Experienced Ph.D. with a demonstrated history of working in the higher education industry. Skilled in Data Science, AI, Deep Learning, Big Data, & Mathematics.

This series(“Bagging & Boosting Ensemble Methods and What is the Difference Between Them?”) consists of 6 separate articles and is the fourth article in this series. In this part, we will talk about What Is the “Boosting” Ensemble Method?

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I have written about the bagging issue in my previous article. On top of that, we can see the difference between Boosting and Boosting above, but let’s try to see the system running in the background and finally share the pros and cons.

In the boosting method, all the individual models are built sequentially. …


This series(“Bagging & Boosting Ensemble Methods and What is the Difference Between Them?”) consists of 6 separate articles and is the third article in this series. In this part, we will talk about “What is the “Bagging” Ensemble Method”.

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In fact, we can see that the bagging method, as we have selected above, gives us visually what it tries to explain. But let’s try to explain the idea behind it.

The idea behind bagging is combining the results of multiple models (for instance, all decision trees) to get a generalized result.

The questions are:

  • If you create all the models…


This series(“Bagging & Boosting Ensemble Methods and What is the Difference Between Them?”) consists of 6 separate articles and is the second article in this series. In this part, we will talk about “Weak Learners & Strong Learners for Machine Learning”.

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Note: I usually will use some abbreviated words below:

  • Machine Learning — ML

Let’s start…

In both, homogeneous and heterogeneous ensemble methods we said that the individual models are called weak learners, in the homogeneous ensemble method these weak learners are built using the same ML algorithms, whereas in the heterogeneous ensemble methods these weak learners are built using…


This series(“Bagging & Boosting Ensemble Methods and What is the Difference Between Them?”) consists of 6 separate articles and is the first article in this series. In this part, we will talk about “What is Ensemble Learning?”.

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Note: I usually will use some abbreviated words below:

  • Data Science — DS
  • Machine Learning — ML

Let’s start…

In the world of ML, ensemble learning methods are the most popular topics to learn. These ensemble methods have been known as the winner algorithms. …


I tried to bring clarity to this area with the second and last part of the series on this topic. After that, when the data comes in, you will know what to do next!

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Note: I usually will use some abbreviated words below:

  • Data Science — DS
  • Exploratory Data Analysis — EDA

Then let’s go…

In the first article you have questions about how to behave when it comes to data.
1. What’s the Purpose?
2. Tidy Data Process
3. Determining and Setting Variable Types
4. Summary Statistics: Showing the Basic Structure of the Data Set

We tried to bring…


I wrote this topic in two articles without boring you. We start with the first part of the series. We got data and we did the reading with the pandas library so what are we going to do? Here we will look for the answer to this question!

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Note: I usually will use some abbreviated words below:

  • Data Science — DS
  • Exploratory Data Analysis — EDA

Let’s go…

I don’t mean a problem at the beginning of the data preparation phase, but unfortunately it is! If we talk about that subject here, we will deviate from our noble purpose.


The first part of the article was that we discussed conceptually and result-oriented, now in the second part will be technically related to the background tests and the theoretical working principles of these tests.

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Note: I usually will use some abbreviated words below:

  • Data Science — DS
  • Artificial Intelligence — AI
  • Machine Learning — ML
  • Big Data — BD

Let’s continue…

What path should be followed and what should be considered? We started by saying, under the big heading in the first article. Let’s continue from here.

4. The optimum answer to the question of how long should the measurement…


In this article, Statistical A / B test, how to choose the right methods and points to be considered in A / B tests will be mentioned and an example will be made. The first part of the article, which will be discussed in two parts, will be conceptually and result-oriented, the second part will be technically related to the background tests and the theoretical working principles of these tests.

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Note: I usually will use some abbreviated words below:

  • Data Science — DS
  • Artificial Intelligence — AI
  • Machine Learning — ML
  • Big Data — BD

Let’s start…

If A /…


Association analysis applications are among the most common applications in data science. It will also coincide as “Recommendation Systems”. I will try to explain to you this system, which works in the background of applications and sites where we live almost every moment, on an example kaggle notebook.

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It is a rule-based machine learning technique used to find patterns (relationships, structures) in the data.

Association analysis applications are among the most common applications in data science. It will also coincide as “Recommendation Systems”.

These applications may have come up in the following ways, such as “bought this product that bought…


Well, we calculated CLV, drowned in formulas or saw the result! So what’s going on now?

In this article, I have tried to address CLV as a continuation of the previous one.

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Note: I usually will use some abbreviated words below:

  • Customer Lifetime Value — CLTV or CLV
  • Lifetime Customer Value — LCV
  • Life Time Value — LTV
  • Customer Acquisition Costs — CAC
  • Key Performance Indicator — KPI

Let’s start…

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