Principal-Component-Analysis

This notebook contains simple explanation of PCA and it's importance in data reduction to avoid multi-collinearity in given variables of data.

Overview

• Principal component analysis is a dimention reduction technique that finds the variance maximizing directions onto which to project the data.
• Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space. The input data is centered but not scaled for each feature before applying the SVD.
• Principal components are calculated to reduce variance of features and thus reducing dimentionality of variables in dataset.

Table of contents of this repo:

1. Library imports
2. Plotting continues numerical features
3. PCA Object
4. Plotting explained variance ratio
5. Plotting explained variance ratio
6. Plotting cummulative explained variance