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Regression


Exploring the Dow-Jones Industrial Average using Linear Regression

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. It's free, we don't spam, and we never share your email address.


Predict Ads Click - Practice Data Analysis and Logistic Regression Prediction - Projects Based Learning

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In this project we will be working with a data set, indicating whether or not a particular internet user clicked on an Advertisement. We will try to create a model that will predict whether or not they will click on an ad based off the features of that user. Welcome to this project on predict Ads Click in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id. In this project, we explore Apache Spark and Machine Learning on the Databricks platform. I am a firm believer that the best way to learn is by doing.


Regularization in Machine Learning - DataScienceCentral.com

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Hello Guys, This blog contains all you need to know about regularization. This blog is all about mathematical intuition behind regularization and its Implementation in python.This blog is intended specially for newbies who are finding regularization difficult to digest. For any machine learning enthusiast, understanding the mathematical intuition and background working is more important then just implementing the model. I am new to world of blogging so If anyone encounters any problem whether conceptual or language-related please comment below. Back in the days, when I came across regularization it became difficult for me to to get mathematical intuition behind it.


Regularization in Machine Learning - DataScienceCentral.com

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This article was written by Prashant Gupta. One of the major aspects of training your machine learning model is avoiding overfitting. The model will have a low accuracy if it is overfitting. This happens because your model is trying too hard to capture the noise in your training dataset. By noise we mean the data points that don't really represent the true properties of your data, but random chance.


Assumptions of Linear Regression -- What Fellow Data Scientists Should Know

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Linear Regression is a linear approach to modeling the relationship between a target variable and one or more independent variables. This modeled relationship is then used for predictive analytics. Working on the linear regression algorithm is just half the work done. For linear regression to work on the given data, it is assumed that Errors (residuals) follow a normal distribution. Although this is not necessarily required when the sample size is very large.


Machine Learning with Python

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We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.


Improving forecasting by learning quantile functions

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The quantile function is a mathematical function that takes a quantile (a percentage of a distribution, from 0 to 1) as input and outputs the value of a variable. It can answer questions like, "If I want to guarantee that 95% of my customers receive their orders within 24 hours, how much inventory do I need to keep on hand?" As such, the quantile function is commonly used in the context of forecasting questions. In practical cases, however, we rarely have a tidy formula for computing the quantile function. Instead, statisticians usually use regression analysis to approximate it for a single quantile level at a time.


Principal Component Regression -- Clearly Explained and Implemented

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Principal Component Regression (PCR) is a regression technique that serves the same goal as standard linear regression -- model the relationship between a target variable and the predictor variables. The difference is that PCR uses the principal components as the predictor variables for regression analysis instead of the original features. The idea is that the smaller number of principal components represents most of the variability in the data and (presumptively) the relationship with the target variable. Therefore, instead of using all the original features for regression, we only utilize a subset of the principal components. Although the assumption of a relationship with the target variable does not always hold, it is often a reasonable enough approximation to yield good results.


Machine Learning in Python

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This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyze raw real-time data, identify trends, and make predictions. The participants will explore key techniques and tools to build Machine Learning solutions for businesses. You don't need to have any technical knowledge to learn this skill. You'll start with the History of Machine Learning; Difference Between Traditional Programming and Machine Learning; What does Machine Learning do; Definition of Machine Learning; Apply Apple Sorting Example Experiences; Role of Machine Learning; Machine Learning Key Terms; Basic Terminologies of Statistics; Descriptive Statistics-Types of Statistics; Types of Descriptive Statistics; What is Inferential Statistics; What is Analysis and its types; Probability and Real-life Examples; How Probability is a Process; Views of Probability; Base Theory of Probability.


Using Machine Learning and Regression Techniques to Rank Liberal Arts Colleges on Social Mobility and the Advancement of Underrepresented Groups - The College of Wooster

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The purpose of higher education is to contribute to the advancement of society by graduating students of all backgrounds and providing them the skills and knowledge to be successful in life. One way colleges and universities can contribute to this purpose is to promote the goal of social mobility. The data shows that elite schools are enrolling mostly students from the highest income families. The current college ranking systems are highly weighted towards wealth, rather than social mobility and the advancement of all students. Therefore, there is a need to redirect the focus of college rankings to social mobility, not for the few but for all, especially those who have been traditionally excluded.