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 blog post, we will take a look at the activity on websites that became a significant part of development across all areas in, as well as, outside of Data Science: GitHub and StackOverflow. It doesn't matter where developers are from or what their specific focus is, everyone uses these websites.
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!
Considering all the craze around cryptocurrencies happening all around us this year, our team has also decided to dip their toes into these crypto-waters and invest a little bit of money into the hype. In this blog, we will build a simple Google Sheet based portfolio monitoring tool. It is supposed to auto-update with new values on exchange rate changes.