Box Plot (also called as Box and Whiskers Plot) is a very popular and widely used plot for visualizing data in the field of Statistics and Data Analysis. In comparison with other graphical techniques, Box Plot not only shows the distribution/spread of data but also indicates the minimum and maximum values, quartiles, the symmetry and skewness of the data. Box Plot is also used to detect outliers. In Machine Learning, you might have used this plot in Exploratory Data Analysis.
Let us understand more about it -
This article includes:1.What is Box Plot?2.How to read a Box Plot?…
As we Know, Outliers are patterns in the datasets that do not conform to the expected behaviour. It may appear in the dataset due to low-quality measurements, malfunctioning equipment, manual error e.t.c. The presence of outliers may create problems in building a good machine learning model.
There are mainly two types of Outliers:
Global Outliers - The data points which are significantly different from the rest of the dataset are called Global Outliers.
Local Outliers -The data points which are significantly different from their neighbours in the dataset are called Local Outliers.
NumPy library is an important foundational tool for studying Machine Learning. Many of its functions are very useful for performing any mathematical or scientific calculation. As it is known that mathematics is the foundation of machine learning, most of the mathematical tasks can be performed using NumPy.
Matplotlib is one of the most popular and oldest plotting libraries in Python which is used in Machine Learning. In Machine learning, it helps to understand the huge amount of data through different visualisations.
Now, let us explore more about Matplotlib.
1.Introduction to Matplotlib
2. How to Install?
3. How to import?
4.Understanding the basics of Graph/Plots using Matplotlib
5.Important plots used in Machine Learning
6.Three-Dimensional Plotting with Matplotlib
1. Introduction to Matplotlib
Matplotlib is an open-source plotting library in Python introduced in the year 2003. …