Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical fac...
Predictive analytics is the use of statistics and modeling techniques to determine future performance based on current and historical data.
What is predictive modeling? ; Predictive modeling is a mathematical process used to predict future events or outcomes by analyzing patterns in a given set of input data. It is a crucial component of predictive analytics, a type of data analytics which uses current and historical data to forecast activity, behavior and trends. Examples of predictive modeling include estimating the quality of a sales lead, the likelihood of spam or the probability someone will click a link or buy a product. These capabilities are often baked into various busines ...
배울 내용 ; Students will learn about simple forecasting methods and whether they are useful in business context ; Students will learn about how to forecast when the product exhibits an intermittent or discrete demand trend, which is usually bit difficult to do ; They will NOT learn the statistical derivations of the models but only the application of them
What Machine Learning and Artificial Intelligence Services Does Oracle Analytics Support? ; Use Oracle Machine Learning Models in Oracle Analytics ; Apply a Predictive or Registered Oracle Machine Learning Model to a Dataset ; Use OCI Vision Models in Oracle Analytics
Predictive analytics refers to the use of statistical modeling, data mining and ML to predict future outcomes based on historical and current data.
X1 = density of the local population · X2 = average income of the local population · X3 = number of attractive stores nearby · X4 = distance to the nearest competitor
Take your data analytics and predictive modeling skills to the next level using the popular tools and libraries in Pytho | 어떤 주제든 강사에게 배우세요
배울 내용 ; Data Preprocessing: Techniques for cleaning, formatting, and organizing data effectively. ; Linear Regression: Understanding and implementing linear regression models for predictive analysis. ; Logistic Regression: Applying logistic regression for classification tasks and understanding its nuances. ; Multiple Linear Regression: Extending regression analysis to multiple predictors for more complex modeling.
Learn about CART algorithm using data ; How CART model can be used to make predictions on unseen data ; Understand Predictive Analytics ; Understand how to use predictive analytics tools to solve real time business problems