Need For Principal Component Analysis (PCA)Machine Learning in general works wonders when the dataset provided for training the machine is large and concise. Usually having a good amount of data lets us build a better predictive model since we have more data to train the machine with. However, using a large data set has its own pitfalls. The biggest pitfall is the curse of dimensionality. It turns out that in large dimensional datasets, there might be lots of inconsistencies in the features or lots of redundant features in the dataset, which will only increase the computation time and make data processing and EDA more convoluted. To get rid of the curse of dimensionality, a process called dimensionality reduction was introduced. Dimensionality reduction techniques can be used to filter only a limited number of significant features needed for training and this is where PCA comes in. What Is Principal Component Analysis (PCA)? Principal components analysis (PCA) is a dimensionality reduction technique that enables you to identify correlations and patterns in a data set so that it can be transformed into a data set of significantly lower dimension without loss of any important information.The main idea behind PCA is to figure out patterns and correlations among various features in the data set. On finding a strong correlation between different variables, a final decision is made about reducing the dimensions of the data in such a way that the significant data is still retained. Such a process is very essential in solving complex data-driven problems that involve the use of high-dimensional data sets. PCA can be achieved via a series of steps. Let’s discuss the whole end-to-end process.