Predictive analytics is a collection of a various statistical techniques. It is related to topics such as data modeling, data mining as well as machine learning. It is used to analyze current and historical data to determine interesting patterns and forecast the future events. A series of steps must be followed along to develop a perfect predictive model. The two basic algorithms used for predictive Analysis are Linear Regression and Ensemble methods.
Linear regression is by far the most basic predictive analysis model used in machine learning today. Ensemble methods are more efficient and accurate as they are created from a combination of predictive models.
The use of multiple predictive models helps in figuring out the one that presents the best results. By using the mathematical logic of Combinatronics, it can be proven that the accuracy of ensemble methods is always higher than its counterparts. Ensemble methods are very useful to attain efficiency and better training and prediction capabilities. Some basic methodologies used in predictive analytics.
Predictive Analysis can appear as a complex topic to understand for some if not understood thoroughly. With the emergence of new packages, a variety of tasks can be easily carried out with the help of Predictive Analysis models. The first step in any Predictive Analysis project is to preprocess the data for the better training and prediction results. Data processing can be done with the help of various python libraries such as Pandas, Numpy etc. or by using software tools such as RapidMiner, Weka etc.
Some interesting applications of Predictive Analysis are:
- Business Demand Forecasting
- Defect Detection
- Market Optimization
- Banking and Insurance
A training data set must be separated from the overall dataset for training purposes. The base line here is to get insights from the data for best possible results. The predictive analysis techniques are very useful in the machine learning/deep learning development.
Predictive models are exclusively business oriented and are developed by taking into consideration of all the business targets. Predictive modeling is a term given to the development of models relating to predictive analysis. Predictive analytics can also serves as a basis for a classification problem. ROC (receiver operating characteristic) curve can be used to strength of the classifier model. The ROC curve plots the true positive rate against the false positive rate. It is present inside the metrics package.
We can import it as follows in python programs:
from sklearn import metrics.roc_curve
Many Penalized Predictive techniques such as lasso regression, ridge regression etc can be used for achieving accuracy and efficiency. Python and R are popular programming languages used to implement predictive analytics solutions in machine learning. The problem of over-fitting can also be solved by a penalized model of regression analysis technique.