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Predictive Modelling: It is the most prevalent statistical technique used to predict future behaviour.Predictive Modelling also contemplated as a mathematical process used to forecast future outcomes and events by analysing the pattern in a data set.It works by analysing past data,current data and projecting the leanings on the result which has been generated to forecast the outcomes. Predictive Modelling can be taken as assumptions which relies on what is happening currently and what has happened in the past. Here are the 5 models of Predictive Modelling: Forecast model: Its a very common model and it works on the values which based on numbers got from the learning of historical data. Time series model: This works on sequence of points in the data given based on time. Classification Model: This model merely works on classification of data into different types of categories from the past and current data and analyses the future outcome from it.It is the most simplest model of predictive Modelling. Clustering Model: This model works on common attributes on the same data.Like grouping the smaller things,people with same behaviour will be considered as a sub group from a large scale data. Outliers model: This works on outlying data or analysing abnormal data points.This technique can be used to predict the process behaviour as short term predictions, medium term predictions and long term predictions. Example for short term prediction: prediction of traffic on websites for the next hours so that it helps in utilisation of server resources. Medium term prediction: Predicting monthly power consumption on a building to optimize HVAC systems. Long term prediction: Predicting occurrence of natural disasters like earthquakes, hurricanes floods and droughts. Example for predicting models: Fraud detection in banks, insurance companies: Predictive Modelling helps to detect fraudulent activities,transactions like insurance or credit card fraud