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Univariate Analysis: Various visualizations are done by taking only one variable into account.Here I am doing an EDA as a part of the process of building a machine learning model to predict the median housing price.ĮDA can be broadly divided into two types of analyses: To demonstrate the EDA process, I have chosen the California Housing Prices data-set from the StatLib repository. The temperature of a particular area can be described as 30 ☌, 30.1 ☌, 30.22 ☌, 30.221 ☌, and so on.Ĭategorizing your data will allow you to short-list the kind of charts that are most applicable to your requirements from a plethora of data visualization tools. Any kind of measure is a continuous variable. Continuous Variable: A continuous variable is a variable that has an infinite no.Discrete Variable: Discrete variables are countable variables but can take on a large range of values.Educational Qualifications: Uneducated/ Undergraduate/ Postgraduate/ Doctoral, etc. Polynomic Variable: A polynomic variable is a variable that has multiple values to choose from.Dichotomous Variable: A dichotomous variable is a variable that takes only one out of two possible values when measured.But the good thing is, like any art form, you get better the more you practice and develop your own heuristics.īefore doing any EDA, you should know the kind of data you may encounter. There is no one way and each analyst has their own methodology that they like to follow. EDA is more like an art, like writing or building a machine learning model.
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You are not really sure where to start, which charts to choose, and how to interpret the visualizations that you get. However, for someone starting out in their data science journey, EDA can seem a daunting task. This procedure is termed Exploratory Data Analysis or EDA.
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How can you analyze the data to make such crucial decisions? This is done by drawing various graphs/charts/plots or other data visualization methods between the various variables in your data-set and drawing inferences from them. For instance, if you have a strong idea about which input variables most affect the response variable in a machine learning model, it will not only simplify your model but save on the computation cost of training the model.
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Getting a strong idea of its behavior and trends will allow you to make more educated decisions so that your solution at the end of your data-related task is an optimal one. is to see and analyze the data that is contained inside the data set that you have. The first step before doing any kind of data-related task, be it machine learning, data analytics, etc. “The greatest value of a picture is when it forces us to notice what we never expected to see” - John Tukey