If you are someone who thinks “how many stars should I give” when you buy a product from Amazon like me, this medium article is for you.

In this project, I chose the Electronics dataset among the datasets containing the comments belonging to Amazon. I made an application by choosing the best model among the models I trained and published it on Heroku.

Now let’s get to the purpose of this project

Nowadays, the product and criticism are happening beyond the private pages and also take place in the social media space.

When I review reviews for electronic products on Amazon, most of the product reviewers have scored between…


No matter how much we are not aware of the recommendation systems in our daily life, they are services that are almost instant and can direct us when necessary. However, recommendation systems are basically created to predict what users might like, what they need and present them to users when there are many options available.

When there are too many options, they quickly present them to the user by running in the background and indexing them, rather than processing them and keeping the user waiting.

Although creating a simple recommendation system is thought to be quite simple, the difficult part…


When most people hear “Machine Learning”, they picture a robot: a deadly Terminator or a dangerous computer, which wants to destroy humanity just like in the Matrix movie.

But welcome to reality, Machine Learning is not just a futuristic fantasy; it’s already here. In fact, it has been around for decades in some specialized applications, such as movie recommendation systems in online Movie/Series Platforms.

But in my opinion, the first ML app that really became indispensable, the spam filter. It’s not exactly Skynet or Agent (from Matrix), but it does technically qualify as a Machine Learning. It has…


In this project, I will try to handle Data Analysis in a different way and if I were to open a restaurant for a good Turkish meal in Milan, where I live, I will think about where I should open it.

1. Scrap data from Wikipedia page into a DataFrame

First of all, since I had no data, I researched where I could learn the Milan regions and I could access Milan information on Wikipedia. First of all, I made this information so that I can retrieve the information through BeautifulSoup.

# Getting information about Milan
data = requests.get("https://en.wikipedia.org/wiki/Category:Districts_of_Milan").text

# creating beautifulsoup object from html
soup = BeautifulSoup(data, 'html.parser')


One of the most important issues in online shopping platforms, which have many products that users rate products based on their experiences, is scoring, and you certainly need to find an answer to questions such as how to do it.

In everyday life, many people get the highest positive rating or the lowest negative rating, etc. examines the products they will buy on the pages according to such filters. So if we were to make an evaluation, according to what and how should we follow?

There are some ways to find a rating and rate products:

1. Overall average of…


In that topic, we will consider the evaluation of the advertising methods of a large company.

The data we have includes the new advertisement proposal method of a large company and the old advertisement proposal method. Thanks to this data, the company wants to compare the old method, that is, the current method, with the new method. Here, we will look for a conclusion about which method is more successful. In this way, the company can continue with the method we decided and get more profit or Impressions/clicks.


In my previous article, I aimed to give information about how we can simply segmentation without using any machine learning method. In this article, I will try to describe the segmentation on a dataset I found from Kaggle. But this time I’ll try to look from a different perspective using K-Means.

Data Source

For sample analysis, I used the “Credit Card Dataset for Clustering” dataset available on Kaggle.

Problem Statement

We have data of about 9000 credit card holders for the last 6 months. Our job is to group these customers based on their credit card usage.

Importing Libraries and Data

### Import libraries and Data ###import…


Good marketers understand the importance of “getting to know your customer”. Marketers should follow the paradigm shift from increased CTRs (Click Through Rates) towards retention, loyalty, and customer relationships rather than just focusing on generating more clicks. Because it is easier to retain existing customers than to seek new customers

It would be quite wrong to analyze the entire customer base in the same way, engaging them with the same campaigns. Rather than segmenting the customer base into age and geographic segments, it is better to homogeneously segment groups according to their characteristics and engage them with related campaigns.

One…


How to Create Customer Profiles

Every successful sale starts with successful marketing, whether it’s word of mouth, direct sales, or an advertising campaign based on a sophisticated algorithm.

Before customers buy something, someone has to show them something. Therefore, if a system does not have enough sales in service, the marketing of that system, should be reconsidered/evaluated.


We are living with data in every step of our life. And when we discover them and convert them to graphical things, they become easier to understand. In that step, Exploratory Data Analysis (EDA) appears and helps our lives.

So, What is EDA ?

Discovery data analysis (EDA) is used by data scientists to analyze and research datasets and summarize their main features, and often uses data visualization methods.

It makes it easier for data scientists to discover patterns, detect anomalies, test a hypothesis, or check assumptions by helping determine how best to manipulate data sources to get the answers you need.EDA …

Ogulcan Ertunc

I’m a Master Student🎓pursuing Data Analytics. I’m a Data Enthusiast 💻 😃 passionate about learning and working with new tech. https://github.com/ogulcanertunc

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