Tag - Let’s

5 Misconceptions About Data Science

Despite the massive advantages and benefits big data, machine learning and predictive analytics have to offer, data science is still a touchy subject for businesses of all sizes. Not only are many reluctant to adopt the related systems and hardware,...

A complete guide to K-means clustering algorithm

Clustering - including K-means clustering - is an unsupervised learning technique used for comments By Diego Lopez Yse, Moody's Operations LATAM. Photo by Ankush Minda on Unsplash Let’s say you want to classify hundreds (or thousands) of documents ba...

All Models Are Wrong – What Does It Mean?

During your adventures in __data science, you may have heard “all models are wrong.” Let’s unpack this famous quote to understand how we can still make models that are useful. comments By Sydney Firmin, Alteryx. “Essentially, all models are wrong, bu...

Annotated Facets with ggplot2

(This article was first published on R – Stat Bandit, and kindly contributed to R-bloggers) I was recently asked to do a panel of grouped boxplots of a continuous variable, with each panel representing a categorical grouping variable. This seems easy...

Back to returns forecasting

Neural networks for algorithmic trading. Volatility forecasting and custom loss functions Hi again! In last three tutorials we compared different architectures for financial time series forecasting, realized how to do this forecasting adequately with...

Basic Concepts of Feature Selection

Feature selection is a key part of Sponsored Post. Why should we care about Feature Selection? There is a consensus that feature engineering often has a bigger impact on the quality of a model than the model type or its parameters. Feature selection...