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scikit-learn

What is it?

Scikit-learn is a free machine learning algorithm library written in Python and built upon the SciPy module. The Scikit-learn module gives programmers a number of supervised and unsupervised learning algorithms in the form of a coherent programming interface. Moreover, the module is available with a BSD licence, which allows it to be used both commercially and academically.

With Scikit-learn it is possible to use the following machine learning concepts:

Clustering: grouping data that has not been “marked”
Cross validation:
estimating the efficiency of predictive models
Datasets:
creating datasets for testing purposes with a specific layout in order to research the model
Dimensionality reduction:
reducing the number of attributes
Ensemble methods:
in order to combine predictive results from several supervised models
Feature extraction:
to define the attributes that are to be used when creating a model
Feature selection: in order to identify the attributes that allow achieving the best prediction performance
Parameter tuning: optimizing predictive models in order to achieve maximum efficiency
Manifold learning: graphically summarizing and representing complex, multidimensional data
Supervised models: a set of algorithms for building linear models, discriminant analysis, bayesian algebra, neuron networks, decision trees, and many others.

Scikit-learn comprises:

SciPy:

the primary library used for calculations

NumPy:

a library for n-dimensional matrix calculations

Matplotlib:

a chart and graph library

IPython:

an interactive console supporting calculations

Sympy:

an algebraic library

Pandas:

data structures and analysis

Scikit-learn is an open source library which enables to build applications and interfaces upgraded with the latest developments in machine learning. The interfaces supplied by this module are considered some of the most coherent and, thus, they are widely used on the IT market. Google, Spotify or Evernote are but a few commercial users of the library.

What is it used for?

Companies use Scikit-learn to improve the quality of their operations. By using machine learning algorithms, it is possible to discover new information regarding a company’s operations. Consequently, customer service, production, distribution or UX processes may be improved. Examples include companies operating in the insurance, technology or finance sectors. Several example uses have been listed below:

Insurance:

Optimizing customer service by applying machine learning to sorting client queries by topic. Messages are directed to specialized staff and the client receives a to-the-point answer

Insurance, finance:

Scoring model optimization for clients.

Finance:

Using predictive models to anticipate clients’ credit profiles for particular banking products

Finance:

Real-time stock exchange data analysis which helps predict future stock exchange behaviour

Public institutions:

Spending analysis depending on situation, time, category

Health care:

Patient data analysis to expedite diagnostics.

There are many other examples of how this tool may be used – the full list of example uses may be downloaded here.

Our experience

BlueSoft successfully uses the Scikit-learn technology at its clients representing such industries as financial, telecoms or life science, while our expertise allows us to fully utilize its possibilities.
Our company has ample experience in the realm of business analysis, which helps our clients choose appropriate issues that can be improved using machine learning algorithms and then deploy them with the help of our team of experienced developers, analysts and architects. Scikit-learn is a platform which, if used properly, greatly benefits organizations and makes it possible to improve operations or the products themselves. However, it is BlueSoft’s experienced team, acquainted with issues of data science, that may fully utilize the data already collected and extrapolate maximum value from it.

BlueSoft has successfully implemented many projects in this area. We will happily present our portfolio directly as well as answer more questions about technology itself and benefits to be brought by its implementation.

See other technologies, which we use in this area

Machine Learning