Monday, April 19, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Data Science

Regression analysis using Python – Data Science Central

December 9, 2019
in Data Science
Regression analysis using Python – Data Science Central
588
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter

This article was written by Stuart Reid. 

 

You might also like

DSC Weekly Digest 12 April 2021

6 Limitations of Desktop System That QuickBooks Hosting Helps Overcome

Robust Artificial Intelligence of Document Attestation to Ensure Identity Theft

This tutorial covers regression analysis using the Python StatsModels package with Quandl integration. For motivational purposes, here is what we are working towards: a regression analysis program which receives multiple data-set names from Quandl.com, automatically downloads the data, analyses it, and plots the results in a new window.

 

TYPES OF REGRESSION ANALYSIS

Linear regression analysis fits a straight line to some data in order to capture the linear relationship between that data. The regression line is constructed by optimizing the parameters of the straight line function such that the line best fits a sample of (x, y) observations where y is a variable dependent on the value of x. Regression analysis is used extensively in economics, risk management, and trading. One cool application of regression analysis is in calibrating certain stochastic process models such as the Ornstein Uhlenbeck stochastic process. 

Non-linear regression analysis uses a curved function, usually a polynomial, to capture the non-linear relationship between the two variables. The regression is often constructed by optimizing the parameters of a higher-order polynomial such that the line best fits a sample of (x, y) observations. In the article, Ten Misconceptions about Neural Networks in Finance and Trading, it is shown that a neural network is essentially approximating a multiple non-linear regression function between the inputs into the neural network and the outputs.

The case for linear vs. non-linear regression analysis in finance remains open. The issue with linear models is that they often under-fit and may also assert assumptions on the variables and the main issue with non-linear models is that they often over-fit. Training and data-preparation techniques can be used to minimize over-fitting.

A multiple linear regression analysis is a used for predicting the values of a set of dependent variables, Y, using two or more sets of independent variables e.g. X1, X2, …, Xn. E.g. you could try to forecast share prices using one fundamental indicator like the PE ratio, or you could used multiple indicators together like the PE, DY, DE ratios, and the share’s EPS. Interestingly there is almost no difference between a multiple linear regression and a perceptron (also known as an artificial neuron, the building blocks of neural networks). Both are calculated as the weighted sum of the input vector plus some constant or bias which is used to shift the function. The only difference is that the input signal into the perceptron is fed into an activation function which is often non-linear.

If the objective of the multiple linear regression is to classify patterns between different classes and not regress a quantity then another approach is to make use of clustering algorithms. Clustering is particularly useful when the data contains multiple classes and more than one linear relationship. Once the data set has been partitioned further regression analysis can be performed on each class. Some useful clustering algorithms are the K-Means Clustering Algorithm and one of my favourite computational intelligence algorithms, Ant Colony Optimization.

The image below shows how the K-Means clustering algorithm can be used to partition data into clusters (classes). Regression can then be performed on each class individually.

Logistic Regression Analysis – linear regressions deal with continuous valued series whereas a logistic regression deals with categorical (discrete) values. Discrete values are difficult to work with because they are non differentiable so gradient-based optimization techniques don’t apply.

Stepwise Regression Analysis – this is the name given to the iterative construction of a multiple regression model. It works by automatic selecting statistically significant independent variables to include in the regression analysis. This is achieved either by either growing or pruning the variables included in the regression analysis.

Many other regression analyses exist, and in particular, mixed models are worth mentioning here. Mixed models is is an extension to the generalized linear model in which the linear predictor contains random effects in addition to the usual fixed effects. This decision tree can be used to help determine the right components for a model.

 

To read the whole article, with illustrations, click here.

 


Credit: Data Science Central By: Andrea Manero-Bastin

Previous Post

Dow Rally Thrills Retail Investors but Why Are Billionaires Running Scared?

Next Post

ADHA to find new CEO as Tim Kelsey steps down

Related Posts

DSC Weekly Digest 01 March 2021
Data Science

DSC Weekly Digest 12 April 2021

April 14, 2021
6 Limitations of Desktop System That QuickBooks Hosting Helps Overcome
Data Science

6 Limitations of Desktop System That QuickBooks Hosting Helps Overcome

April 13, 2021
Robust Artificial Intelligence of Document Attestation to Ensure Identity Theft
Data Science

Robust Artificial Intelligence of Document Attestation to Ensure Identity Theft

April 13, 2021
Trends in custom software development in 2021
Data Science

Trends in custom software development in 2021

April 13, 2021
Epoch and Map of the Energy Transition through the Consensus Validator
Data Science

Epoch and Map of the Energy Transition through the Consensus Validator

April 13, 2021
Next Post
Almost 300,000 Australians cancelled their My Health Record by mid-November

ADHA to find new CEO as Tim Kelsey steps down

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

Machine Learning Helps Optimize Therapeutic Antibodies
Machine Learning

Machine Learning Helps Optimize Therapeutic Antibodies

April 18, 2021
Researchers at MIT DAI Lab Have Recently Built Cardea: A Machine Learning Framework That Turns Health Care Data Into Insights
Machine Learning

Researchers at MIT DAI Lab Have Recently Built Cardea: A Machine Learning Framework That Turns Health Care Data Into Insights

April 18, 2021
Automating Drug Discovery With Machine Learning
Machine Learning

Automating Drug Discovery With Machine Learning

April 18, 2021
Twitter aims to fight bias by examining its own machine learning algorithms
Machine Learning

Twitter aims to fight bias by examining its own machine learning algorithms

April 18, 2021
Make Machine Learning Interpretable with Shapash
Machine Learning

Make Machine Learning Interpretable with Shapash

April 18, 2021
Why the Patent Classification System Needs an Update
Machine Learning

Why the Patent Classification System Needs an Update

April 18, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • Machine Learning Helps Optimize Therapeutic Antibodies April 18, 2021
  • Researchers at MIT DAI Lab Have Recently Built Cardea: A Machine Learning Framework That Turns Health Care Data Into Insights April 18, 2021
  • Automating Drug Discovery With Machine Learning April 18, 2021
  • Twitter aims to fight bias by examining its own machine learning algorithms April 18, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates