ProblemSolving-FrameWork-ML/visualize.py at main · MvMukesh/ProblemSolving-FrameWork-ML · GitHub

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# Understand data with simple visualization

'''

1.Univariate Plots

1.1. Histogram

1.2. Density plot

1.3. Box plot

2.Multivariate Plots

2.1 Correlation matrix plot

2.2 Scatter plot

'''

from matplotlib import pyplot

from pandas import read_csv

import numpy

import pandas.plotting.scatter_matrix

filename = 'pima-indians-diabetes.data.csv'

names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']

data = read_csv(filename, names=names)

# 1. Univariate Plots

# 1.1 Histogram

data.hist()

pyplot.show()

# 1.2 Density plots

data.plot(title = 'Density Plot', kind = 'density',subplots = True, layout=(3,3), sharex = False)

pyplot.show()

# 1.3 Box plots

data.plot(title = 'Box Plot', kind = 'box', subplots = True, layout=(3,3), sharex = False, sharey = False)

pyplot.show()

# 2. Multivariate plots

# 2.1 Correlation matrix plot

correlations = data.corr()

fig = pyplot.figure()

ax = fig.add_subplot(111)

cax = ax.matshow(correlations, vmin = -1, vmax = 1)

fig.colorbar(cax)

# Modify this part for generic

ticks = numpy.arange(0,9,1)

ax.set_xticks(ticks)

ax_set_yticks(ticks)

ax.set_xticklabels(names)

ax.set_yticklabels(names)

#########################

pyplot.show()

# 2.2 Scatter plots

scatter_matrix(data)

pyplot.show()

ProblemSolving-FrameWork-ML/visualize.py at main · MvMukesh/ProblemSolving-FrameWork-ML · GitHub

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