CompTIA Data+ Practice Exam 2025 – Your Complete All-in-One Guide to Exam Success!

The technique often utilized for predictive analytics is machine learning algorithms. Machine learning encompasses a variety of methods and models that can analyze large datasets, recognize patterns, and make predictions based on those patterns. This is particularly useful in predictive analytics, where the goal is to forecast future trends or behaviors based on historical data.

Machine learning algorithms can adapt and improve their predictions as more data becomes available, making them a powerful tool for developing predictive models across various domains such as finance, marketing, healthcare, and more. By identifying complex relationships within the data that traditional statistical methods might miss, these algorithms enhance the accuracy and reliability of predictions.

While statistical regression can also play a role in predictive analytics by modeling relationships between variables, it is often limited to scenarios where those relationships are linear. In contrast, machine learning can handle a wider range of data types and complexities. Surveys and feedback methods are typically used for data collection rather than for making predictions, and data cleansing methods are essential for preparing data for analysis but do not directly contribute to prediction-making.