## Recently Published

##### Handling imbalanced classes

This blog talks about handling imbalance in data for classification using different sampling methods.

##### Artificial Neural Network from scratch - part 1

Introduction to Artificial Neural Network. Comparison with natural neural network and backpropagation. ANN for And-gate built from scratch in R.

##### CART Classification

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

##### Curse of dimensionality

Explaining the curse of dimensionality using a relevant example

##### CHAID

Step by step explanation of CHAID decision trees using the titanic dataset

##### Exploratory Factor analysis

Factor analysis is discussed. 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.

##### Stationarity tests

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

##### Hierarchical Clustering

Blog on hierarchical clustering using dendogram for beer customer segmentation.

##### Stationarity introduction

Discussion about stationary, random walk, deterministic drift and other vocabulary which form as foundation to time series

##### ARIMA

ARIMA using the Box-Jenkins approach. Discussed Dickey fuller, Ljung−Box Test and KPSS tests. Built an ARIMA model from scratch and validated the same.

##### Adoption of new product - non linear programming

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

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.

##### Linear Programming

Linear programming in R along with sensitivity analysis and cool visualizations.

##### Linear regression

A complete analytical journey of linear regression. From EDA, model building, model diagnostics, residual plots, outlier treatment, co-linearity effects, transformation of variables, model re-building and validation for Boston housing price prediction problem.

##### Part and partial correlation

Understanding part (semi partial) and partial correlation coefficients in multiple regression model. Deriving the multiple R-Squared and beta coefficients from basics. Inspired from Business Analytics: The Science of Data-Driven Decision Making by Dinesh Kumar.

##### Logistic regression

Logistic regression using caret. validation using multiple tests and plots, insights, EDA and analysis. A complete analytical journey for solving classification problems.

##### KNN Imputation

Handling missing values in original mtcars data set by imputation using KNN algorithm.

##### Analysis of Variance

Anova hypothesis test to test if unemployment is similar across Bangalore. Post hoc analysis and visualizations are presented.

##### Univariate analysis

Tutorial on Univariate analysis which is the first part of EDA. Explained using in-time problem with reusable R code.

##### Multivariate analysis

Tutorial on Multivariate analysis which is the second part of EDA. Explained using in-time problem with reusable R code.

##### Web scraping in R

Explaining Class size paradox by web scraping placements data from Amrita University website.

##### Chi Square test of independence

Analyzing the safety of different types of vehicles in Bangalore by using a Chi Square test of Independence.

##### Chi-square goodness of fit test

Implementing Chi square goodness of fit test on R. Testing if a sampling distribution is Normally distributed or exponentially distributed.

##### One Sample Location test

z test and t test for .one sample location tests

##### Multicollinearity

Tutorial on Multicollinearity which is the third part of EDA. Plot of Correlation matrix and network for in-time problem with reusable code.

##### Recommendation Systems

Recommendation systems using associate mining