Predictive Analytics
DOE and Regression Models
- Development and application of linear, logistic, and polynomial regression models to predict outcomes based on input variables. Design of Experiments (DOE) are the most efficient way to develop, optimise and improve your process to save time and cost.
Time Series Forecasting
- Based on historical data, forecast future values using exponential smoothing, SARIMA, and ARIMA models.
Classification and Clustering
- Implementing algorithms like k-means, hierarchical clustering, decision trees, and random forests to classify data and identify natural groupings.
Machine Learning
A computational approach that enables systems to learn and make predictions from data.
Supervised Learning
Applying algorithms such as support vector machines, neural networks, and gradient boosting to predict outcomes based on labelled training data.
Unsupervised Learning
Methods for finding patterns in unlabelled data, such as density-based spatial clustering (DBSCAN) and principal component analysis (Uni and Multivariate data analysis).
Model Validation
Ensuring the robustness of models through techniques such as cross-validation, bootstrapping, and evaluating performance using receiver operating characteristic (ROC) curves.
Tell Us About Your Project
Our team of experienced professionals at Graphtal is ready to transform your project from idea to
reality, ensuring alignment with your organisation goals through advanced data analytics and
predictive modelling.
Let's simplify your work