Machine Learning Data Analytics Tool

Machine Learning Data Analytics use self-improving algorithms to extract data and calculate statistical inferences, providing predictive insights on future events. First, raw big data is collected from sources such as devices, log data or user-generated content. Then, the collected data is processed using different techniques, which produces valid and editable data along with predictive features, generating predictive knowledge. After that, prediction improvement takes place using different statistical models, machine learning algorithms and hybrid models.

While traditional data analysis uses a static model built on past data and specialist interference, machine learning automatically looks for predictor variables and their interactions, starting with the outcome variables. When the machine learning algorithm is given a goal, it learns from the collected data which factors and variables are important to achieve that goal. Without relying on specific programming, the algorithms are constantly improving as more information is collected and analyzed.

Gender Equality

Challenges

• In oppressive governments or companies, these systems may be used to deliberately retain pay gaps or even automate the harassment of female workers.

Opportunities

• Potential to overcome cultural and social norms, which can entail gender biases in education, business, etc.

• Gender-sensitive advisory services for vocational training actors due to recurring data collection.