Dimensionality reduction techniques are methods used to simplify large sets of data by reducing the number of variables or features while keeping the essential information. This helps make data easier to understand, visualise, and process, especially when dealing with complex or high-dimensional datasets. By removing less important features, these techniques can improve the performance and…
Category: Data Science
Time Series Forecasting
Time series forecasting is a way to predict future values by looking at patterns and trends in data that is collected over time. This type of analysis is useful when data points are recorded in a sequence, such as daily temperatures or monthly sales figures. By analysing past behaviour, time series forecasting helps estimate what…
Statistical Hypothesis Testing
Statistical hypothesis testing is a method used to decide if there is enough evidence in a sample of data to support a specific claim about a population. It involves comparing observed results with what would be expected under a certain assumption, called the null hypothesis. If the results are unlikely under this assumption, the hypothesis…
Data Drift Detection
Data drift detection is the process of monitoring and identifying when the statistical properties of input data change over time. These changes can cause machine learning models to perform poorly because the data they see in the real world is different from the data they were trained on. Detecting data drift helps teams take action,…
Feature Selection Algorithms
Feature selection algorithms are techniques used in data analysis to pick out the most important pieces of information from a large set of data. These algorithms help identify which inputs, or features, are most useful for making accurate predictions or decisions. By removing unnecessary or less important features, these methods can make models faster, simpler,…
Contextual Bandit Algorithms
Contextual bandit algorithms are a type of machine learning method used to make decisions based on both past results and current information. They help choose the best action by considering the context or situation at each decision point. These algorithms learn from feedback over time to improve future choices, balancing between trying new actions and…
Low-Rank Factorization
Low-Rank Factorisation is a mathematical technique used to simplify complex data sets or matrices by breaking them into smaller, more manageable parts. It expresses a large matrix as the product of two or more smaller matrices with lower rank, meaning they have fewer independent rows or columns. This method is often used to reduce the…
Process Discovery Algorithms
Process discovery algorithms are computer methods used to automatically create a process model by analysing data from event logs. These algorithms look for patterns in the recorded steps of real-life processes, such as how orders are handled in a company. The resulting model helps people understand how work actually happens, spot inefficiencies, and suggest improvements.
Automated Data Validation
Automated data validation is the process of using software tools to check that data is accurate, complete, and follows the required format before it is used or stored. This helps catch errors early, such as missing values, wrong data types, or values outside of expected ranges. Automated checks can be set up to run whenever…
Knowledge Graph Reasoning
Knowledge graph reasoning is the process of drawing new conclusions or finding hidden connections within a knowledge graph. A knowledge graph is a network of facts, where each fact links different pieces of information. Reasoning uses rules or algorithms to connect the dots, helping computers answer complex questions or spot patterns that are not immediately…