Unleash performance with in-database analytics

Data engineers and data scientists can quickly clean, transform, and organize raw data before using it for analysis or machine learning purposes. Frequently used functions include descriptive statistics, hypothesis testing, outlier handling, statistical tests, and many more.

Analyze and visualize data to understand its underlying patterns, relationships, and distributions. This crucial step helps uncover insights and identify potential issues in the data. Data science teams and business analysts can use a variety of functions—from path analysis, 4D analytics, temporal and time-series analysis, and more—to understand the data, identify trends, and develop hypotheses about the relationships between variables.

Select and transform raw data into features that can be used to train machine learning models. These features help models make accurate predictions or classifications. Effective feature engineering can improve the accuracy, efficiency, and interpretability of machine learning models.

Leverage a variety of models to make accurate predictions or classifications. ClearScape Analytics provides a robust assortment of algorithms to help build models that can generalize well to new data and make accurate predictions. Algorithms include Linear Regression, Logistic Regression, Decision Forest, Arima, XGBoost, SVM, and many more.

Assess the performance of a machine learning model on a test set or validation set. This capability helps data science teams determine how well the model generalizes to new data while comparing the performance of different models. The models can be operationalized to score and make predictions.