notebook.community

Edit and run



In [25]:

    
import linreg
import pandas as pd



In [13]:

    
linreg.lab_experiments()



In [14]:

    
linreg.linreg_example()









    



Slope = 1.965552
Intercept = 0.789555



In [15]:

    
linreg.make_data_points()









    Out[15]:





(array([ 0.        ,  0.26315789,  0.52631579,  0.78947368,  1.05263158,
        1.31578947,  1.57894737,  1.84210526,  2.10526316,  2.36842105,
        2.63157895,  2.89473684,  3.15789474,  3.42105263,  3.68421053,
        3.94736842,  4.21052632,  4.47368421,  4.73684211,  5.        ]),
 array([  0.39764617,   1.26578746,   1.29291222,   1.80108222,
         3.58815344,   3.82828186,   3.70434868,   4.3250836 ,
         5.0950376 ,   5.86005947,   6.26675257,   5.64136313,
         6.95328529,   7.4565617 ,   8.54487947,   8.83795151,
         7.92023398,   9.26144715,  10.80819687,  10.28071513]))



In [16]:

    
linreg.lab_experiments()



In [17]:

    
linreg.lab_experiments(1000,2003)



In [18]:

    
x,y = linreg.make_data_points()
linreg.make_single_plot(x,y,'Force','Acceleration','Lab Experiment')









    Out[18]:





<matplotlib.axes.AxesSubplot at 0x10cba92d0>



In [19]:

    
linreg.lab_experiment()









    Out[19]:





<matplotlib.axes.AxesSubplot at 0x10bb77c10>

data exploration



In [20]:

    
import pandas as pd
#loansDataRaw = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')
loansmin = pd.read_csv('../datasets/loanf.csv')



In [21]:

    
loansmin.head()









    Out[21]:






  
    
      
      Interest.Rate
      FICO.Score
      Loan.Length
      Monthly.Income
      Loan.Amount
    
  
  
    
      6 
       15.31
       670
       36
        4891.67
        6000
    
    
      11
       19.72
       670
       36
        3575.00
        2000
    
    
      12
       14.27
       665
       36
        4250.00
       10625
    
    
      13
       21.67
       670
       60
       14166.67
       28000
    
    
      21
       21.98
       665
       36
        6666.67
       22000

data analysis



In [39]:

    
a = pd.scatter_matrix(loansmin,alpha=0.6,figsize=(9,9), diagonal='kde')



In [38]:

    
a = pd.scatter_matrix(loansmin,alpha=0.05,figsize=(9,9), diagonal='kde')



In [45]:



In [ ]:

	Interest.Rate	FICO.Score	Loan.Length	Monthly.Income	Loan.Amount
6	15.31	670	36	4891.67	6000
11	19.72	670	36	3575.00	2000
12	14.27	665	36	4250.00	10625
13	21.67	670	60	14166.67	28000
21	21.98	665	36	6666.67	22000