💡😎Basics of working with Data-Mining: process, tools and techniques
Data mining is the process of processing data to identify patterns, correlations and anomalies in large datasets. It uses a variety of statistical analysis and machine learning techniques to extract meaningful information and insights from data. Companies can use these insights to make informed decisions, predict trends, and improve business strategies.
There are such data mining techniques as:
Decision trees - at the end of each branch there is a prediction or decision. In classification tasks, these endpoints separate data into categories
Detection of anomalies - anomalies can arise from fluctuations in measurements or be indicators of experimental error; in some cases they may indicate an important discovery or a new trend
Software for working with data mining is divided into:
1. Visualization tools:
Grafana - suitable for analytics and real-time monitoring
Google Charts is a web-based solution for creating interactive charts
2. Data mining platforms:
KNIME is an analytical platform that allows you to download data from various sources, transform data and load it into various databases
RapidMiner is a multi-user software platform that is an integrated environment for processing data in large information arrays, machine learning, text analytics and building predictive models, as well as for solving other Data Mining problems.