Our analytic models adhere to a methodology to ensure success.

Regardless of methodology, most processes for creating predictive models incorporate the following steps:
1. Project Definition: Define the institutional objectives and desired outcomes for the project and translate them into predictive analytic objectives and tasks.
2. Exploration: Analyze source data to determine the most appropriate data and model building approach, and scope the effort.
3. Data Preparation: Select, extract, and transform data upon which to create models.
4. Model Building: Create, test, and validate models, and evaluate whether they will meet project metrics and goals.
5. Deployment: Apply model results to business decisions or processes. This ranges from sharing insights with business users to embedding models into applications to automate decisions and business processes.
6. Model Management: Manage models to improve performance (i.e., accuracy), control access, promote reuse, standardize toolsets, and minimize redundant activities.
 

Forecasting Techniques

We will test and recommends the best fit forecast by applying the selected forecasting methods to past data sets and comparing the forecast simulation to the actual history. When you generate a best fit forecast, the system compares actual sales order (worker placement) histories to forecasts for a specific time period and computes how accurately each different forecasting method predicted sales (worker placements). Then the system recommends the most accurate forecast as the best fit.

In this context; Statistical methods such as parametric and non-parametric regression models, cluster models and time series models are used.

 Data Mining

Accurate storage, correct classification, correct sorting, correct processing and correct interpretation play an important role in the success of business decisions. However, the complexity of decision processes has revealed the need for more data numerically. It is becoming increasingly complex to process these data in decision making processes.

Data mining is a collection of methods developed to make the data owner both in an understandable and usable form by exploring the data that has the potential to be beneficial, and that there are unexpected / unknown relationships among large data stacks. Data mining techniques developed and applied as follows:

• Decision Tree
• Association rules
• Sequence analysis
• Random forest
• Machine Learning / Deep learning
• Fraud detection