Data Mining and Machine Learning are closely interconnected. Data Mining is a method used to analyze large datasets with statistical and mathematical techniques to uncover hidden patterns or trends and extract valuable insights. Machine Learning (ML)* then uses these datasets to make decisions and take actions based on pre-modeled algorithms.
Data Analysis Process:
Data Cleaning: Essential for removing noise, incomplete, or incorrect data that could interfere with the algorithm's accuracy.
Data Integration: Extracting, combining, and integrating data.
Data Selection: Extracting useful data from the database.
Data Transformation: Normalization, aggregation, and generalization of data.
Data Mining: Utilizing various methods like regression, classification, clustering, and prediction to find patterns in the data.
Model Evaluation: Assessing the utility of the obtained model.
Information Presentation: Presenting the information in an accessible format
Data Analysis helps analyze huge amounts of data and find valuable information among them that will help, for example, detect anomalies or failures in the product, or segment customers by dividing them into separate groups based on their needs, analyze the market basket and offer the buyer products that can be purchased together with the already selected product, and much more.
Artificial intelligence built on ML algorithms is capable of calculating the maximum number of possible solutions to one situation, constantly receiving, processing and combining a lot of new information. Like the human brain, artificial intelligence is capable of tirelessly processing information around the clock, helping a person to perform various tasks related to recognition, decision-making, and data analysis.
Algorithm training is divided into:
Supervised learning (for each situation there is a required solution)
Unsupervised learning (only a situation without a solution is known)
Reinforcement learning (for each situation there is an accepted solution)
Types of Tasks Solved
Classification
Dividing objects into predefined classes, useful for sorting emails into personal, work, or spam categories
Clustering
Grouping objects based on data describing them, useful for identifying similar customers for specific product sales.
Regression
Establishing dependence of continuous output data on input data, useful for predicting real estate prices.
Prediction
Similar to regression but takes temporal aspects into account, useful for predicting future sales.
Association
Association is the task of establishing patterns between related events.
Association is an unsupervised learning task. The most striking example of an association task is identifying purchases that are usually bought together. For example, an analysis of the store across from the school showed that between 3 and 4 p.m., chips and soda are most often bought together.
SEQUENTIAL PATTERNS
Sequential patterns are the task of identifying patterns between events related in time.
If the association task considers only the fact of the appearance of a group of goods together, then in sequential patterns we consider the sequence of the appearance of items in their groups.
DEVIATION ANALYSIS
Deviation analysis is the task of identifying anomalies.
Anomaly analysis is necessary to identify atypical situations related to the operation of programs, mechanisms or even human behavior, and promptly notify the responsible personnel, or even automatically make a limiting decision. When analyzing the operation of programs, the cause of an anomaly may be the presence of a virus or a hacked system, and the identified deviations from behavioral patterns, for example, of a bank client, may signal the theft of a bank card or hacking of access to an online bank, or other fraudulent actions.
SYNTHESIS
Synthesis tasks - generation of, for example, texts, audio, video, photographs are usually solved using specialized neural networks. One example is the implementation of voice assistants in Yandex or Google. There are also specialized networks that can synthesize paintings and music, colorize black and white photographs or even films.
LIBRARIES USED IN SOLVING DA/ML PROBLEMS
The main library used for machine learning in C# is the ML.NET library from Microsoft. One of the main structures of this library is DataView, the idea of which is borrowed from SQL. In the DataView structure, there is no limit on the data size, since only the amount of data that is currently needed is loaded into memory. Moreover, this structure uses lazy evaluation, that is, it does not perform any actions until the model is trained. However, this creates some difficulties when debugging. When working with machine learning, data is most often stored in .csv and .tsv files, or a database. Data that has already been loaded into DataView can also be processed, for example, it can be normalized, for which DataView provides many options. Prepared data ML.NET uses TrainTestSplit to split the dataset into training and test data if necessary. With the help of training classes, ML.NET provides algorithms used in data science. https://github.com/jeffprosise/ML.NET
ALSO USED IN .NET FOR MACHINE LEARNING AND DATA SCIENCE ARE THE FOLLOWING LIBRARIES:
Infer.NET. This platform is designed to perform Bayesian inference in graphical models, and is also used for probabilistic programming. Supports cross-platform.
Accord.NET. A machine learning framework combined with libraries for audio and image processing. This library is written entirely in C#. It is used to create computer vision, listening, signal processing and statistics applications. Supports cross-platform development.
AForge.NET. A framework for computer vision and artificial intelligence, such as image processing, neural networks, genetic algorithms, machine learning, robotics.
Catalyst. This library offers pre-trained models for natural language processing, and also provides faster training for some models, supports cross-platform.
GeneticSharp. A multi-platform genetic algorithm library.
SciSharp STACK. This library brings all the major machine learning frameworks from python to .NET. SciSharp STACK is cross-platform. It brings together TensorFlow, NumPy, Keras-related technologies from Python, as well as the TensorFlow-inspired and ground-up NeuralNetwork.NET library, and the SciSharp Cube library for using machine learning features in a Docker container.
m2cgen. This is a command-line tool for porting trained classical machine learning models to native .NET code without any dependencies.
THE MOST COMPLETE SET OF ML METHODS IS IMPLEMENTED AS LIBRARIES FOR THE PYTHON LANGUAGE:
NumPy. A library designed to perform linear algebra and numerical transformations, simplifies work with data preparation and model building.
Pandas. A library for processing data that can be loaded from almost any source, calculate various functions, create new parameters, build queries to data similar to SQL queries.
Scikit-learn. A software library containing methods for dividing a dataset into a test and training one, calculating the main metrics over data sets, and performing cross-validation. This library also has some machine learning algorithms: linear regression, ridge regression, LASSO, support vector machines, decision tree methods, forests, and implementation of basic clustering methods.
SciPy. This library has a huge set of mathematical analysis tools, such as calculating integrals, processing signals, images, calculating extrema. SciPy helps to perform optimization tasks, solve systems of equations, implement genetic algorithms.
TensorFlow. This library is used to build neural networks. To work with NVIDIA video cards, it uses the cuDNN library. With TensorFlow, you can build deep neural networks for recognizing images and handwritten text, as well as recurrent networks for NLP.
Keras. The library supports the main types of layers and structural elements for building neural networks, has support for both recurrent and convolutional networks, as well as the implementation of well-known neural network architectures. It has ready-made functions for working with text and images.
Theano. This library can work with very large neural networks, it helps to reduce development time, optimize and calculate mathematical expressions.
Caffe. Contains the implementation of well-known neural networks, as well as TensorFlow, it uses cuDNN to work with NVIDIA video cards.
pyTorch. This library contains implementations of static operations, algorithms for working with images, as well as tools for working with neural networks.
Seaborn. A graphical library for visualizing the results of machine learning, for example, plotting time series graphs, heat maps, etc.
The staff of the 2K-Software company includes specialists with specialized education in applied mathematics, who have scientific works in the field of data processing and machine learning, as well as experience in applying these technologies in practice in commercial projects.