Data mining is the process of applying algorithms to discover patterns within large collections of data. It involves an automated approach to analyze data that would be impractical to handle manually. The data mining process typically includes several key steps: data collection, data cleaning and validation, model development and testing, and final deployment. Model development often utilizes various machine learning algorithms, such as regression, decision trees, and support vector machines, among others ².

The initial step in data mining is data collection, where relevant data is gathered. This is followed by data cleaning, which ensures that the data is free from errors and inconsistencies. After cleaning, data analysis is performed to identify patterns, and finally, the results are interpreted to draw conclusions or make predictions ⁴.

Data mining has numerous applications across different domains, including marketing, education, and environmental predictions. It helps organizations understand customer behavior, improve educational outcomes, and even predict natural disasters ⁴⁵. However, it is important to note that data mining should not be used when a complete and accurate solution is available for a specific problem ⁴.