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