The Support Vector Machine algorithm (or SVM) is a classification algorithm that classifies cases by separating them one from another.
The point of SVM is to separate the data into classes by finding a boundary/separator.
In SVM, data points that are on the one side from that separator belong to one class, and those points on the other side belong to another class.
One might wonder: if we want to classify unknown cases why do not use a classification algorithm that works by classifying the data points directly (such as K-Nearest Neighbors) instead of classifying them by separation?
A step-by-step complete Data Science project: from Business problem description to solution and implementation (with code). Using Foursquare API, Beautiful soup, Requests, Pandas, and Folium.
I created this project in order to complete a Capstone Project to obtain IBM Professional Data Science Certificate. I came up with this business idea because it resonates with me as a promoter of a healthy lifestyle. So I would like to share it with you step-by-step.
The article consists of the following chapters:
The K-Nearest Neighbors algorithm (or K-NN) is a classification algorithm that takes a batch of labeled points and uses them to learn how to label other points.
K-NN algorithm classifies cases based on their similarity to other cases.
In K-Nearest Neighbors, data points that are close to each other are said to be neighbors.
Similar cases with the same class labels are close to each other in the feature space. Thus, the distance between the two cases is a measure of their similarity or conversely, their dissimilarity.
Essentially, it comes down to calculating the distance between two data points.
Hi there! A while ago, I have been talking to a friend and the topic touched on Machine Learning. And by the end of the conversation, I concluded that many people have some prejudice against the whole topic of “intellectual machines”, or even might be afraid to start the learning process due to the overwhelming amount of information. So,
I decided to break the ice for everyone who is just starting out on this journey of mastering the Machine Learning.
Let us be clear — Machine Learning is not Magic. Machine Learning (so-called ML) is the study of computer algorithms…
This tutorial is meant for everyone interested in Python. Especially, for those who are just starting out and cannot break the ice from intention to action. You are here which means you are serious about learning Python and I appreciate it.
I am using Python for my work a lot. Some of the applications are scientific computing, statistics, and advanced visualization. But in my free time, I enjoy creating mini-applications for various reasons. Some of them solve a particular problem (personal, or a world large-scale problem), others are just for fun, or just something to challenge myself.
This particular lesson…
After the short introduction to clustering and the practical ideas of using it, we will go through this tutorial on K-Means Clustering from scratch in Python. I will show you how it works intuitively step by step, in a way I wish somebody showed it to me. After completing this tutorial, you will learn how to:
numpy, a basic Python library,
Imagine that we are a company that…
This article is the second part of my project I discussed previously. To recap, the idea is to create a Pomodoro app in Python from scratch. Because we all want to be effectively productive while staying at home amidst the COVID-19 pandemic.
One way to do so is to take one step at a time. Because our brain is not meant to be multitasking. In reality, it does multi-switching between different tasks.
In other words, we need to concentrate on one single activity at a time. And the Pomodoro technique can help us with it.
Previously, we built the base…
The idea behind this project is very simple yet meaningful. It boils down to the following questions:
What can I do for the community during this horrible pandemic? How can I help others to research the coronavirus?
I am going to apply my knowledge of Python to analyze a scientific paper on COVID-19 and to show you that everyone can use Python to analyze the text and get some insights out of it. This post is meant to encourage everyone to use Python in day-to-day life. …
The pandemic is hitting hard. It is necessary to limit any physical contact with the “outside” world and to stay indoors as much as possible. If you have an opportunity to work from home, it is great. However, there is a price to pay as well.
Staying efficiently productive at home is not an easy task. A home environment is not a work environment. Assuming you are not living alone, you cannot just say bye to your kids/family for the next ~8 hours and leave the house for work. Often, the workplace is the same room where you rest, eat…
Python is my favorite programming language. It is easy to get around, versatile, yet powerful. Python is a great choice for both beginners and experts. There are numerous reasons for using Python. In my opinion, one of the main reasons is an enormous amount of open-source Python libraries, packages, and frameworks.
There are many articles describing the advantages of Python. For example, as was mentioned by Mindfire Solutions in 7 Important Reasons Why You Should Use Python article: “You can use Python for developing complex scientific and numeric applications. Python is designed with features to facilitate data analysis and visualization.”
Ph.D. candidate in Astrophysics | I write about everything I find fascinating.