# Machine Learning II

In Machine Learning I, we observed the prediction success of our models and how the process was. We continue.

Least Square Method

This method is generally used in libraries such as scikit-learn, scipy, and regression problems. Gradient descent method is not used in these libraries.
The Normal Equations method gives us an analytical solution. It is a standard regression method used to write the mathematical relationship between two interdependently varying physical quantities as an equation that is as realistic as possible.

In the Gradient Descent method, the old values of the parameters are updated with certain rates, and…

# Machine Learning I

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

## 1- Variables

It is divided into dependent and independent variables. Dependent variables can be named as a target, dependent, output, response in many pieces of literature. The dependent variable is the variable we are interested in in the data set. Often referred to as a target. It is called output in artificial neural networks. It is called dependent in statistical studies. The arguments are often called features. In much literature…

# Feature Engineering for Machine Learning

Feature engineering is the process that takes raw data and transforms it into features that can be used to create a predictive model using machine learning or statistical modeling, such as deep learning. Feature engineering aims to prepare an input data set that best fits the machine learning algorithm and enhance the performance of machine learning models. Feature engineering can help data scientists by accelerating the time it takes to extract variables from data, allowing for more variables. Automating feature engineering will help organizations and data scientists create models with better accuracy.

Here, the need for feature engineering arises. Feature…

# What Is A/B Testing?

A/B testing (also known as split testing or bucket testing) compares two versions of a web page or app against each other to determine which one performs better. AB testing is essentially an experiment where two or more variants of a page are shown to users at random. Statistical analysis is used to determine which variation performs better for a given conversion goal.

In an A/B test, you take a webpage or app screen and modify it to create a second version of the same page. This change can be as simple as a single headline or button or be…

# Hybrid Recommender System

With the development of technology, our habits are also changing. 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 recommender system, or a recommendation system, can be thought of as a subclass of information filtering system that seeks to predict the best “rating” or…

# Market Basket Analysis Using Association Rules

Association Rule was one of the first techniques used in data mining. Today, these rules are also referred to as the “Recommendation System.” It aims to make predictions about future sales by analyzing the patterns of transactions in the past and using this information. It is a rule-based machine learning technique. It reveals the rules of being together with certain probabilities. “Which products are sold together?” It helps us find the answer to the question.

Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of…

# Customer Life Time Value (CLTV)

In this article, I will discuss the ways to reveal this potential value.

This is the second and the final part of the article series about Customer Value. In the first part, I discussed the core of customer value calculation: RFM metrics and scores.

This final part will be about Customer Life Time Value prediction using BG/NBD and Gamma Gamma Models.

With RFM Analysis, a company can have a vision of its customers’ current behavior by scoring and segmenting them based on their purchasing habits and therefore be able to maintain an effective CRM.

What is CRM?

Customer relationship management…

# Customer Segmentation with RFM Analysis

Hello everyone! I am back with another article. This post will describe RFM analysis and show how to use it for customer segmentation by analyzing an online retail shop’s data set on python. Based on the results of the RFM analysis, I will exemplify what kind of actions can be taken for different kinds of customers.

# What is RFM?

RFM represents a method used for measuring customer value. An RFM analysis can show you who are the most valuable customers for your business. The ones who buy most frequently, most often, and spend the most. …

# Level Based Persona and Segmentation

## Rule-Based Classification

In this article, we will do a simple rule-based segmentation study using only pandas functions without using machine learning.

We will create new customer definitions (level-based persona) by using the user information we have and the information about the purchases made by the users. Then, we will try to predict which segment a new person will be with these customer definitions we created.

Persona can be defined as examining characteristic information obtained from different sources and the emergence of the ideal customer profile in light of this information. We can say that persona is actually an analysis process.

What is…

# What is virtual environment?

At its core, the main purpose of Python virtual environments is to create an isolated environment for Python projects. This means that each project can have its own dependencies, regardless of what dependencies every other project has.

Tools with functionality to create and manage virtual environments:

• venv (part of the standard library)
• virtualenv (widely-used)
• pipenv (high-level interface)
• conda (not only for python)

## Berkay

Data Science Enthusiast — For more information check out my LinkedIn page here: www.linkedin.com/in/berkayihsandeniz

Get the Medium app