Dejah  Reinger

Dejah Reinger


Multivariate Regression (Problem Solving)

In this article, we will discuss a problem that can solve using Multivariate Regression.

The Problem:

  • A researcher has collected data on three psychological variables, four academic variables (standardized test scores), and the type of educational program the student is in for 600 high school students. She is interested in how the set of psychological variables is related to the academic variables and the type of program the student is in.
  • A doctor has collected data on cholesterol, blood pressure, and weight. She also collected data on the eating habits of the subjects (e.g., how many ounces of red meat, fish, dairy products, and chocolate consumed per week). She wants to investigate the relationship between the three measures of health and eating habits.
  • A property dealer wants to set housing prices which are based on various factors like the size of house, Number of bedrooms, Age of house, etc.

#python #machine-learning #artificial-intelligence #data-science #algorithms

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Multivariate Regression (Problem Solving)
Vincent Lab

Vincent Lab


How to do Problem Solving as a Developer

In this video, I will be talking about problem-solving as a developer.

#problem solving skills #problem solving how to #problem solving strategies #problem solving #developer

5 Regression algorithms: Explanation & Implementation in Python

Take your current understanding and skills on machine learning algorithms to the next level with this article. What is regression analysis in simple words? How is it applied in practice for real-world problems? And what is the possible snippet of codes in Python you can use for implementation regression algorithms for various objectives? Let’s forget about boring learning stuff and talk about science and the way it works.

#linear-regression-python #linear-regression #multivariate-regression #regression #python-programming

Vincent Lab

Vincent Lab


How to Ask a Good Question on the Internet as a Programmer

In this video, I will be talking about how to ask for help as a developer.

#problem solving skills #problem solving how to #coding interviews #problem solving #how to ask a good question

Angela  Dickens

Angela Dickens


Regression: Linear Regression

Machine learning algorithms are not your regular algorithms that we may be used to because they are often described by a combination of some complex statistics and mathematics. Since it is very important to understand the background of any algorithm you want to implement, this could pose a challenge to people with a non-mathematical background as the maths can sap your motivation by slowing you down.

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In this article, we would be discussing linear and logistic regression and some regression techniques assuming we all have heard or even learnt about the Linear model in Mathematics class at high school. Hopefully, at the end of the article, the concept would be clearer.

**Regression Analysis **is a statistical process for estimating the relationships between the dependent variables (say Y) and one or more independent variables or predictors (X). It explains the changes in the dependent variables with respect to changes in select predictors. Some major uses for regression analysis are in determining the strength of predictors, forecasting an effect, and trend forecasting. It finds the significant relationship between variables and the impact of predictors on dependent variables. In regression, we fit a curve/line (regression/best fit line) to the data points, such that the differences between the distances of data points from the curve/line are minimized.

#regression #machine-learning #beginner #logistic-regression #linear-regression #deep learning

Ilene  Jerde

Ilene Jerde


Multivariable Linear Regression Basics

The intention of this article is to help you get up and running on the creation of a simple linear regression model using Python. Creating a model is not an easy task, and every dataset can be different. This model will be imperfect and simplified, but you can always go back and tweak it to your purposes.

The dataset we will use is the King County Housing Data available on The .csv for the dataset can be found below. We will attempt to make a multivariable linear regression using several home attributes (sqft, bedrooms, bathrooms, condition, etc.). We hope to use this model to interpret how the variables affect our home price.

House Sales in King County, USA

Predict house price using regression

Let’s start by importing the csv as a Pandas DataFrame.

You should do some exploratory data analysis to get used to the data. The available columns are:

At this point, you would also do some work with the df to filter out anomalies, eliminate or replace NaN values, and clean up your dataset as needed. That can be a significant job, so I will simply move past this for now and use the raw data from the set even though it will produce a less than stellar regression.

Continuous, Categorical, or Ordinal

Not all variables can be treated the same in regression. They would typically fall into one of three categories:

Continuous — variable has a continuous or “infinite” range of values. For this dataset, it could be something like price or square feet. Those are numerical values that could be any number in a continuous range.

#data-science #multivariate-regression #linear-regression #king-county-housing #python