## A Data Science Project- Part 1(b)

In the previous article (https://d4datascience.com/2016/11/10/a-data-science-project-part-1/), we have done basic data analysis like calculating means, frequency tables, summary etc. Now we will derive new variables. Why? Derived variables will help to understand more about them. For example, We have derived variable ip(derived from incomeperperson variable) which will help us to understand how many people fall in lower …

## A Data Science Project- Part 1(a)

In the earlier post, we have discussed our hypothesis and different variables impacting life expectancy. In this post, we will start digging into data. I suggest you go through codebook(check the earlier post). It is important to understand variables first before doing any analysis. I have chosen SAS as data analysis language, but you are …

## A Data Science Project-Introduction: How can we have better life expectancy!

Hi, future data science buddies! I have been asked many times how can I become data scientist or what should I do to become a data scientist.  Here I have written about some technical skills that are required to be a data scientist. How to make career into Data Science But above article was lacking …

## How to improve performance of Neural Networks

Neural networks have been the most promising field of research for quite some time. Recently they have picked up more pace. In earlier days of neural networks, it could only implement single hidden layers and still we have seen better results. Deep learning methods are becoming exponentially more important due to their demonstrated success …

## Feature Learning , Deep Learning and Machine learning

Machine learning is a very successful technology but applying it today often requires spending substantial effort hand-designing features. This is true for applications in vision, audio, and text Any machine learning algorithm performs as good as provided features are. Let’s understand this by using image classification example. When we try to classify an image into …

## A brief introduction to Outliers and Outlier Removal methods

What is an Outlier?  Simply speaking, Outlier is an observation that appears far away and diverges from an overall   pattern in a sample. Let’s take an example, we do customer profiling and find out that the average annual income of customers is \$0.8 million. But, there are two customers having annual income of \$4 and \$4.2 …

## RECOMMENDER SYSTEMS 101

Have you ever given a thought how e-commerce websites show you products with “Customer who bought this also bought this” or how Netflix recommends movies based on your interest or how facebook discovers “Person you may know” list? Let’s look at the below pictures: Amazon: “When you buy a book” Netflix: “Other Movies You Might …

## Introduction To Lasso Regression

Lasso regression analysis is a shrinkage and variable selection method for linear regression models. The goal of lasso regression is to obtain the subset of predictors that minimizes prediction error for a quantitative response variable. The lasso does this by imposing a constraint on the model parameters that causes regression coefficients for some variables to …

## Boosting Performance of Machine Learning Models

People often get stuck when they are asked to improve the performance of predictive models. What usually they do is try different algorithms and check their results. But often they end up not improving the model. Today I will walk you through what we can do to improve our models. You can build a predictive …

## Difference between Usual Machine Learning and Deep Learning Explained!

As wikipedia defines: “Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[2] Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions,[3]:2 rather than following strictly static program instructions.” i.e. you collect a …

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