The Ultimate Guide to 12 Dimensionality Reduction Techniques (with Python codes)
Dimensionality reduction Techniques : PCA, Factor Analysis, ICA, t-SNE, Random Forest, ISOMAP, UMAP, Forward and Backward feature selection.
PCA
Visualize and Perform Dimensionality Reduction in Python using Hypertools
Hypertools is a python library designed to implement dimensionality reduction-based visual explorations of datasets with high dimensions.
40 Must know Questions to test a data scientist on Dimensionality Reduction techniques
Introduction Have you come across a dataset with hundreds of columns and wondered how to build a predictive model on it? Or have come …
40 Interview Questions asked at Startups in Machine Learning / Data Science
Overview Contains a list of widely asked interview questions based on machine learning and data science The primary focus is to learn machine learning …
8 Proven Ways for improving the “Accuracy” of a Machine Learning Model
Introduction Enhancing a model performance can be challenging at times. I’m sure, a lot of you would agree with me if you’ve found yourself stuck …
8 Ways to deal with Continuous Variables in Predictive Modeling
Introduction Let’s come straight to the point on this one – there are only 2 types of variables you see – Continuous and Discrete. …
Beginners Guide To Learn Dimension Reduction Techniques
Introduction Brevity is the soul of wit This powerful quote by William Shakespeare applies well to techniques used in data science & analytics as well. …
