Prediction is a very powerful tool when it comes to big data. Prediction in big data is based on datasets, its volume, values and the variety we have in our data. To make a prediction we use different big data tools and algorithms. Predictive modeling can be explained as a process of building statistical models for predicting the future behaviour of our data.
Predictive modeling is a very strong technique that will help us predict the problem, solution or any other issue that might come up for our product maybe soon in the future. Therefore There might be some factors that might influence future results.
Predictive modeling uses machine learning algorithms for prediction. Machine learning depends on the dataset that you give for prediction. Machine learning algorithms help us build a predictive model which will predict the fate based on past numbers and data.
How do we go around building a predictive modeling?
- The expected result
- Reasons that influence the result
- How to use those reasons for accurate prediction
Three simple steps to build a predictive modeling
There are three basic steps for building a predictive model. This is just a simple guide to understand how a predictive model is build:
- Data: Data is the information needed for working on a given problem. Whenever we select a problem to build a build a predictive model, we need information based on which the prediction can be made. Data can be in the form of text, comma-separated values (CSV), database or a raw file.
- Model: Model will be responsible in giving us the needed results. It uses any one of the machine learning algorithms. The model will be used for learning. All this learning will be used for prediction when we wish to do so. Once this model is built, we can use it for various predictions. Also, we can reuse the model for different datasets with a different set of predictors.
We will use an algorithm and provide a training dataset for learning purposes, so we can use it for prediction.
- Prediction: In this phase, we use the trained model on different dataset for similar or different predictions. Once the model is built, we can use it for different predictions based on the input. The input could be of the same data or different data, the prediction model should still work fine for any dataset.
Applications of predictive modeling
A recommendation system is the best example of predictive modeling.
The predictive model is best when it comes to studying the user behaviour and then making the model work accordingly. Recommender systems are part of such behaviour. The whole point of a recommender system is that it recommends you the products as per your search history, just like e-commerce websites like Amazon.
In conclusion, when it comes to big data and big data analytics, predictive modeling is a very crucial step. The analysis will help us understand, clean, transform and use the relevant data. The predictive model will help us predict the future, also find a solution and apply it to newer problems for further and accurate predictions.