For the last 5 years, data science has been one of the world’s hottest professions, but it is also one of the most poorly defined. This can be seen on any career website, where advertisements for ‘Data Scientist’ positions describe everything from what used to be a simple data analyst role, to technical, PhD-only, research positions working on artificial intelligence or autonomous cars.
Continue readingTag: machine learning
This article is Part VI in a series looking at data science and machine learning by walking through a Kaggle competition. If you have not done so already, you are strongly encouraged to go back and read the earlier parts – (Part I, Part II, Part III, Part IV and Part V).
Continuing on the walkthrough, in this part we build the model that will predict the first booking destination country for each user based on the dataset created in the earlier parts.
Continue reading
This article is Part V in a series looking at data science and machine learning by walking through a Kaggle competition. If you have not done so already, you are strongly encouraged to go back and read the earlier parts – (Part I, Part II, Part III and Part IV).
Continuing on the walkthrough, in this part we take the data from sessions.csv that we left aside initially and add it to the transformed and expanded data from Part IV. This part will cover, in brief, all the steps in Parts II – IV.
Continue reading
This article on data transformation and feature extraction is Part IV in a series looking at data science and machine learning by walking through a Kaggle competition. If you have not done so already, you are strongly encouraged to go back and read Part I, Part II and Part III.
Continuing on the walkthrough, in this part we focus on getting the data we cleaned in Part III ready for use in the classification algorithm. These steps are often referred to as data transformation and feature extraction.
Continue reading
This article on cleaning data is Part III in a series looking at data science and machine learning by walking through a Kaggle competition. If you have not done so already, it is recommended that you go back and read Part I and Part II.
In this part we will focus on cleaning the data provided for the Airbnb Kaggle competition.
Continue reading
This article on understanding the data is Part II in a series looking at data science and machine learning by walking through a Kaggle competition. Part I can be found here.
Continuing on the walkthrough of data science via a Kaggle competition entry, in this part we focus on understanding the data provided for the Airbnb Kaggle competition.
Continue reading
This article on understanding the data is Part I in a series looking at data science and machine learning by walking through a Kaggle competition. The other parts in this series can be found here.
In a futile attempt to shed some light on the field of Data Science, I have put together a multi-part series looking at what data science involves and some of the techniques most commonly used. This series is not intended to make everyone experts on data science, rather it is intended to simply try and remove some of the fear and mystery surrounding the field. In order to be as practical as possible, this series will be structured as a walk through of the process of entering a Kaggle competition and the steps taken to arrive at the final submission.
Continue reading