k-Nearest Neighbors, or k-NN as I am going to call it from now on, is one of the easiest algorithms to solve classification tasks. It can be used for regression problems as well, but I am going to focus on the more common use case of classification in this post. In a nutshell, k-NN will assign a new data point to the class that the majority of its k neighbours in the training set belong to....
Have you ever seen some cool applications of computer vision tools, like this the one below? Perhaps your phone’s camera can autofocus on faces, or maybe you have uploaded a photo on a social media platform and it automatically recognized the person on the image? These are facial recognition applications and they all rely on Machine Learning. In this post, we are going to use a very easy package called OpenCV to build our own facial recognition program!...
I recently moved to a new city - Munich! I live in a very calm area, but soon realized that the neighbourhood is not really the best when it comes to eating outside. So, I decided to try to analyse review data from the web to find out which area is most compelling for me and other foodies. I scraped online reviews, cleaned the data and then visualized it on a map, showing the average rating of restaurants in different areas in Munich....
Web Scraping is the automated process of extracting data from websites. This is commonly done by retrieving the HTML code of a website through a request and then extracting the information hidden in the HTML code programmatically. This is especially convenient when there is no API available to you! There has been a lot of discussion going on about the legality and ethics of Web Scraping, which I do not want to get into in this article....
The k-means algorithm is used to divide unlabeled data into categories or classes, in order to draw useful conclusions from the resulting clusters. Let’s take a look at an imaginary dataset of n = 18 observations of different coffee brands. Note that we would never actually use the k-means algorithm on such a small data set. We plot the price of the coffee vs. the rating obtained by customers: As coffee drinkers, we might be interested in finding certain clusters in this data, so that we might purchase the best coffee we can afford with a given budget....