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In this article, we’ll explore the basics of two data science approaches and these relationship with machine learning.

Supervised Learning

Supervised learning is a machine learning approach that’s defined by its use of labeled datasets and it’s important to note that the data is labeled. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately.

The goal is to use the inputs to predict the values of the outputs. In supervised learning, the algorithm “learns” from the training dataset by iteratively making predictions on the data and adjusting for the correct answer. …


Photo by Alexander Shatov on Unsplash

CODEX

With the development of technology, our habits are also changing. Mostly used in digital space, eBay, Amazon, Alibaba, etc. As such, most of today’s E-Commerce sites use their own proprietary recommendation algorithms to better serve customers with the products they have to like. There are many examples such as Netflix’s movies, Spotify’s music, Facebook recommending friends, product recommendations of Amazon, etc. One of the reasons why these companies are so popular can be shown that their business structures are based on recommendation systems.

What is Recommendation System?

A recommendation engine is a filtering system that analyzes this data from heterogeneous sources belonging to different…


https://www.freepik.com/premium-vector/hand-holding-thump-up-thumbs-down-paddle_12620704.htm

Feedback and comments based on customers’ experiences are valuable sources of information. Reviews and ratings are highly effective on whether potential customers will buy the product.

The visibility of the comments is very important for the customers to find their increasing needs and get the right answers to the questions. Well, according to what we can do the order. Time? Rating? sum of Vote? … All of these variables are measurably important.

We expect rating to be higher. But we doesn’t must ignore its reliability.
ex:
up: 20 down: 2
to up: 4020 down: 870
Which scenario do we choose? or…


RFM Analizi İle Satışlarınızı Nasıl Artırırsınız?

https://www.retailreco.com/blog/rfm-analysis-for-customer-segmentation-in-ecommerce/

RFM analizini Python programlama dilini kullanarak ele alacağız. Kodlar için link ekliyor olacağım.

RFM Nedir?

RFM analizi, müşterileri segmentlere ayırmak için geçmiş satın alma davranışlarını baz alan bir müşteri segmentasyon tekniğidir.

Bu analiz ile aşağıdaki başlıkları ele alacağız;

  • Müşterimiz kim?
  • Onları kaybediyor muyuz? Nasıl kazanabiliriz?
  • Hangi müşterileriniz en karlı?
  • Hangi müşterileriniz yakın zamanda sizi ziyaret etti?
  • Hangi müşterileriniz en sadık?
  • Hangi kampanyalara hangi müşteriler nasıl tepki verdi?

RFM Açılımı

Recency — Yenilik (R) — Son satım alımdan bugüne kadar geçen süre

  • Formül = Bugünün tarihi — Son satın alma tarihi

Frequency — Sıklık (F) — Toplam satın alım sayısı

Monetary — Parasallık…

Ayse Yaman

Data Analyst | Data Science Enthusiast

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