In the first part of our blog series called Time Series Analysis we have analysed time-series visually. In the second part, we will take a look at forecasting future values from the past by Box-Jenkins methodology. Let's dive right in.
In this two part blog post, we will show you how to analyse time-series and how to forecast future values by Box-Jenkins methodology. As a testing dataset, we have chosen the "Monthly production of Gas in Australia". This dataset is available from datamarket for free. We have restricted data from the time span 1970 to 1995.
In this article, we look at 'the built environment', which Wikipedia describes as "human-made surroundings that provide the base for human activity, containing everything from parks, public transport or hospitals to coffees, bars, and restaurants." One way to do it is to explore the neighbourhoods through open data.
A 5% increase in customer retention produces more than a 25% increase in profit. It is cheaper to keep existing customers than gain new ones. Over the years, we have collected a lot of experience with churn prediction, from industries like telecommunication providers, banking or computer security. Today we want to share some of our experience with you.
It has been two years since we have started Knoyd. We have helped quite a few companies using their data more efficiently and have learnt a lot during the process. It has also been two years since we published one of our very first blog posts, analysing tweets about Star Wars: The Force Awakens. And since the new installment has hit theaters in December, we have decided to create a re-hash of this original blogpost.
In this post we will show you how to scrape reviews from an amazon product page. This data can be used to create datasets for sentiment analysis or other educational or research purposes. If you sell products on Amazon it can even be useful to analyse the reviews to understand what customers like and dislike about your product. Let's dive in!
In this post we will use modern Natural Language Processing techniques to find similar posts in a Facebook group.You have probably been in a situation where you want to post something in a Facebook group but you are not sure whether almost the same post already exists and is maybe just hiding on the next page.
In the next blog of our Knoyd series, we are going to show one of the ways of measuring impact of NBA players on the game. Everyone knows that all-stars like LeBron James or Stephen Curry have tremendous impact and they show it in every game on the floor. However, very often role players need to step up and save the game. This analysis is dedicated especially to these players and how they can change the game by their performance. The cornerstone of this analysis is the basic correlation between players‘ impact and result of the game. The way, how impact of an individual player is computed, will be described later on.
Everyone keeps talking about data; its great value, utility, and the many benefits it offers. However, to many people all those might just seem like abstract concepts. That's why we want to show you what data means in real life: data to help, data to increase efficiency, and sometimes data just to amuse. We point out use cases where data science helped someone to solve a unique problem or to solve a very old problem in a unique way. We will demonstrate that data can help individuals, communities, as well as corporations and governments. It's not a universal solution to everything, but it is right up there :)