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Visualization has become a key application of data science in the telecommunications industry.
Specifically, telecommunication analysis is highly dependent on the use of geospatial data. This is because telecommunication networks in themselves are geographically dispersed, and analysis of such dispersions can yield valuable insights regarding network structures, consumer demand and availability.
To illustrate this point, a k-means clustering algorithm is used…
Image recognition and classification is a rapidly growing field in the area of machine learning. In particular, object recognition is a key feature of image classification, and the commercial implications of this are vast.
For instance, image classifiers will increasingly be used to:
The purpose of a variance-covariance matrix is to illustrate the variance of a particular variable (diagonals) while covariance illustrates the covariances between the exhaustive combinations of variables.
A variance-covariance matrix is particularly useful when it comes to analysing the volatility between elements of a group of data. For instance, a variance-covariance matrix has particular applications when it comes to…Continue
The numpy, scipy, and statsmodels libraries are frequently used when it comes to generating regression output. While these libraries are frequently used in regression analysis, it is often the case that a user might choose different libraries depending on the data in question, among other considerations. Here, we will go through how to use each of the above to generate regression output.