diff --git a/Gemfile.lock b/Gemfile.lock
index 83f1fe2..76edd03 100644
--- a/Gemfile.lock
+++ b/Gemfile.lock
@@ -86,7 +86,7 @@ DEPENDENCIES
tzinfo-data
RUBY VERSION
- ruby 2.4.2p198
+ ruby 2.3.3p222
BUNDLED WITH
1.16.1
diff --git a/_drafts/2017-06-11-iris-nb-click.md b/_drafts/2017-06-11-iris-nb-click.md
new file mode 100644
index 0000000..4284f64
--- /dev/null
+++ b/_drafts/2017-06-11-iris-nb-click.md
@@ -0,0 +1,171 @@
+---
+layout: post
+author: Michal Dyzma
+title: Naive Bayes classifier for Iris Data Set
+date: 2017-06-12 14:53:32 +0200
+comments: true
+mathjakx: false
+categories: python naive-bayes machine-learning
+keywords: python, naive-bayes, machine-learning
+---
+
+
+Beginning of my Machine Learning practical adventure. I intend to learn through practice. Language I chose is Python. My learning sessions will comprise of view repeatable exercises building classical data science pipeline. For this session I chose famous [__Iris Data Set__](https://archive.ics.uci.edu/ml/datasets/iris) to predict the flower class based on given attributes. Algorithm will be __Naive Bayes classifier__. When launched, command line interface will accept four numbers as an input (Petal Length, Petal Width, Sepal Length, Sepal width). Based on given numbers it will use trained model to classify unknown Iris to one of the species: _Iris setosa_, _Iris virginica_ or _Iris versicolor_.
+
+
+{% include note.html content="Source code from the article can be downloaded from this [GitHub repository](https://github.com/mdyzma/irispy)" %}
+
+This is first of many sessions, which goal is to get familiar with machine learning methods and train how to produce additional value from raw data. Each learning session will comprise of four basic exercises:
+
+1. Find data set
+2. Clean the data
+3. Choose and tune algorithm/algorithms
+4. Visualize data
+
+Sometimes I will use previously learned algorithm to do some benchmarks and compare their performance on different data sets.
+
+## Naive Bayes
+
+“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiate the two classes very well (look at the below snapshot).
+
+## Project structure
+
+Basic project structure is:
+
+{% highlight bash %}
+.
+├── .gitignore
+├── features
+│ ├── environment.py
+│ ├── iris.feature
+│ └── steps
+│ └── iris_steps.py
+├── irisvmpy
+│ ├── __init__.py
+│ ├── iris.py
+│ └── test_iris.py
+├── LICENSE
+└── setup.py
+{% endhighlight %}
+
+## Setting pipeline
+
+
+
+
+
+
+
+## Unit an acceptance tests
+
+{% highlight bash %}
+.
+├── features
+│ ├── environment.py
+│ ├── iris.feature
+│ └── steps
+│ └── iris_steps.py
+├── irisvmpy
+│ ├── __init__.py
+│ ├── iris.py
+│ └── test_iris.py
+...
+{% endhighlight %}
+
+
+## Command line interface
+
+
+
+
+__irisvmp/iris.py__
+{% highlight python %}
+import click
+
+@click.command()
+@click.option('--petal-lenght', prompt='Petal Lenght',
+ help='Unknown Iris Petal Lenght.', type=float)
+@click.option('--petal-width', prompt='Petal Lenght',
+ help='Unknown Iris Petal Width.', type=float)
+@click.option('--sepal-lenght', prompt='Petal Lenght',
+ help='Unknown Iris Sepal Lenght.', type=float)
+@click.option('--sepal-width', prompt='Petal Lenght',
+ help='Unknown Iris Sepal Width.', type=float)
+def cli(petal_lenght, petal_width, sepal_lenght, sepal_width):
+ click.echo("Iris Flower classifier\n")
+ click.echo("\nCalculating result...")
+ time.sleep(1)
+ click.echo()
+ click.echo("Your Petal Lenght is: {}".format(petal_lenght))
+ click.echo("Your Petal Width is: {}".format(petal_width))
+ click.echo("Your Sepal Lenght is: {}".format(sepal_lenght))
+ click.echo("Your Sepal Width is: {}".format(sepal_width))
+ click.echo()
+ click.echo("Your flower seems to be fine representant of:")
+ click.secho("{}".format(species), fg='green', bold=True)
+# (Petal Length , Petal Width , Sepal Length , Sepal width
+
+if __name__ == "__main__":
+ cli()
+{% endhighlight %}
+
+
+
+## Packaging
+
+
+__setu.py__
+{% highlight python %}
+import codecs
+try:
+ codecs.lookup('mbcs')
+except LookupError:
+ ascii = codecs.lookup('ascii')
+ func = lambda name, enc=ascii: {True: enc}.get(name=='mbcs')
+ codecs.register(func)
+
+from setuptools import setup, find_packages
+
+
+requirements = [
+ 'scipy', 'numpy', 'scikit-learn', 'Click'
+]
+
+test_requirements=[
+ 'behave'
+]
+
+setup(
+ name='irisvmpy',
+ version='0.0.1',
+ description='SVM classifier for iris data-set',
+ author='Michal Dyzma',
+ author_email='mdyzma@gmail.com',
+ license='MIT',
+ packages=find_packages(),
+ install_requires=requirements,
+ entry_points={
+ 'console_scripts': [
+ 'irisvmpy = irisvmpy.iris:cli',
+ ],
+ },
+ classifiers=[
+ 'Development Status :: 1 - Alpha',
+ 'License :: OSI Approved :: MIT License',
+ 'Programming Language :: Python :: 2.7',
+ 'Programming Language :: Python :: 3.6',
+ ],
+ zip_safe=False
+)
+{% endhighlight %}
+
+
+
+{% include note.html content="Source code from the article can be downloaded from this [GitHub repository](https://github.com/mdyzma/irispy)" %}
+
+
+
+
+[banner]: /assets/2017-05-12/banner.jpg
+
diff --git a/_drafts/2017-06-12-iris-lr-click.md b/_drafts/2017-06-12-iris-lr-click.md
new file mode 100644
index 0000000..906d891
--- /dev/null
+++ b/_drafts/2017-06-12-iris-lr-click.md
@@ -0,0 +1,170 @@
+---
+layout: post
+author: Michal Dyzma
+title: Logistic regression and Iris Data Set
+date: 2017-06-12 20:44:01 +0200
+comments: true
+mathjakx: false
+categories: python logistic-regression machine-learning
+keywords: python, logistic-regression, machine-learning
+---
+
+
+
+Beginning of my Machine Learning practical adventure. I intend to learn through practice. Language I chose is Python. My learning sessions will comprise of view repeatable exercises building classical data science pipeline. For this session I chose famous [__Iris Data Set__](https://archive.ics.uci.edu/ml/datasets/iris) to predict the flower class based on given attributes. Algorithm will be __Logistic regression__ classifier. When launched, command line interface will accept four numbers as an input (Petal Length, Petal Width, Sepal Length, Sepal width). Based on given numbers it will use trained model to classify unknown Iris to one of the species: _Iris setosa_, _Iris virginica_ or _Iris versicolor_.
+
+
+{% include note.html content="Source code from the article can be downloaded from this [GitHub repository](https://github.com/mdyzma/irispy)" %}
+
+This is first of many sessions, which goal is to get familiar with machine learning methods and train how to produce additional value from raw data. Each learning session will comprise of four basic exercises:
+
+1. Find data set
+2. Clean the data
+3. Choose and tune algorithm/algorithms
+4. Visualize data
+
+Sometimes I will use previously learned algorithm to do some benchmarks and compare their performance on different data sets.
+
+## Logistic regression
+
+“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiate the two classes very well (look at the below snapshot).
+
+## Project structure
+
+Basic project structure is:
+
+{% highlight bash %}
+.
+├── .gitignore
+├── features
+│ ├── environment.py
+│ ├── iris.feature
+│ └── steps
+│ └── iris_steps.py
+├── irisvmpy
+│ ├── __init__.py
+│ ├── iris.py
+│ └── test_iris.py
+├── LICENSE
+└── setup.py
+{% endhighlight %}
+
+## Setting pipeline
+
+
+
+
+
+
+
+## Unit an acceptance tests
+
+{% highlight bash %}
+.
+├── features
+│ ├── environment.py
+│ ├── iris.feature
+│ └── steps
+│ └── iris_steps.py
+├── irisvmpy
+│ ├── __init__.py
+│ ├── iris.py
+│ └── test_iris.py
+...
+{% endhighlight %}
+
+
+## Command line interface
+
+
+
+
+__irisvmp/iris.py__
+{% highlight python %}
+import click
+
+@click.command()
+@click.option('--petal-lenght', prompt='Petal Lenght',
+ help='Unknown Iris Petal Lenght.', type=float)
+@click.option('--petal-width', prompt='Petal Lenght',
+ help='Unknown Iris Petal Width.', type=float)
+@click.option('--sepal-lenght', prompt='Petal Lenght',
+ help='Unknown Iris Sepal Lenght.', type=float)
+@click.option('--sepal-width', prompt='Petal Lenght',
+ help='Unknown Iris Sepal Width.', type=float)
+def cli(petal_lenght, petal_width, sepal_lenght, sepal_width):
+ click.echo("Iris Flower classifier\n")
+ click.echo("\nCalculating result...")
+ time.sleep(1)
+ click.echo()
+ click.echo("Your Petal Lenght is: {}".format(petal_lenght))
+ click.echo("Your Petal Width is: {}".format(petal_width))
+ click.echo("Your Sepal Lenght is: {}".format(sepal_lenght))
+ click.echo("Your Sepal Width is: {}".format(sepal_width))
+ click.echo()
+ click.echo("Your flower seems to be fine representant of:")
+ click.secho("{}".format(species), fg='green', bold=True)
+# (Petal Length , Petal Width , Sepal Length , Sepal width
+
+if __name__ == "__main__":
+ cli()
+{% endhighlight %}
+
+
+## Packaging
+
+
+__setu.py__
+{% highlight python %}
+import codecs
+try:
+ codecs.lookup('mbcs')
+except LookupError:
+ ascii = codecs.lookup('ascii')
+ func = lambda name, enc=ascii: {True: enc}.get(name=='mbcs')
+ codecs.register(func)
+
+from setuptools import setup, find_packages
+
+
+requirements = [
+ 'scipy', 'numpy', 'scikit-learn', 'Click'
+]
+
+test_requirements=[
+ 'behave'
+]
+
+setup(
+ name='irisvmpy',
+ version='0.0.1',
+ description='SVM classifier for iris data-set',
+ author='Michal Dyzma',
+ author_email='mdyzma@gmail.com',
+ license='MIT',
+ packages=find_packages(),
+ install_requires=requirements,
+ entry_points={
+ 'console_scripts': [
+ 'irisvmpy = irisvmpy.iris:cli',
+ ],
+ },
+ classifiers=[
+ 'Development Status :: 1 - Alpha',
+ 'License :: OSI Approved :: MIT License',
+ 'Programming Language :: Python :: 2.7',
+ 'Programming Language :: Python :: 3.6',
+ ],
+ zip_safe=False
+)
+{% endhighlight %}
+
+
+
+{% include note.html content="Source code from the article can be downloaded from this [GitHub repository](https://github.com/mdyzma/irispy)" %}
+
+
+
+
+[banner]: /assets/2017-05-12/banner.jpg
+
diff --git a/_drafts/2017-06-13-iris-dt-click.md b/_drafts/2017-06-13-iris-dt-click.md
new file mode 100644
index 0000000..18ba070
--- /dev/null
+++ b/_drafts/2017-06-13-iris-dt-click.md
@@ -0,0 +1,170 @@
+---
+layout: post
+author: Michal Dyzma
+title: Decision tree classifier for Iris Data Set
+date: 2017-06-13 12:12:14 +0200
+comments: true
+mathjakx: false
+categories: python decision-tree machine-learning
+keywords: python, decision-tree, machine-learning
+---
+
+
+
+Beginning of my Machine Learning practical adventure. I intend to learn through practice. Language I chose is Python. My learning sessions will comprise of view repeatable exercises building classical data science pipeline. For this session I chose famous [__Iris Data Set__](https://archive.ics.uci.edu/ml/datasets/iris) to predict the flower class based on given attributes. Algorithm will be __Decision tree__. When launched, command line interface will accept four numbers as an input (Petal Length, Petal Width, Sepal Length, Sepal width). Based on given numbers it will use trained model to classify unknown Iris to one of the species: _Iris setosa_, _Iris virginica_ or _Iris versicolor_.
+
+
+{% include note.html content="Source code from the article can be downloaded from this [GitHub repository](https://github.com/mdyzma/irispy)" %}
+
+This is first of many sessions, which goal is to get familiar with machine learning methods and train how to produce additional value from raw data. Each learning session will comprise of four basic exercises:
+
+1. Find data set
+2. Clean the data
+3. Choose and tune algorithm/algorithms
+4. Visualize data
+
+Sometimes I will use previously learned algorithm to do some benchmarks and compare their performance on different data sets.
+
+## Decision tree
+
+“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiate the two classes very well (look at the below snapshot).
+
+## Project structure
+
+Basic project structure is:
+
+{% highlight bash %}
+.
+├── .gitignore
+├── features
+│ ├── environment.py
+│ ├── iris.feature
+│ └── steps
+│ └── iris_steps.py
+├── irisvmpy
+│ ├── __init__.py
+│ ├── iris.py
+│ └── test_iris.py
+├── LICENSE
+└── setup.py
+{% endhighlight %}
+
+## Setting pipeline
+
+
+
+
+
+
+
+## Unit an acceptance tests
+
+{% highlight bash %}
+.
+├── features
+│ ├── environment.py
+│ ├── iris.feature
+│ └── steps
+│ └── iris_steps.py
+├── irisvmpy
+│ ├── __init__.py
+│ ├── iris.py
+│ └── test_iris.py
+...
+{% endhighlight %}
+
+
+## Command line interface
+
+
+
+
+__irisvmp/iris.py__
+{% highlight python %}
+import click
+
+@click.command()
+@click.option('--petal-lenght', prompt='Petal Lenght',
+ help='Unknown Iris Petal Lenght.', type=float)
+@click.option('--petal-width', prompt='Petal Lenght',
+ help='Unknown Iris Petal Width.', type=float)
+@click.option('--sepal-lenght', prompt='Petal Lenght',
+ help='Unknown Iris Sepal Lenght.', type=float)
+@click.option('--sepal-width', prompt='Petal Lenght',
+ help='Unknown Iris Sepal Width.', type=float)
+def cli(petal_lenght, petal_width, sepal_lenght, sepal_width):
+ click.echo("Iris Flower classifier\n")
+ click.echo("\nCalculating result...")
+ time.sleep(1)
+ click.echo()
+ click.echo("Your Petal Lenght is: {}".format(petal_lenght))
+ click.echo("Your Petal Width is: {}".format(petal_width))
+ click.echo("Your Sepal Lenght is: {}".format(sepal_lenght))
+ click.echo("Your Sepal Width is: {}".format(sepal_width))
+ click.echo()
+ click.echo("Your flower seems to be fine representant of:")
+ click.secho("{}".format(species), fg='green', bold=True)
+# (Petal Length , Petal Width , Sepal Length , Sepal width
+
+if __name__ == "__main__":
+ cli()
+{% endhighlight %}
+
+
+## Packaging
+
+
+__setu.py__
+{% highlight python %}
+import codecs
+try:
+ codecs.lookup('mbcs')
+except LookupError:
+ ascii = codecs.lookup('ascii')
+ func = lambda name, enc=ascii: {True: enc}.get(name=='mbcs')
+ codecs.register(func)
+
+from setuptools import setup, find_packages
+
+
+requirements = [
+ 'scipy', 'numpy', 'scikit-learn', 'Click'
+]
+
+test_requirements=[
+ 'behave'
+]
+
+setup(
+ name='irisvmpy',
+ version='0.0.1',
+ description='SVM classifier for iris data-set',
+ author='Michal Dyzma',
+ author_email='mdyzma@gmail.com',
+ license='MIT',
+ packages=find_packages(),
+ install_requires=requirements,
+ entry_points={
+ 'console_scripts': [
+ 'irisvmpy = irisvmpy.iris:cli',
+ ],
+ },
+ classifiers=[
+ 'Development Status :: 1 - Alpha',
+ 'License :: OSI Approved :: MIT License',
+ 'Programming Language :: Python :: 2.7',
+ 'Programming Language :: Python :: 3.6',
+ ],
+ zip_safe=False
+)
+{% endhighlight %}
+
+
+
+{% include note.html content="Source code from the article can be downloaded from this [GitHub repository](https://github.com/mdyzma/irispy)" %}
+
+
+
+
+[banner]: /assets/2017-05-12/banner.jpg
+
diff --git a/_drafts/2017-06-14-iris-svm-click.md b/_drafts/2017-06-14-iris-svm-click.md
new file mode 100644
index 0000000..669b128
--- /dev/null
+++ b/_drafts/2017-06-14-iris-svm-click.md
@@ -0,0 +1,171 @@
+---
+layout: post
+author: Michal Dyzma
+title: SVM classifier for Iris Data Set
+date: 2017-06-14 01:00:12 +0200
+comments: true
+mathjakx: false
+categories: python SVM machine-learning
+keywords: python, SVM, machine-learning
+---
+
+
+
+Beginning of my Machine Learning practical adventure. I intend to learn through practice. Language I chose is Python. My learning sessions will comprise of view repeatable exercises building classical data science pipeline. For this session I chose famous [__Iris Data Set__](https://archive.ics.uci.edu/ml/datasets/iris) to predict the flower class based on given attributes. Algorithm will be __SVM classifier__. When launched, command line interface will accept four numbers as an input (Petal Length, Petal Width, Sepal Length, Sepal width). Based on given numbers it will use trained model to classify unknown Iris to one of the species: _Iris setosa_, _Iris virginica_ or _Iris versicolor_.
+
+
+{% include note.html content="Source code from the article can be downloaded from this [GitHub repository](https://github.com/mdyzma/irispy)" %}
+
+This is first of many sessions, which goal is to get familiar with machine learning methods and train how to produce additional value from raw data. Each learning session will comprise of four basic exercises:
+
+1. Find data set
+2. Clean the data
+3. Choose and tune algorithm/algorithms
+4. Visualize data
+
+Sometimes I will use previously learned algorithm to do some benchmarks and compare their performance on different data sets.
+
+## Support Vector Machine
+
+“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiate the two classes very well (look at the below snapshot).
+
+## Project structure
+
+Basic project structure is:
+
+{% highlight bash %}
+.
+├── .gitignore
+├── features
+│ ├── environment.py
+│ ├── iris.feature
+│ └── steps
+│ └── iris_steps.py
+├── irisvmpy
+│ ├── __init__.py
+│ ├── iris.py
+│ └── test_iris.py
+├── LICENSE
+└── setup.py
+{% endhighlight %}
+
+## Setting pipeline
+
+
+
+
+
+
+
+## Unit an acceptance tests
+
+{% highlight bash %}
+.
+├── features
+│ ├── environment.py
+│ ├── iris.feature
+│ └── steps
+│ └── iris_steps.py
+├── irisvmpy
+│ ├── __init__.py
+│ ├── iris.py
+│ └── test_iris.py
+...
+{% endhighlight %}
+
+
+## Command line interface
+
+
+
+
+__irisvmp/iris.py__
+{% highlight python %}
+import click
+
+@click.command()
+@click.option('--petal-lenght', prompt='Petal Lenght',
+ help='Unknown Iris Petal Lenght.', type=float)
+@click.option('--petal-width', prompt='Petal Lenght',
+ help='Unknown Iris Petal Width.', type=float)
+@click.option('--sepal-lenght', prompt='Petal Lenght',
+ help='Unknown Iris Sepal Lenght.', type=float)
+@click.option('--sepal-width', prompt='Petal Lenght',
+ help='Unknown Iris Sepal Width.', type=float)
+def cli(petal_lenght, petal_width, sepal_lenght, sepal_width):
+ click.echo("Iris Flower classifier\n")
+ click.echo("\nCalculating result...")
+ time.sleep(1)
+ click.echo()
+ click.echo("Your Petal Lenght is: {}".format(petal_lenght))
+ click.echo("Your Petal Width is: {}".format(petal_width))
+ click.echo("Your Sepal Lenght is: {}".format(sepal_lenght))
+ click.echo("Your Sepal Width is: {}".format(sepal_width))
+ click.echo()
+ click.echo("Your flower seems to be fine representant of:")
+ click.secho("{}".format(species), fg='green', bold=True)
+# (Petal Length , Petal Width , Sepal Length , Sepal width
+
+if __name__ == "__main__":
+ cli()
+{% endhighlight %}
+
+
+
+## Packaging
+
+
+__setu.py__
+{% highlight python %}
+import codecs
+try:
+ codecs.lookup('mbcs')
+except LookupError:
+ ascii = codecs.lookup('ascii')
+ func = lambda name, enc=ascii: {True: enc}.get(name=='mbcs')
+ codecs.register(func)
+
+from setuptools import setup, find_packages
+
+
+requirements = [
+ 'scipy', 'numpy', 'scikit-learn', 'Click'
+]
+
+test_requirements=[
+ 'behave'
+]
+
+setup(
+ name='irisvmpy',
+ version='0.0.1',
+ description='SVM classifier for iris data-set',
+ author='Michal Dyzma',
+ author_email='mdyzma@gmail.com',
+ license='MIT',
+ packages=find_packages(),
+ install_requires=requirements,
+ entry_points={
+ 'console_scripts': [
+ 'irisvmpy = irisvmpy.iris:cli',
+ ],
+ },
+ classifiers=[
+ 'Development Status :: 1 - Alpha',
+ 'License :: OSI Approved :: MIT License',
+ 'Programming Language :: Python :: 2.7',
+ 'Programming Language :: Python :: 3.6',
+ ],
+ zip_safe=False
+)
+{% endhighlight %}
+
+
+
+{% include note.html content="Source code from the article can be downloaded from this [GitHub repository](https://github.com/mdyzma/irispy)" %}
+
+
+
+
+[banner]: /assets/2017-05-12/banner.jpg
+
diff --git a/_posts/2017-06-07-social-media-analysis-part-I.md b/_posts/2017-07-07-social-media-analysis-part-I.md
similarity index 97%
rename from _posts/2017-06-07-social-media-analysis-part-I.md
rename to _posts/2017-07-07-social-media-analysis-part-I.md
index 014e174..64fccc4 100644
--- a/_posts/2017-06-07-social-media-analysis-part-I.md
+++ b/_posts/2017-07-07-social-media-analysis-part-I.md
@@ -2,7 +2,7 @@
layout: post
author: Michal Dyzma
title: Social media analysis with Flask, Part I
-date: 2017-06-07 11:11:47 +0200
+date: 2017-07-07 11:11:47 +0200
comments: true
mathjax: false
categories: python social-media Flask TravisCI Heroku
@@ -19,8 +19,8 @@ Flask application presenting social media accounts analysis in form of dashboard
## Series consists of:
-* [Social media analysis with Flask, Part I]({{site.url}}{% post_url 2017-06-07-social-media-analysis-part-I %}) (Setting environment, flask, Travis CI/Heroku CD )
-* [Social media analysis with Flask, Part II]({{site.url}}{% post_url 2017-07-12-social-media-analysis-part-ii %}) (Templates, login/register mechanism, data storage)
+* [Social media analysis with Flask, Part I]({{site.url}}{% post_url 2017-07-07-social-media-analysis-part-I %}) (Setting environment, flask, Travis CI/Heroku CD )
+* [Social media analysis with Flask, Part II]({{site.url}}{% post_url 2017-08-12-social-media-analysis-part-ii %}) (Templates, login/register mechanism, data storage)
## Part I
@@ -906,17 +906,17 @@ To continue:
-[banner]: /assets/2017-06-07/banner.png
-[tw_create_app]: /assets/2017-06-07/twitter-create-app.png
-[tw_settings]: /assets/2017-06-07/twitter-app-settings.png
-[tw_tokens]: /assets/2017-06-07/twitter-app-tokens.png
-[github_issues]: /assets/2017-06-07/github-issues.png
-[hello_flask]: /assets/2017-06-07/hello-flask.png
-[h_pipeline]: /assets/2017-06-07/heroku-pipeline.png
-[h_empty]: /assets/2017-06-07/heroku-empty-app.png
-[h_first]: /assets/2017-06-07/heroku-first-deployment.png
-[h_conn_gh]: /assets/2017-06-07/heroku-connect-github.png
-[travis_ci]: /assets/2017-06-07/connect-github-travis-ci.png
-[travis_board]: /assets/2017-06-07/travis-ci-project.png
-[travis_log]: /assets/2017-06-07/travis-ci-fail-report.png
-[travis_green]: /assets/2017-06-07/travis-ci-green.png
+[banner]: /assets/2017-07-07/banner.png
+[tw_create_app]: /assets/2017-07-07/twitter-create-app.png
+[tw_settings]: /assets/2017-07-07/twitter-app-settings.png
+[tw_tokens]: /assets/2017-07-07/twitter-app-tokens.png
+[github_issues]: /assets/2017-07-07/github-issues.png
+[hello_flask]: /assets/2017-07-07/hello-flask.png
+[h_pipeline]: /assets/2017-07-07/heroku-pipeline.png
+[h_empty]: /assets/2017-07-07/heroku-empty-app.png
+[h_first]: /assets/2017-07-07/heroku-first-deployment.png
+[h_conn_gh]: /assets/2017-07-07/heroku-connect-github.png
+[travis_ci]: /assets/2017-07-07/connect-github-travis-ci.png
+[travis_board]: /assets/2017-07-07/travis-ci-project.png
+[travis_log]: /assets/2017-07-07/travis-ci-fail-report.png
+[travis_green]: /assets/2017-07-07/travis-ci-green.png
diff --git a/_posts/2017-07-12-social-media-analysis-part-ii.md b/_posts/2017-08-12-social-media-analysis-part-ii.md
similarity index 98%
rename from _posts/2017-07-12-social-media-analysis-part-ii.md
rename to _posts/2017-08-12-social-media-analysis-part-ii.md
index 97993bf..d42e1e5 100644
--- a/_posts/2017-07-12-social-media-analysis-part-ii.md
+++ b/_posts/2017-08-12-social-media-analysis-part-ii.md
@@ -2,7 +2,7 @@
layout: post
author: Michal Dyzma
title: Social media analysis with Flask, Part II
-date: 2017-07-12 16:11:47 +0200
+date: 2017-08-12 16:11:47 +0200
comments: true
mathjax: false
categories: python social-media Flask Jinja2 MongoDB
@@ -19,8 +19,8 @@ Flask application presenting social media accounts analysis in form of dashboard
## Series consists of:
-* [Social media analysis with Flask, Part I]({{site.url}}{% post_url 2017-06-07-social-media-analysis-part-I %}) (Setting environment, flask, Travis CI/Heroku CD )
-* [Social media analysis with Flask, Part II]({{site.url}}{% post_url 2017-07-12-social-media-analysis-part-ii %}) (Templates, login/register mechanism, data storage)
+* [Social media analysis with Flask, Part I]({{site.url}}{% post_url 2017-07-07-social-media-analysis-part-I %}) (Setting environment, flask, Travis CI/Heroku CD )
+* [Social media analysis with Flask, Part II]({{site.url}}{% post_url 2017-08-12-social-media-analysis-part-ii %}) (Templates, login/register mechanism, data storage)
## Part II
@@ -955,12 +955,12 @@ __/app/settings.py__
-[banner]: /assets/2017-07-12/banner.png
-[navbar]: /assets/2017-07-12/navbar.png
-[intro]: /assets/2017-07-12/intro.png
-[content]: /assets/2017-07-12/content.png
-[contact]: /assets/2017-07-12/contact.png
-[footer]: /assets/2017-07-12/footer.png
-[home_fdt]: /assets/2017-07-12/home-fdt.png
-[register]: /assets/2017-07-12/register.png
-[login]: /assets/2017-07-12/login.png
+[banner]: /assets/2017-08-12/banner.png
+[navbar]: /assets/2017-08-12/navbar.png
+[intro]: /assets/2017-08-12/intro.png
+[content]: /assets/2017-08-12/content.png
+[contact]: /assets/2017-08-12/contact.png
+[footer]: /assets/2017-08-12/footer.png
+[home_fdt]: /assets/2017-08-12/home-fdt.png
+[register]: /assets/2017-08-12/register.png
+[login]: /assets/2017-08-12/login.png
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