Machine-Learning-for-DS

1.“Curse of Dimensionality”: It is the phenomena that arise during analyzing and organizing data in high-dimensional spaces that do not fit in low-dimensional settings such as the three-dimensional physical space. Here, Dimension refers to the “Features”. If the number of features increases exponentially, the ML model gets confused and may not be able to observe all the information/patterns due to sparse data and hence, the accuracy of the model decreases after the particular threshold value of the number of samples. K-Nearest Neighbour (KNN) technique performs clustering efficiently in low dimensional spaces while it fails in higher dimensional spaces resulting in the Curse of Dimensionality. To overcome this situation, a Dimensional Reduction Technique called “Principal Component Analysis” is used. With the help of Python Program using Number of Samples and Different Dimension, we can visualize the data using Histogram. As the number of dimensions increases the histogram plot becomes far apart from each other.