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.
Linear programming in R along with sensitivity analysis and cool visualizations.
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.
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.
A complete walk through of logistic regression. From EDA to model diagnostics with cool plots.
Handling missing values in original mtcars data set by imputation using KNN algorithm.