## Artificial Neural Network – Part 1

First among the series of blogs on ANN. Implemented ‘AND-gate’ logic as an ANN from scratch.

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# Author: Achyuthuni Sri Harsha

## Artificial Neural Network – Part 1

## Part and partial correlation

## 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

and machine learning

A passionate decision scientist. Ponders over how to use data science to solve everyday problems.

First among the series of blogs on ANN. Implemented ‘AND-gate’ logic as an ANN from scratch.

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.

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.