# Why Is The K-Nearest Neighbors (KNN) Called A “Lazy Algorithm”?

Understanding why K-Nearest Neighbors is called a “lazy learner”

K-Nearest Neighbors or KNN is one of the simplest machine learning algorithms. This algorithm is very easy to implement and equally easy to understand.

It is a supervised machine learning algorithm. This means we need a reference dataset to predict the category/group of the future data point.

Once the KNN model is trained on the reference dataset, it classifies the new data points based on the points (neighbors) that are most similar to it. …

# K-Nearest Neighbors (KNN) Algorithm For Machine Learning

## Understanding the fundamental working of the KNN algorithm

K-Nearest Neighbors (KNN) is a classification machine learning algorithm. This algorithm is used when the data is discrete in nature.

It is a supervised machine learning algorithm. This means we need a set of reference data in order to determine the category of the future data point.

This algorithm classifies the given dataset into different groups or categories. And when a new data point is entered, the algorithm helps us to identify which group the new data point belongs to based on various similarity measures. We will learn what these similarity measures are as we progress further in this blog.

# Why Can’t We Use Linear Regression To Solve A Classification Problem?

Linear regression and Logistic regression are two of the earliest machine learning algorithms that came into existence. Both of them are supervised machine learning algorithms.

Linear regression is used when the output data is continuous in nature. While Logistic regression is used when the output data is discrete in nature.

But have you ever wondered why is that so? Why can’t we use Linear regression to solve a classification problem where the output data is discrete in nature? …

# Linear Regression Clearly Explained (Part 2)

## Understanding the fundamental maths and working of linear regression

In part 1 of this article, we tried to understand the need for linear regression and also looked at some of the real-life scenarios where linear regression can be used.

This is Part 2 and the final part of this blog.

In this article, we will dive deep into the concepts of linear regression. Understand how linear regression works and also take a look at the underlying maths behind linear regression.

# The Main Ideas Of Linear Regression

There are 3 main ideas on which…

# Linear Regression Clearly Explained (Part 1)

## Understanding the need and applications of linear regression algorithm using real-life examples.

Linear regression has been around since 1911. It is one of the core pillars of the data science and machine learning domain and is widely used in the industry to date.

This is Part 1 of the Linear regression series.

The main objective of this blog is to highlight the need for linear regression and to identify the domains where linear regression is used to solve real-world problems.

# Why Is Logistic Regression Called “Regression” If It Is A Classification Algorithm?

## The hidden relationship between linear regression and logistic regression that most of us are unaware of

Linear regression and Logistic regression are two machine learning algorithms that we all have stumbled upon during our data science journey.

But have you ever wondered, why does Logistic regression have “regression” in its name if it is a classification machine learning algorithm?

Why don’t we call it “Logistic Classification”?

In this blog, we try to demystify this question.

# 10 Keys for Successful BI Implementation

There are no specific stone-carved set of rules when it comes to implementing a BI project. Consultants and Pundits make their own list of rules which they implement in their projects. Of course, every situation is different. Companies, market situation, and personalities involved all contribute to making a BI project unique in itself. But if you combine the ideas in this list with your own knowledge of your company’s unique qualities, you will be on your way to a successful implementation and a valuable business intelligence program.

# 1. Selecting Good Key Performance Indicators (KPIs)

How does your company measure success? Seems a pretty simple question right? …

## Introduction and Basics of Business Intelligence

This blog is a primer on business intelligence. It will lay the foundation of your BI journey and will help you understand what are the fundamentals of BI and what values does it hold.

The topics that we will cover in this blog are:

2. Value Proposition of Business Intelligence

# Decisions Drive Organizations

From the CEO of a company down to the lowest levels of any organization, every minute of the day someone is making a decision that has an impact on the company’s performance. Sometimes a decision is at a very high strategic
level that affects the fate of the…

## You have come too far to say “I Quit!”

The idea of chasing your dreams and living the life of your choice comes with a fear of uncertainty.

Will my dreams ever come true?

How long will it take before my dreams turn into reality?

What if I fail and could never get back up?

When questions like these surface in our minds, it paralyzes us. These thoughts restrict us from taking the next step forward.

But logically speaking, if we don't take the next step, then how are we ever gonna reach our destination?

Achieving your dreams is like nurturing a plant. You have to sow the right…

# 4 Things To Do During Your Free Time

## Your time is limited. Use it wisely!

We live in an age where food which used to take us hours to obtain can be delivered to our doorsteps in under 30 minutes.

Never in the history of mankind have we had this much free time in our hands until now.

But the majority of us waste it!

We turn on our T.V or our gaming console. We medicate ourselves with weed, alcohol, and drugs.

What a waste!

So, what should you really be doing with all this free time?

If you are even the slightest bit serious about becoming a winner in the game of life, here…

## Ashish Mehta

I learn every day. And I share what I learn.

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