## CART classification

Step by step explanation of CART decision tree classification using Titanic dataset.

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# Author: Achyuthuni Harsha

## CART classification

## Curse of dimensionality

## CHAID decision trees

## K means clustering

## Exploratory factor analysis

## Stationarity tests

## Hierarchical Clustering

## Stationarity

## ARIMA

## Analytic Hierarchy Process

and machine learning

Step by step explanation of CART decision tree classification using Titanic dataset.

Explaining the curse of dimensionality using a relevant example

Step by step explanation of CHAID decision trees using the Titanic data set

K means clustering explained using customer segmentation in R. Touches on Silhouette statistic, Calinski and Harabasz index and Elbow curve.

After a brief introduction to PCA and CFA, hypothesis tests like KMO,Bartlett’s test of sphericity are introduced. In PCA, Scree plot, eigenvalues, validation and interpreting the factors is discussed.

Dickey fuller unit root test and Ljung box independence tests are discussed using attendance data set.

Blog on hierarchical clustering using dendogram for beer customer segmentation.

Discussion about stationary, random walk, deterministic drift and other vocabulary related to time series

ARIMA using the Box-Jenkins approach. Discussed Dickey fuller, Ljung−Box Test and KPSS tests. Built and validated a forecast for in-time data in attendance data.

A multi criteria decision of selecting a phone is explained using AHP.