The tale of missing values in Python – Towards Data Science

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Imagine buying a chocolate box with 60 chocolate samples where there are 15 different unique shapes of chocolates. Unfortunately, on opening the chocolate box, you find two empty segments of chocolate. Can you accurately find a way out off handling the missing chocolate segments. Should one just pretend as if the missing chocolate isn't missing.? Should one return the chocolate box to the seller? Should one go and buy two other chocolates to fill the missing portion. Or can one just predict the shape of the missing chocolate based on previous experience of arrangement and shapes of chocolate in the box and then buy a chocolate of such predicted shape.


Doing Data Science: A Kaggle Walkthrough – Cleaning Data

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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. When we talk about cleaning data, what exactly are we talking about? Missing data in general is one of the trickier issues that is dealt with when cleaning data.


How to Handle Missing Data with Python - Machine Learning Mastery

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Data can have missing values for a number of reasons such as observations that were not recorded and data corruption. Handling missing data is important as many machine learning algorithms do not support data with missing values. In this tutorial, you will discover how to handle missing data for machine learning with Python. Note: The examples in this post assume that you have Python 2 or 3 with Pandas, NumPy and Scikit-Learn installed, specifically scikit-learn version 0.18 or higher. How to Handle Missing Values with Python Photo by CoCreatr, some rights reserved.


Machine Learning Walkthrough Part One: Preparing the Data

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Cleaning and preparing data is a critical first step in any machine learning project. In this blog post, Dataquest student Daniel Osei's takes us through examining a dataset, selecting columns for features, exploring the data visually and then encoding the features for machine learning. This post is based on a Dataquest'Monthly Challenge', where our students are given a free-form task to complete. After first reading about Machine Learning on Quora in 2015, Daniel became excited at the prospect of an area that could combine his love of Mathematics and Programming. After reading this article on how to learn data science, Daniel started following the steps, eventually joining Dataquest to learn Data Science with us in in April 2016.


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Data science is an immensely powerful tool in our data-driven world. Call me idealistic, but I believe this tool should be used for more than getting people to click on ads or spend more time consumed by social media. Not only do we get to improve our data science skills in the most effective manner - through practice on real-world data - but we also get the reward of working on a problem with social benefits. The full code is available as a Jupyter Notebook both on Kaggle (where it can be run in the browser with no downloads required) and on GitHub. This is an active Kaggle competition and a great project to get started with machine learning or to work on some new skills. The Costa Rican Household Poverty Level Prediction challenge is a data science for good machine learning competition currently running on Kaggle.