Data preparation is an important part of a predictive modeling project. Correct application of data preparation will transform raw data into a representation that allows learning algorithms to get the most out of the data and make skillful predictions. The problem is choosing a transform or sequence of transforms that results in a useful representation […]
Data preparation is the transformation of raw data into a form that is more appropriate for modeling. It is a challenging topic to discuss as the data differs in form, type, and structure from project to project. Nevertheless, there are common data preparation tasks across projects. It is a huge field of study and goes […]
Data Preparation for Machine Learning Crash Course. Get on top of data preparation with Python in 7 days. Data preparation involves transforming raw data into a form that is more appropriate for modeling. Preparing data may be the most important part of a predictive modeling project and the most time-consuming, although it seems to be […]
Data preparation is the process of transforming raw data into learning algorithms. In some cases, data preparation is a required step in order to provide the data to an algorithm in its required input format. In other cases, the most appropriate representation of the input data is not known and must be explored in a […]
Datasets may have missing values, and this can cause problems for many machine learning algorithms. As such, it is good practice to identify and replace missing values for each column in your input data prior to modeling your prediction task. This is called missing data imputation, or imputing for short. A popular approach to missing […]
Data preparation is the process of transforming raw data into a form that is appropriate for modeling. A naive approach to preparing data applies the transform on the entire dataset before evaluating the performance of the model. This results in a problem referred to as data leakage, where knowledge of the hold-out test set leaks […]
Predictive modeling machine learning projects, such as classification and regression, always involve some form of data preparation. The specific data preparation required for a dataset depends on the specifics of the data, such as the variable types, as well as the algorithms that will be used to model them that may impose expectations or requirements […]
Data preparation may be one of the most difficult steps in any machine learning project. The reason is that each dataset is different and highly specific to the project. Nevertheless, there are enough commonalities across predictive modeling projects that we can define a loose sequence of steps and subtasks that you are likely to perform. […]
On a predictive modeling project, machine learning algorithms learn a mapping from input variables to a target variable. The most common form of predictive modeling project involves so-called structured data or tabular data. This is data as it looks in a spreadsheet or a matrix, with rows of examples and columns of features for each […]
Machine learning models 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 Ordinal Encoding and a One-Hot Encoding. In this tutorial, you will discover how […]
