## Recommendation systems

Recommendation systems using associate mining rules

Skip to content
# Author: Achyuthuni Sri Harsha

## Recommendation systems

## Hypothesis test for population parameters

## Table of Contents

## Why are basics important?

## Multicollinear analysis

## Multivariate Analysis

## Handling Google maps location data

## Class size paradox

## Univariate Analysis on in-time

## Donut chart using pure CSS and HTML

and machine learning

A graduate in mechanical engineering from Amrita Univesity, data scientist and worked on front end UI.

Recommendation systems using associate mining rules

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

Links to all other posts in a structured way. Table of contents.

Explains why basics are important using a simple example.

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

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

Getting traffic, vehicle used, location and journey time from Google Maps. Integrating these factors for in-time problem.

Explanation of class size paradox using Amrita University placement data. Contains reusable R code for web scraping.

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

Last week time spent ■ Cleaning data ■ Formulating hypothesis ■ EDA ■ Actual analytics One of the ways of plotting numerical proportions in statistics is by using the donut chart. In the above example, the time I spent working on a problem in the last week is shown. Although I recommend using D3, PlotlyContinue reading Donut chart using pure CSS and HTML