Modeling consists of statistical modeling and building machine learning models. For example, if you were analyzing weather patterns, you may want to reclassify ‘cloudy’, ‘grey’, ‘cloudy with a chance of rain’, and ‘mostly cloudy’ simply as ‘cloudy’.And you can see that the values have been re-classified below.You now know how to reclassify discrete data if needed, but there are a number of things that still need to be looked at.You can see that the minimum and maximum values have changed in the results below.The first thing I like to do when analyzing my variables is visualizing it through a correlation matrix because it’s the fastest way to develop a general understanding of We can see that there is a positive correlation between price and year and a negative correlation between price and odometer. It all begins with exploring a large set of unstructured data while looking for patterns, characteristics, or points of interest. We can also see that there is a negative correlation between year and odometer — the newer a car the less number of miles on the car.It’s pretty hard to beat correlation heatmaps when it comes to data visualizations, but scatterplots are arguably one of the most useful visualizations when it comes to data.This narrates the same story as a correlation matrix — there’s a negative correlation between odometer and price. This was quite interesting !Please check your browser settings or contact your system administrator.Categorical variables require a slightly different approach to review the overall number of each unique value per variable and compare them to each other. This article is focused on the number of step three Exploratory Data Analysis. Therefore, this article will walk you through all the steps required and the tools used in each step. Common solutions of handling missing values would be dropping rows, linear interpolation, using mean values etc. I will discuss the first 4 steps in this article and rest in the upcoming... Dataset:. ‘Understanding the dataset’ can refer to a number of things including but not limited to…Have you heard of the phrase, “garbage in, garbage out”?With EDA, it’s more like, “garbage in, perform EDA, 2. One insight that I got was that Linebackers accumulated more than eight times as many injuries as Tight Ends. The remaining columns are shown below.Revisiting the issue previously addressed, I set parameters for price, year, and odometer to remove any values outside of the set boundaries. You’ll see how I dealt with this in the next section. It helps data scientists and business stakeholders to easily align on processes and data quality. Duplicate rows could be legitimate values depending on your data and how it was collected or the magnitude of variation that is expected in your data. Before applying imputation make sure you fully understand how the imputation method you are using works so that you can identify any issues in your modeling outcome.Next, we need to check for duplicate rows and columns. These may not have the same column name, but if the columns’ rows are identical to another column, one of them should be removed.Summary statistics can be evaluated via a summary statistics table and by checking the individual variable distribution plots. Before we dive into each step of exploratory data analysis, let’s find out which technologies we use. This is why people say that it’s not a good investment to buy a brand new car!To give another example, the scatterplot above shows the relationship between year and price — the newer the car is, the more expensive it’s likely to be.Correlation matrices and scatterplots are useful for exploring the relationship between two variables. It gives you a better understanding of the variables and the relationships between them.To me, there are main components of exploring data:In this article, we’ll take a look at the first two components.You don’t know what you don’t know. We want to consider if those values are data collection errors (which is very likely for anything above 100%) and then remove those observations.Investigating variable relationships through covariance matrices and other analysis methods is essential for not only evaluating the planned modeling strategy but also allows you to understand your data further. At the moment Python is the most popular language for data scientists.


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