Download Applied Predictive Modeling: An Overview AudioBook Free
Predictive modeling uses figures to be able to predict effects. However, predictive modeling can be applied to events no matter time of occurrence. With regards to the applications of predictive modeling, techniques are used in various fields including algorithmic trading, uplift modeling, archaeology, health care, customer marriage management, and many more. This book includes the predictive modeling process with important steps of the process, data preprocessing, data splitting and vital steps of model tuning and enhancing model performance. Further, the reserve will expose you to the most common classification and regression techniques including logistic regression which is widely used as it pertains to the finding the possibility of event success or event failure. You will get to know the common predictive modeling techniques as well such as stepwise regression, polynomial regression, and ridge regression which can only help you when you are dealing with the info that is suffering from very common multicollinearity where indie parameters are highly correlated. What you would learn in Applied Predictive Modeling:
- Most common predictive modeling techniques
- Types of regression models
- The overall predictive modeling process
- Fundamental steps to effective and highly exact predictive modeling
- How to build predictive model with logistic regression with code entries
- How to build predictive model using Python
- How to enhance your model performance
- Guidelines for increasing the entire predictive electric power
- How to take care of school imbalance
- Common factors behind poor model performance
