What is meant by Predictive analytics?
Predictive analytics refers to the process of using data, statistical algorithms, and machine learning techniques to predict future events or trends. This technique is based on analyzing historical data to identify patterns and develop models that can be used to make predictions about future events. Predictive analytics is used in various industries and use cases, such as marketing, finance, healthcare, manufacturing, and logistics, to optimize decisions, minimize risks, and identify opportunities.
Typical functions of software in the area of predictive analytics include:
- Data Integration: Collecting and consolidating data from various sources for analysis.
- Data Cleansing and Preparation: Cleaning and preparing data to make it suitable for analysis.
- Feature Engineering: Identifying and selecting relevant features or variables for prediction.
- Model Building: Developing statistical models and algorithms based on historical data.
- Model Training: Fitting and training predictive models using historical data.
- Model Validation: Checking and validating predictive models using independent test data.
- Making Predictions: Using trained models to make predictions about future events.
- Model Optimization: Fine-tuning models to improve the accuracy and reliability of predictions.
- Visualization of Results: Presenting predictions and results in clear graphics or dashboards.
- Real-Time Deployment: Integrating predictive models into real-time systems or applications for automated decision support.