Category Archives: Pandas

Inside your data are subsets of data that represent different groups  If you plot the price values for each subset, you might get a bunch of different shapes.

You can evaluate the relationship between each feature and target using a correlation and selecting those features that have the strongest relationship with the target variable. Such as methods that remove redundant variables using correlation.

You can keep adding plt.plot as many times as you like. As for multiple scatter plot, you need to specify the color, marker, edgecolor, alpha so you can differentiate them.

We’ll look at a real-world example with data that I’ll load from a CSV file and also we’re going to learn how to draw overlapping histograms using Pandas and Matplotlib. Before we go and set up how we create the chart let’s look at what the end results would be.

min-max scaling, is the simplest and consists method in rescaling. The range of features to scale in [0, 1] or [−1, 1]. The impact is that we end up with smaller standard deviations, which can suppress the effect of outliers.

For Data analysis continuous data is often discretized or separated into “bins”.Suppose you have a list of people and their age and you want to group them into discrete age buckets.

Using the correlation coefficient you can find out how these two variables are related and to what degree. Please note that this is only a part of the whole dataset.

Z-score re-scale and center(Normalize) the data and look for data points which are too far from zero(center). Data points far from zero will be treated as the outliers. In most of the cases, a threshold of 3 or -3 is used i.e if the Z-score value is greater than or less than 3 or -3 respectively, that data point will be identified as outliers.

Many machine learning models are designed with the assumption that each feature values close to zero or all features vary on comparable scales. The gradient-based model assumes standardized data.