# Predicting Car Price using Machine Learning

In this post, we will learn Linear Regression and real time challenges during implementation for a business problem.

## Problem Description:

There is an automobile company XYZ from Japan which aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US…

# Employee Retention using Data Science

Hiring and retaining top talent is an extremely challenging task that requires capital, time and skills. Small business owners spend 40% of their working hours on tasks that do not generate any income such as hiring process for new employees.

# Customer Clustering using DBSCAN

In this blog, we will learn about one of my favorite clustering algorithm and that is the DBSCAN algorithm. We will first understand the theory then I will demonstrate the working of the DBSCAN with the help of a very simple example.

The DBSCAN stands for density based spatial clustering…

# How Data distributions are used?

One of the most basic term used in statistical analysis is Random variable. There are multiple concepts like Probability Density Function, Cumulative Distribution function related to random variable.

We have also a very popular distribution concept i.e Gaussian distribution or Normal distribution and it has few properties like 68–95–99.7.

All…

# Applications of Correlation

How to use correlations?

As we know correlation is all about relationship between two random variables X and Y, and there are multiple ways of measuring correlation like Pearson correlation coefficient, Spearman’s rank correlation coefficient, etc.

Now you might wonder what the applications of correlation are. …

# Correlation doesn’t imply Causation

## The idea of correlation is sometime misinterpreted with Causation

There is a very famous statement in statistics, which is Correlation doesn’t imply Causation. Let’s try to understand this statement with below chart example.

This chart has two random variables X and Y.

First random variable X is chocolate consumption per capita, which means what is the average amount of…

# Pearson correlation coefficient

In my previous blog, we learnt about Covariance to measure relationship between two random variables.

As Covariance has limitation to quantify the relationship, there is another concept called Pearson correlation coefficient (PCC) that overcome this limitation. It’s often represented with the Greek alphabet ρ. So the Pearson correlation coefficient between…

# Understanding Correlation using Covariance

In this blog, we’ll try to understand how to measure relationships between random variables.

Let me explain what I mean by relationships.

Suppose I have a random variable X, which takes values of student’s heights in a class and another variable Y, which takes weights of students. … ## Tarique Akhtar

Data Science Professional, Love to learn new things!!!We can get connected through LinkedIn (https://www.linkedin.com/in/tarique-akhtar-6b902651/)