Links to all other posts in a structured way. Table of contents.
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.
Forecasting sales of new products using Bass model. Calculating p, q and m for iPhone sales using gradient descent. Cool visualizations and code provided.
Customer Lifetime value and steady state retention probability using Markov chains. Markov chains, steady state, homogeneity and Anderson− Goodman test and CLT explained. Used data from UCI m/c learning repository.
A multi period integer programming model was used to predict when and by how much quantity a new purchase has to be done in a Kirana store.