%pylab inline
import linreg
import pandas as pd
Populating the interactive namespace from numpy and matplotlib
linreg.lab_experiments()
linreg.linreg_example()
Slope = 1.965552 Intercept = 0.789555
linreg.make_data_points()
(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]))
linreg.lab_experiments()
linreg.lab_experiments(1000,2003)
x,y = linreg.make_data_points()
linreg.make_single_plot(x,y,'Force','Acceleration','Lab Experiment')
<matplotlib.axes.AxesSubplot at 0x10cba92d0>
linreg.lab_experiment()
<matplotlib.axes.AxesSubplot at 0x10bb77c10>
import pandas as pd
#loansDataRaw = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')
loansmin = pd.read_csv('../datasets/loanf.csv')
loansmin.head()
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 |
a = pd.scatter_matrix(loansmin,alpha=0.6,figsize=(9,9), diagonal='kde')
a = pd.scatter_matrix(loansmin,alpha=0.05,figsize=(9,9), diagonal='kde')