PinnedNitish Kumar ThakurinAnalytics VidhyaLess known Applications of k-Means Clustering — Dimensionality Reduction, Anomaly Detection and…Distance of data points from the cluster centers can be used as engineered features. This allows us to heavily reduce the number of…7 min read·Jul 9, 2021----
PinnedNitish Kumar ThakurinTowards Data ScienceAnomaly Detection in Python — Part 2; Multivariate Unsupervised Methods and CodeIn this article, we will discuss Isolation Forests and One Class SVM to perform Multivariate Unsupervised Anomaly Detection along with code14 min read·May 22, 2021----
Nitish Kumar ThakurinAnalytics VidhyaAnomaly Detection in Python — Part 1; Basics, Code and Standard Algorithms11 min read·May 11, 2021--4--4
Nitish Kumar ThakurinAnalytics VidhyaPrincipal Component Analysis (PCA)— Part 1 — Fundamentals and ApplicationsPCA works due to Linear dependence(Collinearity) among features. Linear Dependence, forces data to lie along lower dimensional Planes.8 min read·Feb 17, 2021----
Nitish Kumar ThakurinAnalytics VidhyaRidge Regression: Regularization FundamentalsRegularization is a method used to reduce the variance of a Machine Learning model; in other words, it is used to reduce overfitting…8 min read·Jun 14, 2020----
Nitish Kumar ThakurinAnalytics VidhyaComparison of Initialization strategies for k-Meansk-Means is a data partitioning algorithm which is among the most immediate choices as a clustering algorithm. Some reasons for the…6 min read·Apr 11, 2020--1--1