What is it?
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.
- 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: Rral-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.
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.