A subset of artificial intelligence that enables systems to learn from data and improve their performance without explicit programming.

The process of using data and statistical algorithms to forecast future outcomes based on historical data.

Estimating future financial outcomes based on historical data, trends, and statistical methods.

The systematic examination of data to extract useful information, identify trends, and support decision-making.

Using automated systems to execute trading strategies based on predefined criteria and algorithms.

The process of selecting a subset of relevant features for model training, improving accuracy and reducing overfitting.

The optimization of algorithm parameters that are not learned from the data but set before the learning process.

Computational models inspired by the human brain, used for recognizing patterns and making predictions.

Techniques that combine multiple models to improve predictive performance and robustness.

An approach to analyzing data sets to summarize their main characteristics, often using visual methods.

The process of correcting or removing inaccurate records from a dataset to improve its quality.

Adjusting values in a dataset to a common scale without distorting differences in the ranges of values.

Quantitative measures used to evaluate the accuracy and effectiveness of a predictive model.

A modeling error that occurs when a model is too complex and captures noise instead of the underlying data trend.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

Predicting future stock prices based on historical data and various financial metrics.

A statistical technique that deals with time-ordered data points to extract meaningful statistics.

A statistical method for estimating the relationships among variables, often used for prediction.

A type of machine learning where the model is trained on labeled data.

A type of machine learning where the model learns patterns from unlabeled data.

The graphical representation of information and data to communicate insights clearly.

A popular Python library used for machine learning, providing simple and efficient tools for data mining.

An open-source library developed by Google for numerical computation and machine learning.

The process of improving a predictive model by adjusting parameters or selecting different algorithms.

A set of rules and tools for building software applications, allowing different software systems to communicate.