AI in Predictive Analytics

Our solution is integrated with linear regression to predict continuous outcome variables based on input predictor variables, whereas logistic regression predicts event occurrence, estimating the probability that a given input point belongs to a certain class.

Our predictive analytics solution uses time series analysis used for data prediction values based on past patterns, useful in economics, finance and any field where data is collected over time.  AI-based predictive analytics is popping up in various industries effectively implementing and working with custom solutions. 

We use decision trees in our AI solution, splitting the data into branches based on decision points over the input features, creating a tree-like model, including event outcomes, resource costs and utility. 

Random forests are the extension of decision trees used to improve predictive accuracy and built from a sample from a training set, and the final prediction is made by averaging the predictions of the individual trees.

Our AI predictive analytics solution uses neural networks and deep learning involving many layers and techniques effective for complex tasks like image recognition, natural language processing, and time series forecasting.

We use clustering techniques in AI for predictive analysis to identify distinct groups or patterns in data, often a preliminary step in predictive analysis to understand the structure of the data.

Our experts offer solutions with ensemble methods combining predictive models to improve accuracy, boosting multiple models trained to solve the same problem and their predictions combined to produce a final outcome.

Our AI predictive analytics solution uses neural networks and deep learning involving many layers and techniques effective for complex tasks like image recognition, natural language processing, and time series forecasting.