TY  - BOOK
AU  - Nokeri, Tshepo Chris
TI  - Data science revealed: with feature engineering, data visualization, pipeline development, and hyperparameter tuning
SN  - 9781484277362
U1  - 005.74 
PY  - 2021///
CY  - New York
PB  - Apress
KW  - Time series analysis; Logistic regression analysis; Time series analysis; Neural networks; Cluster analysis; Machine learning
N1  - Reprint, 2024; index
N2  - The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks
ER  -