Some say over 60-70% time is spent in data cleaning, munging and bringing data to a suitable format such that machine learning models can be applied on that data. This post focuses on the second part, i.e., applying machine learning models, including the preprocessing steps. The pipelines discussed in this post come as a result of over a hundred machine learning competitions that I've taken part in. It must be noted that the discussion here is very general but very useful and there can also be very complicated methods which exist and are practised by professionals. Before applying the machine learning models, the data must be converted to a tabular form.

XGBoost is an implementation of gradient boosted decision trees designed for speed and performance that is dominative competitive machine learning. In this post you will discover how you can install and create your first XGBoost model in Python. How to Develop Your First XGBoost Model in Python with scikit-learn Photo by Justin Henry, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python. Assuming you have a working SciPy environment, XGBoost can be installed easily using pip.

Machine learning and deep learning models, like those in Keras, require all input and output variables to be numeric. This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model. The two most popular techniques are an integer encoding and a one hot encoding, although a newer technique called learned embedding may provide a useful middle ground between these two methods. In this tutorial, you will discover how to encode categorical data when developing neural network models in Keras. How to Encode Categorical Data for Deep Learning in Keras Photo by Ken Dixon, some rights reserved. A categorical variable is a variable whose values take on the value of labels.

The quality of data and the amount of useful information are key factors that determine how well a machine learning algorithm can learn. Therefore, it is absolutely critical that we make sure to encode categorical variables correctly, before we feed data into a machine learning algorithm. In this article, with simple yet effective examples we will explain how to deal with categorical data in computing machine learning algorithms and how we to map ordinal and nominal feature values to integer representations. The article is an excerpt from the book Python Machine Learning – Third Edition by Sebastian Raschka and Vahid Mirjalili. This book is a comprehensive guide to machine learning and deep learning with Python.