What is predictive modelling?
- Predictive modelling is a process used in data science to create a mathematical model that predicts an outcome based on input data. It involves using statistical algorithms and machine learning techniques to analyze historical data and make predictions about future or unknown events.
- In predictive modelling, the goal is to build a model that can accurately predict the target variable (the outcome we want to predict) based on one or more input variables (features). The model is trained on a dataset that includes both the input variables and the known outcome, allowing it to learn the relationships between the input variables and the target variable.
- Once the model is trained, it can be used to make predictions on new data where the target variable is unknown. The accuracy of the predictions can be evaluated using various metrics, such as accuracy, precision, recall, and F1 score, depending on the nature of the problem.
- Predictive modelling is used in a wide range of applications, including sales forecasting, risk assessment, fraud detection, and healthcare. It can help businesses make informed decisions, optimize processes, and improve outcomes based on data-driven insights.
What is Predictive Modeling ?
Predictive modelling is a process used in data science to create a mathematical model that predicts an outcome based on input data. It involves using statistical algorithms and machine learning techniques to analyze historical data and make predictions about future or unknown events.
Table of Content
- What is predictive modelling?
- Importance of Predictive Modeling
- Applications of Predictive Modeling
- What are dependent and independent variables?
- How to select the Right model?
- What is training and testing data?
- Types of Predictive Models
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