Links to all other posts in a structured way. Table of contents.
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.
Recommendation systems using associate mining rules