Descriptive mining:
This term is basically used to produce correlation, crosstabulation, frequency etc. These technologies are used to determine the similarities in the data and to find existing patterns. One more application of descriptive analysis is to develop the captivating subgroups in the major part of the data available.
This analytics emphasis on the summarization and transformation of the data into meaningful information for reporting and monitoring.
Predictive Data Mining:
The main goal of this mining is to say something about future results not of current behaviour. It uses the supervised learning functions which are used to predict the target value. The methods come under this type of mining category are called classification, timeseries analysis and regression. Modelling of data is the necessity of the predictive analysis, and it works by utilizing a few variables of the present to predict the future not known data values for other variables.
Difference Between Descriptive and Predictive Data Mining:
S.No.  Comparison  Descriptive Data Mining  Predictive Data Mining 

1.  Basic  It determines, what happened in the past by analyzing stored data.  It determines, what can happen in the future with the help past data analysis. 
2.  Preciseness  It provides accurate data.  It produces results does not ensure accuracy. 
3.  Practical analysis methods  Standard reporting, query/drill down and adhoc reporting.  Predictive modelling, forecasting, simulation and alerts. 
4.  Require  It requires data aggregation and data mining  It requires statistics and forecasting methods 
5.  Type of approach  Reactive approach  Proactive approach 
6.  Describe  Describes the characteristics of the data in a target data set.  Carry out the induction over the current and past data so that predictions can be made. 
7.  Methods(in general) 

