# Multicollinearity, Regularization, Lasso, Ridge and Polynomial Regression

## Multicollinearity

Generally occurs a high correlation between two or more independent variables. This can be implied widely in the regression model. In realtime, the data might be having collinearity properties. Before applying any model the collinearity needs to be rectified or else it will lead to a false result and lesser accuracy.

Consider daily activities to explain better about multicollinearity.Tom usually likes sweet. He enjoyed sweet while watching television. How can we determine Tom happiness rating? This can be raised in two ways while watching TV and eating sweets. Those two variables are correlated to one another. …

# Applying Multiple Linear Regression in house price prediction

Multiple linear regression refers to a statistical technique that is used to predict the outcome of a variable based on the value of two or more variables. It is sometimes known simply as multiple regression, and it is an extension of linear regression. The variable that we want to predict is known as the dependent variable, while the variables we use to predict the value of the dependent variable are known as independent or explanatory variables.

Multiple linear regression is used to estimate the relationship between two or more independent variables and one dependent variable. …

# A Walkthrough of Linear Regression

What is Regression Analysis?

Regression is the process of predicting the dependent variable based on independent variables in the hand. For example, predicting the revenue for next year in a retail company based on the sales of the products / How much the sales achieved throughout the globe? / Customer satisfaction growth / Any loss in sales? , all those parameters takes in place to prediction. So, this can be predicted by using Linear Regression model. Let’s see in detail.

Linear Regression

To determine the linear relationship between the dependent and independent variables. …

# Data Science vs Machine Learning vs Artificial Intelligence

In today’s modern world the people will be keen to search those buzz words on the internet. Certainly as Machine Learning, Data Science, Artificial Intelligence.Seems you have chased it too. Well, My article gives you a better understanding.

# Introduction to Statistics (Part-IV)

Z Test

Z Test is a statistical procedure used to test an alternative hypothesis against a null hypothesis.

Z test is any statistical hypothesis used to determine whether two samples means are different when variances are known and the sample is large (n>=30)

Formula to find the value of z-test is

# Introduction to Statistics (Part-III)

Why You Should Perform Statistical Hypothesis Testing?

Hypothesis testing is a form of inferential statistics that allows us to draw conclusions about an entire population based on a representative sample. You gain tremendous benefits by working with a sample. In most cases, it is simply impossible to observe the entire population to understand its properties. The only alternative is to collect a random sample and then use statistics to analyze it.

What is a Hypothesis Statement?

If you are going to propose a hypothesis, it’s customary to write a statement. Your statement will look like this:
“If I…(do this to an independent variable)….then (this will happen to the dependent variable).” …

# Introduction to Statistics (Part-II)

Standard Deviation

Standard deviation tells us about the concentration of data around the mean of the data set. In simple words, we can say how much data is deviated from the mean. Its symbol is σ (the Greek letter sigma). It is the square root of the Variance. So now you ask, “What is the Variance?”

# Introduction to Statistics (Part-I)

Scope of Statistics

In the real world, the data plays a huge role to participate in real-time transactions. To deal with the data we need to know How the data is structured? or How the data can be interpreted for the next level of study?. Statistics used to organize, collect and analyze the data. From the end able to study the data structure. How do the data look like? Is the data is well enough to participate in machine learning.

Statistics divided into two broad categories, namely Descriptive and Inferential.

Descriptive Statistics

It is the method of organizing and summarizing data in an informative way. Say example consider the retail company to…

# Scrap amazon products and pricing data with Heroku deployment

What is the need for scrapping?

Today’s world the data plays a vital role in every business perspective. With the help of data, we can satisfy the business requirements. The data can be any form either in Pdf, Excel, Word, webpage data. Some of the files have come in the form of handy. Where you can read and extract the data in a very simple form. In cases of reading the webpage data, How are we going to achieve it? Save the data in another form? Handwritten document based on the webpage data. All the ideas are coming to the mind. This comes to the web scrapping plays a role. From the web scrapping able to extract the data from web sites and save in another format that can be any either in XLS, PDF, CSV. Using the Python we can learn and implement the web scrapping within an hour. …

Flask is a micro web framework written in Python

What is a web framework?

Web Application Framework represents a collection of libraries and modules that enables a web application developer to write applications without having to bother about low-level details such as protocols, thread management etc.

Using the web framework, we can develop dynamic websites, web services and web applications. For each web framework having their own standard for development and deployment in server. In today’s world, there is a lot of web framework plays a vital role. We going to cover this section on Flask. … 