimeRate
        
None
        
-2287.69717443
        
491.537478123
    

[2 rows x 4 columns]

In [22]:

    

plt.plot(crimeData_woHI['CrimeRate'],crimeData_woHI['HousePrice'],'.',
        crimeData_woHI['CrimeRate'],crimeData_woHI_model.predict(crimeData_woHI),'-')


    

    
Out[22]:





[<matplotlib.lines.Line2D at 0x29417160>,
 <matplotlib.lines.Line2D at 0x2937dac8>]






    

In [ ]: