What is the Paris Agreement?

The Paris Agreement signed in 2015 on climate change mitigation, adaptation and financing under the United Nations Framework Convention on Climate Change (UNFCCC) and entered into force in 2016.

The United Nations Framework Convention on Climate Change is the first intergovernmental, international environmental agreement signed by the United Nations on global warming. The Convention aims to carry out studies to minimize the impact of environmental pollution on climate change and the negative effects of harmful gases such as greenhouse gases in the atmosphere. It entered into force on 21 March 1994.

On the same date, the “Convention on Combating Desertification” and the “Convention on Biological Diversity” were also accepted.

Why is the Paris Agreement Important?

We said that the Paris Agreement entered into force in 2016. As of March 2021, 191 member states of the United Nations Framework Convention on Climate Change (UNFCCC) are party to it. There are 6 member states that have not ratified the agreement: Eritrea, Iran, Iraq, Libya, Yemen and Turkey. Our country has signed the contract but has not been a party to the contract. In terms of harmful gases released into the atmosphere among these 6 countries, Iran and Turkey are the countries that are among the top 20 sources of emission.

In addition, the United States withdrew from the Paris Climate Agreement in 2020 under Trump. Trump argued that it would not be fair for the United States to limit carbon emissions while countries such as India and China use fossil fuels as a reason for withdrawing from the contract. The USA, the country that emits the most greenhouse gases into the world’s atmosphere, was the only country to withdraw from the contract. The new administration, which came to Joe Biden, made an application to the United Nations for America to rejoin the convention. Thus, the United States officially returned to the convention by informing the United Nations.

The long-term temperature target of the Paris Agreement is to limit global average temperature rise to 2°C (3.6°F) increase from pre-industrial levels, and even 1.5°C is an effort. Because it is accepted that limiting the temperature increase to 1.5 instead of 2°C will significantly reduce the risks and effects of climate change in terms of risks and impacts.

Paris Agreement; It aims to reduce carbon emissions, use renewable energy sources by keeping up with climate change, and lead developed countries to other countries. With the Paris Agreement, each country must work, plan and report on a regular basis to minimize global warming and climate change.

Turkey’s Statement of National Contribution

In Turkey’s Intended National Contribution Statement submitted to the United Nations, it was stated that the total greenhouse gas emissions, which was 430 million tons in 2012, could increase to 929 million tons in 2030 with reduction measures. In other words, Turkey did not commit to reduce greenhouse gas emissions, saying that it could more than double it.” In our country, it is important to carry out the necessary studies in the field of climate change.

Humanity and countries need to work on climate change, which is the most important problem in today’s world, by showing the necessary devotion. The world is our world, it is in our hands to take good care of it… Rather than seeing a world whose color and harmony have faded; Let’s aim to see a colorful, vibrant world.

You can also access the Kyoto Protocol signed in the field of climate change here.

Orhan Yağızer Çınar

References

https://en.wikipedia.org/wiki/Paris_Agreement

What is PISA? PISA Sınavı Nedir?

PISA (Programme for International Student Assessment) yani Uluslararası Öğrenci Değerlendirme Programı.

PISA, OECD (Organisation for Economic Co-operation and Development) yani Ekonomik Kalkınma ve İşbirliği Örgütü tarafından 1997 yılında geliştirilen üçer yıllık dönemler halinde, 15 yaş grubundaki öğrencilere uygulunan beceri, yetkinlik ve bilgi düzeyinin ölçüldüğü uluslararası bir araştırmadır.

PISA çalışmasının amacı eğitimde standartlaştırmayı ve gelişmeyi arttırmakla birlikte dünyadaki öğrencilerin başarısını karşılaştırmak ve test etmektir.

PISA’da 15 yaş grubundaki öğrencilerin, matematik okuryazarlığı, fen bilimleri okuryazarlığı ve okuma becerileri hakkında veriler toplanıyor. Bunun yanında öğrencilerin motivasyonları, kendileri hakkındaki görüşleri, öğrenme biçimleri, okullarındaki eğitim ortamları vb. alanlarda da veriler toplanmakta.

PISA Kimler Tarafından Yürütülüyor?

PISA, OECD (Ekonomik Kalkınma ve İşbirliği Örgütü) tarafından yürütülen bir eğitim araştırmasıdır. Araştırmada kullanılan testlerin ve anketlerin geliştirilmesi, analizlerinin yapılması, uluslararası raporların hazırlanması gibi işlemler PISA Yönetim Kurulu tarafından belirlenen konsorsiyum ile yürütülüyor.

PISA’nın ulusal düzeyde çeviri ve uyarlamasını ise ulusal merkezler gerçekleştiriyor.

PISA Hangi Okullara Uygulanıyor?

Araştırmaya katılan ülkelerde, örgün öğretime devam eden 15 yaş öğrencilerin bulunduğu bütün okullar bu araştırmaya katılabilir. Ülkemizdeki (Ortaokul, Anadolu Lisesi, Fen Lisesi, Sosyal Bilimler Lisesi, Anadolu Güzel Sanatlar Lisesi, Spor Lisesi, Anadolu İmam Hatip Lisesi, Mesleki ve Teknik Anadolu Lisesi, Çok Programlı Anadolu Lisesi) öğrencileri PISA’ya katılabilir.

Peki, Türkiye PISA Sınavında Dünyada Kaçıncı Sırada?

En son yapılan PISA sınavına göre- 3 Aralık 2019’da açıklandı- Türkiye’deki öğrenciler ortalamanın altında kalarak 37 OECD ülkesi içinde 31. oldu. Ülkemizde 186 farklı okuldan 6 bin 890 öğrenci PISA testine girdi.

Öğrencilerimiz, okuma becerilerinden 466, matematik okuryazarlığından 454, fen bilimleri okuryazarlığından ise 468 puan aldı.

Öğrencilerimiz Başarılı Mı?

PISA sınavının sonuçlarında görüldüğü gibi ülkemiz, eğitim konusunda sınıfta kalıyor ve acilen eğitim alanında reaksiyona geçmek gerekiyor. 37 OECD ülkesi içerisinde 31. sırada olan bir ülke dünyanın en güçlü 10 ekonomisine girmeyi bırakın, dünyanın en güçlü 20 ekonomisine bile giremez. Eğer reaksiyona geçmeyip gerekli çalışmaları yapmazsak, gün geçtikçe ülkemiz kendini daha da geri sıralarda bulabilir.

PISA hakkında yararlanabileceğiniz kaynakları aşağıya bırakıyorum. Ayrıca değerli Erhan Erkut Hocam’ın ve ekonomist Emin

Öğrencilerimiz Başarılı Mı?

PISA sınavının sonuçlarında görüldüğü gibi ülkemiz, eğitim konusunda sınıfta kalıyor ve acilen eğitim alanında reaksiyona geçmek gerekiyor. 37 OECD ülkesi içerisinde 31. sırada olan bir ülke dünyanın en güçlü 10 ekonomisine girmeyi bırakın, dünyanın en güçlü 20 ekonomisine bile giremez. Eğer reaksiyona geçmeyip gerekli çalışmaları yapmazsak, gün geçtikçe ülkemiz kendini daha da geri sıralarda bulabilir.

PISA hakkında yararlanabileceğiniz kaynakları aşağıya bırakıyorum. Ayrıca değerli Erhan Erkut Hocam’ın ve ekonomist Emin Çapa’nın PISA hakkındaki videolarını bırakıyorum.


https://tr.wikipedia.org/wiki/Uluslararas%C4%B1_%C3%96%C4%9Frenci_De%C4%9Ferlendirme_Program%C4%B1

https://en.wikipedia.org/wiki/Programme_for_International_Student_Assessment

https://www.sozcu.com.tr/2019/egitim/pisa-nedir-pisa-neleri-olcuyor-olcum-kriterleri-neler-5486149/

What is Post-Truth?

What is post-truth for you? Post-truth selected the word of the year 2016 by the Oxford Dictionary.

Post-truth is an adjective that is defined as the situation where the objective and immutable facts are less effective than feelings and personal opinions in determining public opinion on a particular issue.

Modern Truth/ Post-truth/ Truth

Post-truth is a philosophical and political concept for “the disappearance of shared objective standards for truth” and the “circuitous slippage between facts or alternative facts, knowledge, opinion, belief, and truth”.

So Why was Post-Truth Chosen Word of the Year?

The word post-truth is first used during the Brexit referendum. The increase in usage starts during the US presidential election in 2016 and is causing a boom in the USA. The most commonly used forms are “post-truth, politics” and “post-factual politics”. What we call “post-truth politics” is, in short, a period in which truths, facts and facts lose their importance.

Frequency of word use
Frequency of Word Use — oxfordlearnersdictionaries.com

With the events I mentioned in 2016, post-truth has ceased to be a technical term and phenomenon and has been used as a current in the media.

History of Post-truth

The post prefix in the word post-truth is used not to mean “what happens after an event or the event”, contrary to its general usage, but to “belong to a time when the concept it comes before is considered unimportant or unnecessary”.

So, when we say “post-truth politics”, we are talking about “a period / time when truths, facts and facts lose their importance”.

Post-truth is mentioned for the first time in an article written in 1992 by American playwright Steve Tesich in The Nation magazine. In the periods before this writing, post-truth has been used in its literal sense, and it has generally been used to mean “after the truth is understood and the truth is revealed”.

Post-truth became widespread with writer Ralph Keyes’ “The Post-Truth Era” published in 2004. With this book, the term “post-truth” has exploded.

Besides, some people regard Friedrich Nietzsche as one of the pioneers for the concept of post-truth. Nietzsche argues that “people created the concepts by which they define good and justice, thus replacing the concept of truth with the concept of the value and basing reality on human will and will.” In his 1873 essay Truth and Lying in an Extra-Moral Sense, he argues that people create the truth about the world using metaphors, myths, and poetry.

There are many thinkers, writers and philosophers who talk about the concept of post-truth in world history. In addition, various theories for post-truth have been signed in the past.

In summary, the concept of Post-Truth is now new realities that have been imposed on us in almost every aspect of our lives, since every individual with access to the Internet has become a content producer.


Thank you for reading my article so far, I hope it has been useful.

Orhan Yağızer Çınar

Continue reading “What is Post-Truth?”

Finland: The Country of White Lilies

About Grigory Petrov

This biographical novel, The Land of White Lilies, was penned by the Russian Grigoriy Spiridonovich Petrov. Grigoriy Petrov was born on January 26, 1866 in Yamburg, a small town in Petersburg. He briefly describes himself as “the son of a middle-class person, and in his childhood a shepherd.” “He is the son of a tavern or a kiosk, and he heard nothing but swearing in his childhood and never saw anyone except drunk people.”

Petrov, who started his education at the Narva High School, settled in Petersburg and then graduated first from the seminary school and then from the theological academy in 1891. The young and talented priest gradually became known as a wanted and respected preacher in the capital. Petrov, who has literary talent as well as his rhetoric skills, was written in a simple and at the same time exciting style and was read by different segments of the public. In 1907, when his reputation grew the most, Grigoriy Petrov was accused of “spreading duties that contradicted the teachings of the Orthodox Church and contained disrespectful approaches to the authority chosen by God and appointed. At the end of this accusation, Petrov was imprisoned in the monastery. After receiving his title, a ban was imposed on entering and living in Moscow and Petersburg for seven years. Since 1908, Petrov had to live in Finland and sometimes in Crimea, while constantly changing his residence. Until the revolution years He lived under constant police surveillance during his period. In 1920, when White Army troops began to leave Crimea, at the end of 1920, Petrov had to leave his homeland hastily. It is known that his wife, who was very afraid of the danger of being imprisoned and persecuted by the Bolsheviks, took this step as a result of long persuasion efforts. Petrov, who came to Istanbul by ferry, then went to the refugee camp in Gallipoli and from there to Trieste in Italy. He was invited to Belgrade by the government of Yugoslavia after the Yugoslavs who read his articles communicated with him. Thus, Petrov’s second, emigrant life began. The Petrov family was able to get together at the end of 1923. Up to that time, Petrov, who lived in Belgrade, mostly wrote articles for newspapers, while his books were published in Serbian and Bulgarian languages. His old and devoted friend, Dinyo Bojkov, translated these texts into Bulgarian and ensured that they were published. Bojkov wrote about 50 books by Petrov in his memoirs. He says he is publishing.

“In the Land of White Lilies” is one of Petrov’s last books. Immediately after the completion of the book in 1923, its Serbian edition was published. Before Petrov had the opportunity to see this book, it was determined that he had an incurable cancer. He passed away on 18 June 1925 at the Maison de Santé clinic near Paris after the surgery he was taken. Petrov’s tomb is located today in the Ostfriedhof Cemetery in Munich.

Finland and Grigory Petrov

It focuses on Finland in the Land of White Lilies. The book tells the story of raising this small country, which is located between swamps and rocks, which is poor in natural resources, from poverty and transforming into a politically, economically and culturally developed welfare society. “Why Finland?” he may ask. It is not difficult to explain this. Petrov knew Finland much better than any other country, because he had lived in this country for a long time and traveled all over him. But the other, perhaps the most important reason, is the sincere admiration, warmth and closeness he feels towards Finland.

Reality lies at the heart of the White Lilies legend, as the main picture of Finland’s historical development was accurately drawn by Petrov. Real events constitute the basis of what is said about the national awakening of the Finnish people and the sincere and devoted struggle of thousands of Finns for the development of their country.

WHITE LILIES IN TURKEY

The book, which was translated into Turkish from Bulgarian, took its place in bookstores in Istanbul in 1928. In those years, the modernization process led by Mustafa Kemal Atatürk was going on in the country, which was in a period of breaking. It is not known how Petrov’s book got into the hands of Atatürk. But after reading the book, Atatürk ordered that all educational institutions in the country, especially military schools, be included in the curriculum. Turkish officers have read the book “In the Land of White Lilies”, which is accepted as a guide in the “renewal of life” studies in their country, as a compulsory source work for many years. The book reached at least 16 editions in Turkey, each edition ranging from 12 thousand to 25 thousand. In the preface of one of the editions, it is stated that the book titled “In the Land of White Lilies” is the most read book among the books published in contemporary Turkish in Turkey.

Get comfortable with being uncomfortable

Today I watched a TED video who the speaker is Luvvie Ajayi Jones.

She said -as a writer, speaker, and entrepreneur, she mentions that we should always defend the truth and that justice must be provided.

Says that keeping quiet is comfortable, and anything in life should be initiated without fear like being a domino.

For her, being the domino looks like public speaking and she suggests that we always speak and defend what we are right in life without fear, without paying attention to anything, without giving in to power.

And finally, she said telling the truth should not be a revolutionary act. So it’s our job, it’s our obligation, it’s our duty to speak truth to power, to be the domino.

Thanks to everyone who reads.

Who is Orhan Yagizer Cinar?

Hello everyone, I am Orhan Yagizer. I am a 18 years old, science high school student from Gaziantep, Turkey.

I study Mathematics, Biology, Chemistry, Physics and English. Because I have been interested in these fields since my childhood. I have also been interested in technology for a long time. I also work in this area.

So what am I doing, what am I trying to do with this blog? As I said before, I’m interested in technology, especially artificial intelligence and data science. I was sharing my work in this area on different sites, but I wanted it to be my own site.

I will share my technology studies and articles I prepared for this blog. Usually my work will be artificial intelligence, machine learning, data science, deep learning areas. Also, I’m planning to share my technology, science and art articles.

Let me talk a little bit about my future dreams and goals. I want to go abroad for my undergraduate study. I want to study artificial intelligence with Computer Science. After I receive my education, I want to start a startup and I want to work on the problems in the world situations. Especially, I want to work on the 17 Sustainable Development Goals of the United Nations Development Programme.

I constantly work and learn new things because I have a goal of constantly improving myself and learning new things.

Let me talk about the activities and organizations I took part in:

Yetkin Gençler (YetGen), Istanbul, Turkey
Cultivation Program, May 2021 – July 2021

  • 12-week 21st century competencies training program
  • Establish enterprise with the focus of the UN Sustainable Development Goals

GelecektekiSen, Istanbul, Turkey
High School Advisory Board, Apr 2021 – present

  • Education plan for high school students, organizing summer camps for them, creating teamwork

Harrington Housing, Toronto, Canada
Booking Assistant, Feb 2021 – present

  • Providing accommodation for students, communicating with them and direct them

Clarusway, Virginia, USA
Data Science Trainee, May 2020 – present

  • Inclusive education in data science
  • Python, SQL, IT Fundamentals, Git, HTML, CSS, Database, Tableau, Spreadsheet, Machine Learning, Deep Learning, Statistics, NLP

Young Leaders Over The Horizon (YOOOTH), Ankara, Turkey
Leader, Jan 2020 – present

  • Working with high school and university students,
  • Our goals, in the framework of our concept ” Soft Skills + Hard Skills = Smart Skills “, is to be able to grow ourselves as versatile, conscientious and well-equipped, multi-talented, double-winged, 21st century literate and a real global citizen. 
  • Organizing YOOOTH Talks with important and valuable people.
  • Content creation for students

This is how I can introduce myself, I continue to work on this challenging road to my dreams and goals.

Logistic Regression Algorithm Analysis with Python

Hi, everyone. I am Orhan Yagizer. In this article, I will work with the Logistic regression algorithm in python. Let’s get start it.

Firstly, what is a logistic regression algorithm?

Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression).

In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. Each object is detected in the image would be assigned a probability between 0 and 1, with a sum of one.

As I mentioned above, logistic regression appears everywhere in our lives, that’s why it’s important to learn it and know it.

What are the differences between linear regression and logistic regression?

Sometimes these two algorithms can be confused with each other.

Linear regression is used to predict the continuous dependent variable using a given set of independent variables. It is used for solving the Regression problem. In Linear regression, we predict the value of continuous variables.

On the other hand, Logistic Regression is used to predict the categorical dependent variable using a given set of independent variables. Logistic regression is used for solving classification problems. In logistic Regression, we predict the values of categorical variables.

Logistic Regression Analysis with Python

Now it’s time to analyze them in python. I will mostly use sci-kit learn. I will use the Titanic data set from Kaggle. It’s a very famous ML data set. You can download the data set from here.

Firstly, we will import the necessary libraries.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Let’s start by reading in the Titanic data set file into a pandas dataframe. My file’s name is “titanic_train.csv”. Then check the dataframe’s head.

train = pd.read_csv('titanic_train.csv')
train.head()

Let’s begin some exploratory data analysis. We’ll start by checking out missing data. We can use seaborn to create a simple heatmap to see where we are missing data.

plt.figure(figsize=(10,6))
sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap="Greens")

Almost one-fourth of age data is missing. Also, look at the cabin column, it looks like we are just missing too much of that data. We’ll have to drop these columns.

Let’s continue by visualizing some more of the data.

sns.set_style('whitegrid')
sns.countplot(x='Survived',data=train,palette='pastel')

According to the chart above, most people couldn’t survive. Now let’s look at survivors by sex.

sns.set_style('whitegrid')
sns.countplot(x='Survived',hue='Sex',data=train,palette='RdBu_r')

As you can see, most male people didn’t survive the sinking of the Titanic. Approximately 220 female, 110 male survived.

Let’s look at survivors by class column.

sns.set_style('whitegrid')
sns.countplot(x='Survived',hue='Pclass',data=train,palette='viridis')

As you can see, more people survived in class 1. Whereas more people died in class 3.

Let’s look at our age column.

plt.figure(figsize=(10,7))
sns.distplot(train["Age"].dropna(),kde=False,bins=30);

As you can see, most people’s age between 20 and 30.

Now, we should make data cleaning. I want to fill in missing age data instead of just dropping the missing age data rows. We will create a function for it. I will fill in missing data with average age values.

plt.figure(figsize=(12, 7))
sns.boxplot(x='Pclass',y='Age',data=train,palette='viridis')
Boxplot
def trans_age(cols):
    Age = cols[0]
    Pclass = cols[1]
    
    if pd.isnull(Age):
        
        if Pclass == 1:
            return 37 
        elif Pclass == 2:
            return 29
        else:
            return 24
    else:
        return Age

Now, apply the function.

train['Age'] = train[['Age','Pclass']].apply(trans_age,axis=1)

Also, we should drop the cabin column due to missing data.

train.drop('Cabin',axis=1,inplace=True)
train.head()

Now check the heatmap again.

plt.figure(figsize=(10,6))
sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap="Greens")

As you can see, we don’t have any missing values anymore.

Before the modelling process, we should convert categorical data to dummy variables. We should get_dummies for it. Our categorical data is the sex and embarked column. Then after the dummy process, we can drop the original categorical columns.

sex = pd.get_dummies(train['Sex'],drop_first=True)
embark = pd.get_dummies(train['Embarked'],drop_first=True)
train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)

Now we should concatenate our dataframes. Then we’ll check the train’s head.

train = pd.concat([train,sex,embark],axis=1)
train.head()

Our data is ready for modelling. Now, we can split our data training set and test set. Our target column is Survived. We will try to predict Survived column.

I’ll use sci-kit learn. So, you should import sci-kit learn.

from sklearn.model_selection import train_test_split
X = train.drop('Survived',axis=1)
y = train['Survived']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=42)

Then, we should train and predict the data with a logistic regression algorithm. For this, we should import linear_model from sci-kit learn.

from sklearn.linear_model import LogisticRegression
logmodel = LogisticRegression()
logmodel.fit(X_train,y_train)

We train and fit our data. Now we can make a prediction.

predictions = logmodel.predict(X_test)

Let’s move on to evaluate our model. We can check precision, recall,f1-score using a classification report and confusion matrix. For this, we should import metrics from sci-kit learn.

from sklearn.metrics import classification_report, confusion_matrixprint(confusion_matrix(y_test,predictions))
print("\n")
print(classification_report(y_test,predictions))

Here is the result of our model. It’s not bad, the predictions are fine. But in the normal world data, our modelling process isn’t that easy. We should more EDA on normal world data.

Today, we analyzed the logistic regression algorithm with the Titanic data set in Python.

I hope, you enjoy my article and it will be useful for you. Thanks for reading!

Orhan Yağızer Çınar

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