Machine learning and Steem #4: Account classification - comparison of available classifiers + 3D visualization of features by jacekw.dev

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utopian-io · @jacekw.dev · Aug 28 '18 (edited)

$23.59

Machine learning and Steem #4: Account classification - comparison of available classifiers + 3D visualization of features

#### Repository
https://github.com/scikit-learn/scikit-learn

#### What Will I Learn?
- Collect data from [beem](https://github.com/holgern/beem) and [SteemSQL](https://steemsql.com/)
- 3D visualization of features
- Use different classifiers
- Compare classifiers accuracy

#### Requirements
- python
- basic concepts of data analysis / machine learning

Tools:
- [python 3](https://www.python.org/)
	- [pandas](https://pandas.pydata.org/)
	- [matplotlib](https://matplotlib.org/)
	- [ipympl](https://github.com/matplotlib/jupyter-matplotlib)
	- [mplot3d](https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html)
	- [seaborn](https://seaborn.pydata.org/)
	- [jupyter notebook](http://jupyter.org/)
	- [scikit-learn](http://scikit-learn.org/stable/)


It looks like a lot of libraries, but it's a standard python toolset for data analysis / machine learning.

<center>![](https://cdn.steemitimages.com/DQmfC76cTW6xPKuvAFhaxoBADgoteDKXCy6MdaCw9gS5UTu/image.png)</center>

#### Difficulty
- Intermediate

#### Tutorial Contents
- Problem description
- Collecting data
- 3D visualization of features
- Overview and comparison of available classifiers

#### Problem description

The purpose of this tutorial is to build the account classifier (content creator vs scammer vs comment spammer vs bid-bot). In the previous part of this tutorial, we achieved an accuracy of 95%. And this is what the confusion matrix looks like:

<center>
![](https://cdn.steemitimages.com/DQmUEzwcnryicipeWJLubsHrTYc7MxB4n7RTVW4RuwxhdJM/image.png)
</center>

The accuracy is quite good, but we will also check other available machine learning models. In one of the next parts of this series we will go on **to build an API that will allow any Steem user to use this classifier**.

#### Collecting data

The data was downloaded in a similar way to the previous part of the tutorial. Currently, each class has 769 records. The script that retrieves all the data can be found [here](https://github.com/jwladzinski/MachineLearningForSteem/blob/master/4/read_data.ipynb).

The data looks as follows:
```
                  name  followers  followings  follow ratio  muters  reputation  effective sp         own sp    sp ratio  curation_rewards  posting_rewards  witnesses_voted_for  posts  average_post_len  comments  average_comment_len  comments_with_link_ratio  posts_to_comments_ratio  class
0         anthowarlike        212          34      0.160377       0   56.409193  1.349465e+02     134.946458    1.000000             0.654          265.146                    0      0                 0         0                    0                  0.000000                 0.000000      0
1           gokcehan61       4117       16596      4.031091      19   51.233260  1.840426e+00       6.897189    0.266837             0.378          143.148                    3      0                 0         0                    0                  0.000000                 0.000000      0
2           mimikombat        773         701      0.906856       2   60.575495  1.503410e+02     388.205087    0.387272             4.340          762.985                    0    125              3366       401                   50                  0.084788                 0.311721      0
3             stixxzyy        238           4      0.016807       0   55.103692  8.356541e+01      83.565407    1.000000             0.000          166.170                    0     80               280         0                    0                  0.000000                 0.000000      0
4            akintunde       2171         589      0.271304       9   62.343843  3.119804e+02     448.934538    0.694935            11.737         1025.171                   19     21              1941        26                  241                  0.115385                 0.807692      0
5             bryangav        718          80      0.111421       0   55.217102  2.791844e+02      77.046693    3.623574             3.779          183.609                    8     62              3023        37                  263                  0.783784                 1.675676      0
6              plouton         50           9      0.180000       0   44.422357  1.500954e+01       6.313641    2.377320             0.011           11.527                    0      0                 0         0                    0                  0.000000                 0.000000      0
7             marzukie       1546        2585      1.672057       6   54.130093  9.463211e+01      94.632106    1.000000             2.988          146.743                   30     77              2597       361                   90                  0.019391                 0.213296      0
8            lisnabuah         25           2      0.080000       0   42.741003  1.190729e+01       0.269269   44.220849             0.002            5.931                    0      0                 0         0                    0                  0.000000                 0.000000      0
9           nappingkid        255          15      0.058824       1   56.969551  1.773283e+02     177.328282    1.000000             1.110          274.575                    0    103               290         8                   45                  0.000000                12.875000      0
10           itsmskali        118          24      0.203390       0   41.774677  5.015003e+00       3.569537    1.404945             0.022            4.982                    0      0                 0         0                    0                  0.000000                 0.000000      0
11             rahmato        341          99      0.290323       0   34.397586  1.502713e+01       1.288114   11.665988             0.008            0.686                    0     23               875         9                   27                  0.000000                 2.555556      0
12    theuniqornaments        305          14      0.045902       0   45.984113  1.863325e+00       0.046184   40.345844             0.054           12.777                    1     39               369         2                   19                  0.000000                19.500000      0
13          alexcarlos        232          25      0.107759       0   52.324648  4.336502e+01      43.365019    1.000000             0.064           83.009                    0     50              1182         2                   39                  0.000000                25.000000      0
14         sharkhssn90        281         115      0.409253       0   26.253763  1.509236e+01       6.586708    2.291336             0.000            0.090                    0     18              1106        25                  121                  0.000000                 0.720000      0
15              omur61        976        1502      1.538934       1   56.939854  4.821556e+01      48.215558    1.000000             7.902          239.101                    1      2               123         1                   32                  0.000000                 2.000000      0
16          philberlin        141          50      0.354610       0   31.309064  1.500171e+01       3.211032    4.671927             0.007            0.305                    0      0                 0         0                    0                  0.000000                 0.000000      0
17             chigz14        342         164      0.479532       2   56.755740  4.491221e+01     165.039345    0.272130             0.167          268.042                    4     88               778        28                   93                  0.214286                 3.142857      0
18            elysiian       1969          43      0.021838       7   60.425031  1.800332e+04   17074.531635    1.054396           291.260         1078.414                    6     68                57         0                    0                  0.000000                 0.000000      0
19           skizoweza        307           5      0.016287      53   59.502481  3.943580e+02     394.357979    1.000000             5.600          526.632                    0      0                 0         0                    0                  0.000000                 0.000000      0
20             coretan        908          76      0.083700       4   61.023030  2.803443e+02     481.469079    0.582268            13.339          767.718                    0     28              2257        49                   83                  0.040816                 0.571429      0
21       tonygreene113       1329         265      0.199398       8   49.221399  2.278498e+02     227.849765    1.000000             4.193           77.221                    7     48               911       570                  128                  0.629825                 0.084211      0
22    tenpoundsterling        293          41      0.139932       0   51.745826  4.027079e+01      40.270792    1.000000             0.042           64.298                    3     25               962       167                  124                  0.053892                 0.149701      0
23          copypast3r         19           1      0.052632       0   25.000000  5.021256e+00       0.509150    9.862030             0.000            0.000                    0      0                 0         0                    0                  0.000000                 0.000000      0
24                 eae        859         428      0.498254       0   57.780078  1.530711e+02     153.071146    1.000000             1.217          327.817                    0    107              1276        12                   66                  0.000000                 8.916667      0
25            papyboys        330          99      0.300000       1   56.442663  2.003855e+01     120.254800    0.166634             0.046          239.335                    1    116              1273         0                    0                  0.000000                 0.000000      0
26         troonatnoor        409         144      0.352078       7   47.927954  4.434722e+01      44.347223    1.000000             0.000           18.675                    0     16              3757         2                  289                  0.500000                 8.000000      0
27           nowonline        544         324      0.595588       0   52.441200  1.474777e+02     147.477713    1.000000             0.679           75.321                    0     24              6303       211                  178                  0.066351                 0.113744      0
28           skizoweza        307           5      0.016287      53   59.502481  3.943580e+02     394.357979    1.000000             5.600          526.632                    0      0                 0         0                    0                  0.000000                 0.000000      0
29         susanli3769       1321         288      0.218017       3   66.699240  5.969675e+03    3778.127511    1.580062           138.835         4059.769                    9     93              2200       167                   51                  0.005988                 0.556886      0
...                ...        ...         ...           ...     ...         ...           ...            ...         ...               ...              ...                  ...    ...               ...
```

#### 3D visualization of features

In the previous parts of this tutorial we have done 2D visualization, but equally well, we can do 3D visualization. We will use the [mplot3d](https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html) library and the code below.

```
# create 3D scatter plot
# x, y, z - names of features
def scatter_plot_3d(d, x, y, z):
    fig = plt.figure(figsize=(10, 10))
    # create 3D axes
    ax = Axes3D(fig)
    # plot points, cmap is colormap
    ax.scatter(d[x], d[y], d[z], c=d['class'], cmap=plt.cm.Accent, edgecolor='k', s=80)
    # set labels of each axis
    ax.set_xlabel(x)
    ax.set_ylabel(y)
    ax.set_zlabel(z)
    # add legend to plot
    # color of each legend label coresponds to color of given class
    ax.legend([label(color(150, 210, 150)), label(color(247, 192, 135)),
               label(color(235, 8, 144)), label(color(102, 102, 102))],
              ['content-creator', 'scammer', 'comment-spammer', 'bid-bot'], numpoints = 1)
```
---
This allows you to place 3 features on the same chart at the same time.

**followings + followers + muters:**
<center>![](https://cdn.steemitimages.com/DQmegQetwvqZ33ZrGuu6n3AqS4HUmQY4DTYkcaESLL2h6ss/image.png)</center>

If we run the code as Jupyter Notebook, the charts are interactive.

https://imgur.com/A60duNm.gif

---
**sp ratio + follow ratio + reputation:**
<center>![](https://cdn.steemitimages.com/DQmfYHUWJ4dERQvXKgGjUoqiN5N3rZ5wQ8zPFPbzEnNwjHo/image.png)</center>
---
**curation_rewards + posting_rewards + posts:**
<center>![](https://cdn.steemitimages.com/DQmUweW1GpzwASKAV1vKUKduQemJCAPG4okp7vRAf5mxkBT/image.png)</center>
---
**average_comment_len + comments_with_link_ratio + posts_to_comments_ratio:**
<center>![](https://cdn.steemitimages.com/DQmYH9hhrYVtCUYf9z5LWBGv7SwFS74CHsfGEMgitD8NmGG/image.png)</center>

3D visualization has its advantages and disadvantages. We can put more information on one chart, but on the other hand, they are sometimes less readable.

#### Overview and comparison of available classifiers

Last time, we used classifiers like the neural network and the decision tree. The latter turned out to have better accuracy. But there are also many other classifiers that you can use:
- [BernoulliNB](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB) - Naive Bayes classifier for multivariate Bernoulli models, is suitable for discrete data.
- [GaussianNB](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB) - Gaussian Naive Bayes classifier
- [KNeighborsClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier) - K-nearest neighbors classifier
- [NearestCentroid](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestCentroid.html#sklearn.neighbors.NearestCentroid) - Nearest Centroid classifier, similiar to KNeighborsClassifier
- [LinearSVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC) - Linear Support Vector Machine (SVM) classifier

In addition to accuracy and confusion matrices, we will also determine the execution time for each classifier.

---

```
X_cols = columns
y_cols = ['class']
X = df[X_cols]
y = df[y_cols]

# split data to training and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# convert from pandas dataframe to plain array
y_train = np.array(y_train).ravel()
y_test = np.array(y_test).ravel()


classifiers = [
    ('DecisionTreeClassifier', DecisionTreeClassifier(max_depth=8)),
    ('BernoulliNB', BernoulliNB()),
    ('GaussianNB', GaussianNB()),
    ('KNeighborsClassifier', KNeighborsClassifier()),   
    ('NearestCentroid', NearestCentroid()),
    ('LinearSVC', LinearSVC())]

# iterate over used classifiers
for clf_name, clf in classifiers:
    start = time.time()
    clf.fit(X_train, y_train) # train
    y_pred = clf.predict(X_test) # test
    end = time.time()
    print('%25s - accuracy: %.3f, execution time: %.3f ms' % 
          (clf_name, accuracy_score(y_pred, y_test), end - start))
    cm = confusion_matrix(y_test, y_pred)
    plot_confusion_matrix(cm)
```
---
The results are as follows:

Classifier|Accuracy|Execution time|Confusion matrix|
-|-|-|-
DecisionTreeClassifier|0.958|0.023|![](https://steemitimages.com/400x400/https://cdn.steemitimages.com/DQmaCg7NPYkQgVMMVipSDd7S2MtLFnTKK9Xunqbgv3zHyxY/image.png)
BernoulliNB|0.508|0.003|![](https://steemitimages.com/400x400/https://cdn.steemitimages.com/DQmTyzCgDfbzDH7ySJhuaQL9EopdG4Q7gk5MN9N1D1LpjTD/image.png)
GaussianNB|0.360|0.003|![](https://steemitimages.com/400x400/https://cdn.steemitimages.com/DQmb4SrDAyXtnRY1R2hbbLWBqBvBnBSshUih3RDQ8Nm11Qh/image.png)
KNeighborsClassifier|0.794|0.012|![](https://steemitimages.com/400x400/https://cdn.steemitimages.com/DQmbjaQcUcvbQhbxiPtirQzE6VrzqgRZm9EXUsSoZQrHyMT/image.png)
NearestCentroid|0.268|0.002|![](https://steemitimages.com/400x400/https://cdn.steemitimages.com/DQmQcSfoTLBXEbWRcziUwqsLaVF64C5zviQGoU4YF8gvJKR/image.png)
LinearSVC|0.656|0.933|![](https://steemitimages.com/400x400/https://cdn.steemitimages.com/DQmYXypfixaHKmSXrprncRVvD5vudE4tJC97UoyVKyCJrde/image.png)
---

The best result was obtained by the decision tree, which means that no better classifier was found for this problem. `LinearSVC` had a much longer execution time than other classifiers.


To visualize the decision tree, just use the following code:
```
import graphviz
from sklearn.tree import export_graphviz

# convert decition tree metadata to graph
graph = graphviz.Source(export_graphviz(
    classifiers[0][1],
    out_file=None,
    feature_names=X_cols,
    class_names=class_names,
    filled=True,
    rounded=True))

# set format of output file
graph.format = 'png'
# save graph to file
graph.render('DecisionTreeClassifier')
```
---
 ![](https://cdn.steemitimages.com/DQmVszZizcFWjjRPEwgf7a8tMhYW8SBDuk4bQdynH63aSNn/image.png)


In the next part of the tutorial we will use [Ensemble learning](https://en.wikipedia.org/wiki/Ensemble_learning), i.e. using multiple machine learning algorithms to obtain better predictive performance.

#### Curriculum
1. [Machine learning (Keras) and Steem #1: User vs bid-bot binary classification](https://steemit.com/utopian-io/@jacekw.dev/machine-learning-keras-and-steem-1-user-vs-bid-bot-binary-classification)
2. [Machine learning and Steem #2: Multiclass classification (content creator vs scammer vs comment spammer vs bid-bot)](https://steemit.com/utopian-io/@jacekw.dev/machine-learning-and-steem-2-multiclass-classification-content-creator-vs-scammer-vs-comment-spammer-vs-bid-bot)
3. [Machine learning and Steem #3: Account classification - accuracy improvement up to 95%](https://steemit.com/utopian-io/@jacekw.dev/machine-learning-and-steem-3-account-classification-accuracy-improvement-up-to-95)

#### Conclusions
- the more data the better
- 3D visualization allows you to show more information than 2D visualization, but on the other hand, the data can be less readable
- it is worth to check the available classifiers, although sometimes the best results are obtained by the simplest ones

#### Proof of Work Done
[Collecting data](https://github.com/jwladzinski/MachineLearningForSteem/blob/master/4/read_data.ipynb)
[Classification](https://github.com/jwladzinski/MachineLearningForSteem/blob/master/4/classification.ipynb)

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properties (23)

author

jacekw.dev

permlink

machine-learning-and-steem-4-account-classification-comparison-of-available-classifiers-3d-visualization-of-features-1534889167857

category

utopian-io

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created

2018-08-21 22:06:18

last_update

2018-08-28 17:17:24

depth

children

last_payout

2018-08-28 22:06:18

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1969-12-31 23:59:59

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4,708,428,413,464

root_title

"Machine learning and Steem #4: Account classification - comparison of available classifiers + 3D visualization of features"

beneficiaries

0.

`account`	steem-plus
`weight`	100

1.

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max_accepted_payout

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author_curate_reward

vote details (133)

voter	rshares	pct
noisy	1,645,372,058,166	100%
arcange	48,037,074,102	5%
steelman	8,175,157,690	100%
raphaelle	2,888,644,039	5%
lukmarcus	32,377,944,131	30%
warofcraft	29,677,340,245	20%
azizbd	4,143,642,176	4%
yuxi	3,034,997,324	10%
eroche	46,403,594,827	100%
lovenfreedom	3,775,202,359	10%
miniature-tiger	54,256,443,849	50%
mys	26,225,872,436	25%
jakipatryk	55,576,174,662	100%
diogogomes	512,531,078	99%
paulag	31,247,810,636	24%
luigi-tecnologo	990,076,932	4%
grecki-bazar-ewy	10,875,487,920	50%
rafalski	27,450,454,459	66%
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astromaniak	18,799,694,280	50%
greenorange	605,432,593	100%
piotr-galas	4,464,482,455	100%
amosbastian	25,801,191,200	38.56%
tdre	6,354,888,439	100%
kryptojanusz	16,810,313,465	100%
grzesiekb	146,776,363,300	100%
hallmann	27,086,337,648	80%
annaburska	47,302,699,075	100%
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jjay	854,401,658	100%
didymos	1,503,918,001	100%
slabiak.com	551,407,442	100%
marcon	12,929,625,267	100%
pkocjan	10,204,165,914	100%
legko	239,472,820	1%
nervi	11,004,281,742	100%
romualdd	4,398,429,676	100%
pomocnik	349,224,023	100%
herbacianymag	1,284,114,516	100%
statsexpert	3,501,443,701	60%
flocki	1,098,650,991	100%
leancenter	519,320,401	100%
rozku	2,190,083,772	100%
ciekawski	442,477,657	100%
matkodurko	20,189,146,975	100%
acbka	488,534,590	100%
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igorik	484,040,551	100%
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rattyopera	482,995,802	100%
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telephonewhy	498,184,349	100%
queensstar	482,995,802	100%
somalianfog	507,297,477	100%
bolttrite	482,998,521	100%
butlervault	495,149,427	100%
bezkresu	1,208,512,740	80%
shamefulhasty	492,108,930	100%
insidescraper	482,998,521	100%
diplute	482,998,521	100%
bloodsiege	495,146,640	100%
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theexcelclub	15,560,604,278	100%
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ptaku	2,838,031,522	80%
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@matkodurko · Aug 23 '18

Nice!! I'm about to defend my master thesis which is all about ML and Scikit-learn :) Btw isn't SteemSQL paid service?

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@jacekw.dev · Aug 23 '18

Thanks. Yes, SteemSQL is paid service, but definitely worth it, because at the moment it's the best database :)

properties (22)

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@paulag · Aug 24 '18

really cool.  I have been looking at at way to use decision trees concepts ( entropy and information gain) on steemit data to calculate a user contribution score.  Been working on it a while now.

I love the visualizations.  Wish I had time to actually try out this tutorial.  really appreciate your efforts here.  Nice work

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@paulag · Aug 27 '18

send you a dm on discord, hoping you will touch base with me

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@portugalcoin · Aug 22 '18

$4.45

Thank you for your contribution.

- Please put comments in your code.For example:

```
//Short descript of scatter_plot_3d function
def scatter_plot_3d(d, x, y, z):
    fig = plt.figure(figsize=(10, 10))
    ax = Axes3D(fig)
    ax.scatter(d[x], d[y], d[z], c=d['class'], cmap=plt.cm.Accent, edgecolor='k', s=80)
   // what will do this function ax.set_xlabel(),ax.set_ylabel(), ax.set_zlabel() 
    ax.set_xlabel(x)
    ax.set_ylabel(y)
    ax.set_zlabel(z)
   // what will do this function ax.legend
    ax.legend([label(color(150, 210, 150)), label(color(247, 192, 135)),
               label(color(235, 8, 144)), label(color(102, 102, 102))],
              ['content-creator', 'scammer', 'comment-spammer', 'bid-bot'], numpoints = 1)
```

<br/>

Your contribution has been evaluated according to [Utopian policies and guidelines](https://join.utopian.io/guidelines), as well as a predefined set of questions pertaining to the category.

To view those questions and the relevant answers related to your post, [click here](https://review.utopian.io/result/8/11113313).

---- 
Need help? Write a ticket on https://support.utopian.io/. 
Chat with us on [Discord](https://discord.gg/uTyJkNm). 
[[utopian-moderator]](https://join.utopian.io/)

👍 utopian-io, espoem, favcau, amosbastian, mightypanda, crokkon, zapncrap, curx, mops2e

`author`	portugalcoin
`permlink`	re-jacekwdev-machine-learning-and-steem-4-account-classification-comparison-of-available-classifiers-3d-visualization-of-features-1534889167857-20180822t125118112z
`category`	utopian-io
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properties (23)vote details (9)

voter	rshares	pct
crokkon	2,213,715,170	4%
espoem	16,080,401,446	15%
utopian-io	3,096,428,052,396	4.45%
favcau	13,762,611,579	25%
zapncrap	2,204,035,963	5%
amosbastian	10,459,942,378	15.77%
curx	1,973,969,227	5%
mightypanda	9,404,890,718	50%
mops2e	339,728,176	10%

@utopian-io · Aug 24 '18

$0.02

Thank you for your review, @portugalcoin!

So far this week you've reviewed 20 contributions. Keep up the good work!

👍 espoem, mops2e

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properties (23)vote details (2)

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@steem-ua · Aug 22 '18

Hi @jacekw.dev! We are @steem-ua, a new Steem dApp, computing UserAuthority for all accounts on Steem. We are currently in test modus upvoting quality Utopian-io contributions! Nice work!

properties (22)

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`json_metadata`	"{"app": "beem/0.19.54"}"
`created`	2018-08-22 13:28:06
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`beneficiaries`	`[]`
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`percent_hbd`	10,000
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@steemitboard · Aug 29 '18

Congratulations @jacekw.dev! You have completed the following achievement on Steemit and have been rewarded with new badge(s) :

[![](https://steemitimages.com/70x80/http://steemitboard.com/notifications/payout.png)](http://steemitboard.com/@jacekw.dev) Award for the total payout received

<sub>_Click on the badge to view your Board of Honor._</sub>
<sub>_If you no longer want to receive notifications, reply to this comment with the word_ `STOP`</sub>



> Do you like [SteemitBoard's project](https://steemit.com/@steemitboard)? Then **[Vote for its witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1)** and **get one more award**!

properties (22)

`author`	steemitboard
`permlink`	steemitboard-notify-jacekwdev-20180829t031005000z
`category`	utopian-io
`json_metadata`	{"image":["https://steemitboard.com/img/notify.png"]}
`created`	2018-08-29 03:10:03
`last_update`	2018-08-29 03:10:03
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`root_title`	"Machine learning and Steem #4: Account classification - comparison of available classifiers + 3D visualization of features"
`beneficiaries`	`[]`
`max_accepted_payout`	1,000,000.000 HBD
`percent_hbd`	10,000
`post_id`	69,659,865
`net_rshares`	0

@steemitboard · Aug 31 '18

Congratulations @jacekw.dev! You have completed the following achievement on Steemit and have been rewarded with new badge(s) :

[![](https://steemitimages.com/70x80/http://steemitboard.com/notifications/votes.png)](http://steemitboard.com/@jacekw.dev) Award for the number of upvotes

<sub>_Click on the badge to view your Board of Honor._</sub>
<sub>_If you no longer want to receive notifications, reply to this comment with the word_ `STOP`</sub>



> Do you like [SteemitBoard's project](https://steemit.com/@steemitboard)? Then **[Vote for its witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1)** and **get one more award**!

properties (22)

`author`	steemitboard
`permlink`	steemitboard-notify-jacekwdev-20180831t230914000z
`category`	utopian-io
`json_metadata`	{"image":["https://steemitboard.com/img/notify.png"]}
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`root_title`	"Machine learning and Steem #4: Account classification - comparison of available classifiers + 3D visualization of features"
`beneficiaries`	`[]`
`max_accepted_payout`	1,000,000.000 HBD
`percent_hbd`	10,000
`post_id`	69,944,733
`net_rshares`	0

@steemitboard · Oct 18 '18

Congratulations @jacekw.dev! You have completed the following achievement on the Steem blockchain and have been rewarded with new badge(s) :

[![](https://steemitimages.com/70x80/http://steemitboard.com/notifications/votes.png)](http://steemitboard.com/@jacekw.dev) Award for the number of upvotes

<sub>_Click on the badge to view your Board of Honor._</sub>
<sub>_If you no longer want to receive notifications, reply to this comment with the word_ `STOP`</sub>



**Do not miss the last post from @steemitboard:**
<table><tr><td><a href="https://steemit.com/steemitboard/@steemitboard/steemitboard-ranking-update-steem-power-followers-and-following-added"><img src="https://steemitimages.com/64x128/https://cdn.steemitimages.com/DQmfRVpHQhLDhnjDtqck8GPv9NPvNKPfMsDaAFDE1D9Er2Z/header_ranking.png"></a></td><td><a href="https://steemit.com/steemitboard/@steemitboard/steemitboard-ranking-update-steem-power-followers-and-following-added">SteemitBoard Ranking update - Steem Power, Followers and Following added</a></td></tr></table>

> Support [SteemitBoard's project](https://steemit.com/@steemitboard)! **[Vote for its witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1)** and **get one more award**!

properties (22)

`author`	steemitboard
`permlink`	steemitboard-notify-jacekwdev-20181018t085002000z
`category`	utopian-io
`json_metadata`	{"image":["https://steemitboard.com/img/notify.png"]}
`created`	2018-10-18 08:50:03
`last_update`	2018-10-18 08:50:03
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`children`	0
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`root_title`	"Machine learning and Steem #4: Account classification - comparison of available classifiers + 3D visualization of features"
`beneficiaries`	`[]`
`max_accepted_payout`	1,000,000.000 HBD
`percent_hbd`	10,000
`post_id`	73,538,175
`net_rshares`	0

@steemitboard · Oct 26 '18

Congratulations @jacekw.dev! You have completed the following achievement on the Steem blockchain and have been rewarded with new badge(s) :

<table><tr><td>https://steemitimages.com/60x70/http://steemitboard.com/@jacekw.dev/votes.png?201810251239</td><td>You made more than 700 upvotes. Your next target is to reach 800 upvotes.</td></tr>
</table>

<sub>_[Click here to view your Board of Honor](https://steemitboard.com/@jacekw.dev)_</sub>
<sub>_If you no longer want to receive notifications, reply to this comment with the word_ `STOP`</sub>



**Do not miss the last post from @steemitboard:**
<table><tr><td><a href="https://steemit.com/steemitboard/@steemitboard/steemitboard-notifications-improved"><img src="https://steemitimages.com/64x128/http://i.cubeupload.com/NgygYH.png"></a></td><td><a href="https://steemit.com/steemitboard/@steemitboard/steemitboard-notifications-improved">SteemitBoard notifications improved</a></td></tr><tr><td><a href="https://steemit.com/steemitboard/@steemitboard/steemitboard-ranking-update-resteem-and-resteemed-added"><img src="https://steemitimages.com/64x128/https://cdn.steemitimages.com/DQmfRVpHQhLDhnjDtqck8GPv9NPvNKPfMsDaAFDE1D9Er2Z/header_ranking.png"></a></td><td><a href="https://steemit.com/steemitboard/@steemitboard/steemitboard-ranking-update-resteem-and-resteemed-added">SteemitBoard Ranking update - Resteem and Resteemed added</a></td></tr></table>

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@utopian-io · Aug 23 '18

Hey @jacekw.dev
 **Thanks for contributing on Utopian**.
We’re already looking forward to your next contribution!

**Want to chat? Join us on Discord https://discord.gg/h52nFrV.**

<a href='https://v2.steemconnect.com/sign/account-witness-vote?witness=utopian-io&approve=1'>Vote for Utopian Witness!</a>

properties (22)