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Principle Component Analysis in Regression

We will apply pca on wine dataset wine = read.csv ("https://storage.googleapis. com/dimensionless/ Analytics/wine.csv") Applying PCA on relevant predictors pca<-prcomp(wine[,3:7],scale=TRUE) Analyzing components of the output #Std Dev...
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MULTI-VARIATE ANALYSIS

WHY DO MULTI-VARIATE ANALYSIS Every data-set comprises of multiple variables, so we need to understand how the multiple variables interact with each other. After we understand uni-variate analysis - where we understand the behaviour of each distribution, and...
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Introduction to Random forest

Random forest is one of those algorithms which comes to the mind of every data scientist to apply on a given problem. It has been around for a long time and has successfully been used for such a wide number of tasks that it has become common to think of it as a basic...
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Introduction to Support Vector Machine

A Support Vector Machine is a yet another supervised machine learning algorithm. It can be used for both regression and classification purposes. But SVMs are more commonly used in classification problems (This post will focus only on classification). Support Vector machine is also commonly known as “Large Margin Classifier”.
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Introduction to XGBoost

In the arsenal of Machine Learning algorithms, XGBoost has its analogy to Nuclear Weapon. It has recently been very popular with the Data Science community. Reason being its heavy usage in winning Kaggle solutions.
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