With the help of machine learning and predictive analytics, you can take your company's analytic capabilities to the next level. Predictive analytics is a type of advanced analytics that uses historical data to create predictions about future outcomes. Many businesses utilize predictive analytics to uncover trends in their data in order to boost their profits and gain a competitive edge.
Organizations use predictive analytics to help solve some of their most difficult problems and uncover new opportunities. Use cases include:
Fraud Detection - Predictive analytics techniques can help spot patterns and prevent illegal activity. High-performance behavioral analytics evaluates all network actions in real-time to discover patterns that may suggest fraud, zero-day vulnerabilities, and advanced persistent attacks, as cybersecurity becomes a rising concern.
Improving marketing campaigns - Predictive analytics is used to predict customer response to offers or likely purchases, as well as to encourage cross-selling opportunities. Businesses can use predictive models to acquire, keep, and develop their most profitable consumers.
Manage risk - Credit ratings are a well-known example of predictive analytics, and they're used to determine the possibility of a buyer defaulting on a purchase. Insurance claims and collections are two more risk-related applications.
Improving operational efficiencies - Many businesses use predictive analytics for forecasting. Predictive analytics is used by airlines to determine ticket prices. To optimize revenue, hotels strive to estimate occupancy for any particular night. Organizations can run more efficiently with the help of predictive analytics.
The Machine Learning Laboratory is a suite of software that gives the Excel user access to the most popular and efficient tools used by Data Science professionals for predictive data analysis without having to learn computer programming.
The Machine Learning Laboratory gives Excel users access to the machine learning models in the Scikit-Learn library, one of the most popular and trusted open-source machine libraries in the world.
Available Machine Learning models include:
14 Regression models
- AdaBoost Regression
- Bayesian Regression
- Decision Tree Regression
- ElasticNet Regression
- Gaussian Process Regression
- Gradient Boosting Regression
- K Neighbors Regression
- Lasso Regression
- Linear Regression
- Multilayer Perceptron Regression
- Random Forest Regression
- Ridge Regression
- Stochastic Gradient Descent Regression
- Support Vector Machine Regression
8 Classification models
- AdaBoost Classification
- Gradient Boosting Classification
- K Neighbors Classification
- Multilayer Perceptron Classification
- Random Forest Classification
- Ridge Classification
- Stochastic Gradient Descent Classification
- Support Vector Machine Classification
3 Clustering models
- Affinity Propagation Clustering
- Birch Clustering
- KMeans Clustering
Get started using the Machine Learning Laboratory for free – no credit card required. With the Free Plan, you can create, train and save a limited number of models. When you are ready to build your library of fully trained machine learning models, you can upgrade to the Solo plan. If you’re part of a team and want to share models with others, we offer the Team Plan as well.