Examples of my work in Machine Learning, Deep Learning, Artificial Intelligence and Natural Language Processing using R, Python and Wolfram Mathematica. Also includes GeoLocation, Face Recognition and Social Networks.

1. Rubens
Zimbres
Fev/2016
1

2. Rubens
Zimbres
Fev/2016
2
All
images,
graphics,
analysis
and
models
presented
here
were
developed
by
me

3. Rubens
Zimbres
Fev/2016
3

4. Rubens
Zimbres
Fev/2016
4
I
was
CEO
of
Doux
from
2010
to
2013.
The
clinic
has
a
yearly
revenue
of
2.5
million
BRL.
In
2010
the
business
has
a
debt
of
1
million
BRL
and
monthly
revenue
of
64
thousand
BRL.
I
fired
6
out
of
12
employees,
hired
new
ones
and
did
a
quality
research
with
clients.
I
analyzed
results
of
the
research
and
the
database
(750,000
records)
regarding
demographic
data,
profitability,
purchases
patterns,
cost,
time
spent
in
procedures
and
sales.
I
opted
for
a
value
strategy,
high
service
quality,
moderate
cost
control,
investment
in
digital
strategies
and
fostering
of
word
of
mouth
advertising.
In
the
first
years
profit
increased
100%
and
lots
of
patients
returned
to
the
business.
Then
I
increased
price
by
8%,
defined
goals
for
variable
remuneration
and
more
flexible
payment
options.
Most
part
of
profit
was
reinvested
in
the
business.
I
increased
salary
of
employees
by
20%
and
goals
were
achieved
in
70%
of
time.
Most
employees
received
2
or
3
additional
salaries
due
to
variable
remuneration.
They
also
got
healthcare
and
their
satisfaction
ncreased
quality
offered
to
the
clients.
After
2.5
years,
net
profit
increased
239%,
financial
leverage
decreased
36%,
ROI
increased
206%,
ROE
95%,
EBITDA
98.5%
and
monthly
revenue
became
180
thousand
BRL
(almost
tripled).

5. Rubens
Zimbres
Fev/2016
5

6. Rubens
Zimbres
Fev/2016
6

7. Rubens
Zimbres
Fev/2016
7
Real
Data

8. Rubens
Zimbres
Fev/2016
8
Digital
strategy
(Google
Adwords
e
SEO)
was
used
to
generate
demand.
At
the
same
time,
we
fostered
word
of
mouth
advertising
among
our
clients
because
it’s
a
low
cost
strategy.
Below,
the
low
values
for
website
(blue
line)
are
due
to
seasonality
of
holidays
and
new
year.
Google
Adwords
generated
a
450%
return
over
investment.

9. Rubens
Zimbres
Fev/2016
9
Tracking
tools:
-­‐ Google
Adwords
-­‐ Google
Webmasters
Tools
-­‐ Google
Analytics
-­‐ Google
Search
-­‐ Woorank
-­‐ Locaweb
Control
Panel
-­‐ Internal
reports
-­‐ Database
data
-­‐ Excel
Locaweb Graphics
Daily
Visits
to
Doux
Dermatology
Website
Rubens
Zimbres
START

10. Rubens
Zimbres
Fev/2016
10
REVENUE
PROFIT

11. Rubens
Zimbres
Fev/2016
11

12. Rubens
Zimbres
Fev/2016
12
GOAL

13. Rubens
Zimbres
Fev/2016
13
Tableau database

14. Rubens
Zimbres
Fev/2016
14

15. Rubens
Zimbres
Fev/2016
15

16. Rubens
Zimbres
Fev/2016
16

17. Rubens
Zimbres
Fev/2016
17

18. Rubens
Zimbres
Fev/2016
18

19. Rubens
Zimbres
Fev/2016
19

20. Rubens
Zimbres
Fev/2016
20

21. Rubens
Zimbres
Fev/2016
21

22. Rubens
Zimbres
Fev/2016
22

23. Rubens
Zimbres
Fev/2016
23

24. Rubens
Zimbres
Fev/2016
24

25. Rubens
Zimbres
Fev/2016
25

26. Rubens
Zimbres
Fev/2016
26

27. Rubens
Zimbres
Fev/2016
27

28. Rubens
Zimbres
Fev/2016
28

29. Rubens
Zimbres
Fev/2016
29

30. Rubens
Zimbres
Fev/2016
30

31. Rubens
Zimbres
Fev/2016
31

32. Rubens
Zimbres
Fev/2016
32
GitHub
page:
https://github.com/RubensZimbres/repo.raz

33. Rubens
Zimbres
Fev/2016
33

34. Rubens
Zimbres
Fev/2016
34

35. Rubens
Zimbres
Fev/2016
35

36. Rubens
Zimbres
Fev/2016
36

37. Rubens
Zimbres
Fev/2016
37

38. Rubens
Zimbres
Fev/2016
38

39. Rubens
Zimbres
Fev/2016
39

40. Rubens
Zimbres
Fev/2016
40

41. Rubens
Zimbres
Fev/2016
41

42. Rubens
Zimbres
Fev/2016
42

43. Rubens
Zimbres
Fev/2016
43

44. Rubens
Zimbres
Fev/2016
44

45. Rubens
Zimbres
Fev/2016
45

46. Rubens
Zimbres
Fev/2016
46

47. Rubens
Zimbres
Fev/2016
47

48. Rubens
Zimbres
Fev/2016
48

49. Rubens
Zimbres
Fev/2016
49

50. Rubens
Zimbres
Fev/2016
50

51. Rubens
Zimbres
Fev/2016
51

52. Rubens
Zimbres
Fev/2016
52

53. Rubens
Zimbres
Fev/2016
53

54. Rubens
Zimbres
Fev/2016
54

55. Rubens
Zimbres
Fev/2016
55

56. Rubens
Zimbres
Fev/2016
56

57. Rubens
Zimbres
Fev/2016
57

58. Rubens
Zimbres
Fev/2016
58

59. Rubens
Zimbres
Fev/2016
59

60. Rubens
Zimbres
Fev/2016
60

61. Rubens
Zimbres
Fev/2016
61

62. Rubens
Zimbres
Fev/2016
62

63. Rubens
Zimbres
Fev/2016
63

64. Rubens
Zimbres
Fev/2016
64

65. Rubens
Zimbres
Fev/2016
65

66. Rubens
Zimbres
Fev/2016
66

67. Rubens
Zimbres
Fev/2016
67

68. Rubens
Zimbres
Fev/2016
68

69. Rubens
Zimbres
Fev/2016
69

70. Rubens
Zimbres
Fev/2016
70

71. Rubens
Zimbres
Fev/2016
71

72. Rubens
Zimbres
Fev/2016
72

73. Rubens
Zimbres
Fev/2016
73

74. Rubens
Zimbres
Fev/2016
74

75. Rubens
Zimbres
Fev/2016
75

76. Rubens
Zimbres
Fev/2016
76

77. Rubens
Zimbres
Fev/2016
77

78. Rubens
Zimbres
Fev/2016
78

79. Rubens
Zimbres
Fev/2016
79

80. Rubens
Zimbres
Fev/2016
80

81. Rubens
Zimbres
Fev/2016
81

82. Rubens
Zimbres
Fev/2016
82

83. Rubens
Zimbres
Fev/2016
83

84. Rubens
Zimbres
Fev/2016
84

85. Rubens
Zimbres
Fev/2016
85

86. Rubens
Zimbres
Fev/2016
86

87. Rubens
Zimbres
Fev/2016
87

88. Rubens
Zimbres
Fev/2016
88
Sales
Profit

89. Rubens
Zimbres
Fev/2016
89

90. Rubens
Zimbres
Fev/2016
90

91. Rubens
Zimbres
Fev/2016
91

92. Rubens
Zimbres
Fev/2016
92

93. Rubens
Zimbres
Fev/2016
93

94. Rubens
Zimbres
Fev/2016
94

95. Rubens
Zimbres
Fev/2016
95

96. Rubens
Zimbres
Fev/2016
96

97. Rubens
Zimbres
Fev/2016
97

98. Rubens
Zimbres
Fev/2016
98

99. Rubens
Zimbres
Fev/2016
99

100. Rubens
Zimbres
Fev/2016
100

101. Rubens
Zimbres
Fev/2016
101

102. Rubens
Zimbres
Fev/2016
102

103. Rubens
Zimbres
Fev/2016
103

104. Rubens
Zimbres
Fev/2016
104

105. Rubens
Zimbres
Fev/2016
105

106. Rubens
Zimbres
Fev/2016
106

107. Rubens
Zimbres
Fev/2016
107
Below
I
present
a
model
that
simulates
population
dynamics,
where
we
can
see
number
of
consumers
in
each
class
(satisfaction
levels,
diferente
colors)
and
their
evolution
over
time
(weeks).
The
model
uses
artificial
intelligence
with
autonomous
choices
of
partners
(unsupervised
learning)
and
complex
behavior.
Tempo
t
Tempo
t+23
Consumidores
Estados

108. Rubens
Zimbres
Fev/2016
108

109. Rubens
Zimbres
Fev/2016
109
Geolocation
of
sales
in
the
states
Profit/Loss
Analysis

110. Rubens
Zimbres
Fev/2016
110
Patent
of
the
Social
Network
code
I also develop social network models using artificial
intelligence associated to supervised and unsupervised
machine learning, to identify clusters and relevant
customers in a community. This way, marketing efforts can
be easily directed.

111. Rubens
Zimbres
Fev/2016
111

112. Rubens
Zimbres
Fev/2016
112
Clusters
inside
community
1
Community
1
(dark
red)
This
model
was
conceived
through
interactions
among
clients.
We
can
see
the
spatial
configuration
of
the
social
network
(social
distance),
connections,
positioning
of
people
of
interest
(red
circles),
communities
(diferente
colors),
clusters
inside
a
coommunity,
people
of
interest’s
connections
and
simulate
future
interactions
following
a
pattern
of
interaction.
Consumer
of
interest

113. Rubens
Zimbres
Fev/2016
113
Social
Network
of
5.000
consumers
Communities
in
different
colors
Consumer
of
interest
(red
dot)

114. Rubens
Zimbres
Fev/2016
114

115. Rubens
Zimbres
Fev/2016
115

116. Rubens
Zimbres
Fev/2016
116

117. Rubens
Zimbres
Fev/2016
117
Pattern
1
Pattern
2
Line
of
pattern
GOAL

118. Rubens
Zimbres
Fev/2016
118

119. Rubens
Zimbres
Fev/2016
119
80%
of
Big
data
is
unstructured.
This
is
the
great
challenge
of
businesses
in
the
analysis,
given
that
academic
literature
lacks
a
reliable
way
to
code
and
analyze
this
type
of
data.
I
developed
an
algorithm
that
identifies
keywords,
the
reasoning
of
the
text
and
the
subject
and
I
have
the
research
skills
to
develop
a
new
way
to
code
and
analyze
unstructured
data
via
qualitative
analysis.

120. Rubens
Zimbres
Fev/2016
120

121. Rubens
Zimbres
Fev/2016
121

122. Rubens
Zimbres
Fev/2016
122

123. Rubens
Zimbres
Fev/2016
123

124. Rubens
Zimbres
Fev/2016
124

125. Rubens
Zimbres
Fev/2016
125

126. Rubens
Zimbres
Fev/2016
126

127. Rubens
Zimbres
Fev/2016
127

128. Rubens
Zimbres
Fev/2016
128
RUBENS
ZIMBRES
rubens.zimbres@eclipso.de
(11)
96750-­‐6962
(11)
3022-­‐6755