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 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 for Machine Learning Data Cleaning, Feature Selection, and Data Transforms in Python …so What is Data Preparation? Predictive modeling projects involve learning from data. Data refers to examples or cases from the domain that characterize the problem that you want to solve. On a predictive modeling project, such as classification or regression, raw […]
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 […]
Thanks for asking, I’m flattered. I have some research degrees and I loved research then and love it now. But I don’t think I’m good at it. In fact, I like reading and figuring out what others have learned perhaps more than devising my own research program. Perhaps I’m more engineer and scholar than academic […]
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 […]
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 sophisticated approach involves defining […]
Often, the input features for a predictive modeling task interact in unexpected and often nonlinear ways. These interactions can be identified and modeled by a learning algorithm. Another approach is to engineer new features that expose these interactions and see if they improve model performance. Additionally, transforms like raising input variables to a power can […]
