Predictive Analytics is a branch of advanced analytics that uses historical data and machine learning techniques to predict future events. It provides estimates about the likelihood of a future outcome.
Imagine you are playing a guessing game, and you remember all the previous turns and results. Now, you use this memory to guess what’s likely to happen next. That’s the game Predictive Analytics plays with data.
Predictive analytics is a branch of artificial intelligence that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics could be applied to any type of unknown event, be it in the past, present or future.
There are several steps involved in predictive analysis. First, data is collected for the relevant predictors and outcome, this data could be historical or newly gathered. Following data collection, it’s preprocessed to eliminate inconsistencies and prepare it for modeling.
Then, statistical techniques and machine learning models such as logistic regression, decision trees, and neural networks are implemented to generate a predictive model. These models can map the relationships between predictors and the outcome and be used to forecast future occurrences.
With the help of the generated models, predictions are made on fresh data. This is the phase where the value of predictive analytics shows, as the predictions made here, if accurate, can facilitate improved decision making in different sectors such as health, finance, marketing etc.
A significant aspect of predictive analytics is its reliance on machine learning. As the name suggests, machine learning is about teaching computers to learn from the data to make decisions or predictions. In the context of predictive analytics, the machine learning component allows the system to learn from past data, detect patterns, and adjust predictions as new data comes in.
Many challenges arise in predictive analytics, particularly relating to data quality, lack of skilled personnel, and ethical issues. However, with careful planning and implementation, along with continuous model monitoring and adjustment, predictive analytics can be a beneficial tool for leveraging data to forecast future trends and behaviors.