## Linear Programming

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

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# Category: Data Sciences

## Linear Programming

## Linear regression

## Part and partial correlation

## Logistic Regression

## KNN imputation

## Recommendation systems

## Chi Square test of independence

## Chi-Square goodness of fit test

## Analysis of variance (Anova)

## Hypothesis test for population parameters

and machine learning

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

This post deals with Chi Square test of independence. Along with the R code, a contingency table, and mosaic plot are also presented.

A brief introduction to ChiSquare goodness of fit test using attendance data. Codes for Chi-sq plots along with post-hoc Cramers V are available.

Anova hypothesis test on unemployment data. Post hoc analysis and visualisations are presented.

Discussion on hypothesis testing. Introduction to z-test and t-test, Code for visualization of z-test and t-test.