We all know the movie “Minority Report” starring Tom Cruise in which he is able to predict crimes before they even happen. Now fast-forward to 2017 and we have so much of data, brute processing power and sheer analytical superiority that “Minority Report” might just move out of the realm of fiction and enter our everyday life. Welcome to the world of predictive analytics and its insanely powerful ways of predicting crimes before they even happen!
Predictive analytics is the process of using Big Data, highly efficient statistical algorithms, machine learning techniques and more in order to predict the future with a heightened degree of accuracy. The branch of predictive analytics has been around for some time now but only now has it got the arsenal to come up with accurate prediction. Today predictive analytics involves data mining, data modeling, statistics, artificial intelligence among other things.
Get 100% Hike!
Master Most in Demand Skills Now!
Some of the reasons why predictive analytics is so pervasive today are directly as a consequence of :
- Unprecedented volumes of data
- Cheaper and powerful computing
- Software that is better and easy to use
- More precise data modeling techniques
- Faster result achievement and action delivery
Predictive data modeling uses known results in order to train a model that can then predict values for a new and different set of data. This type of predictive modeling provides results that represent the probability of a targeted variable. It is more of a diagnostic model that lets you understand key relationships and find out why something happened.
The inner workings of predictive analytics in crime prevention
The first and foremost thing for predictive analytics is the availability of huge amounts of data and based on that everything else is built. In the case of crime prevention, first the huge amount of data related to crimes is fed into the system. This includes all the cities and towns where policing is done and crime prevention is a necessity.
The criminal history of the cities is noted, the profile of the perpetuator of crime is built, the neighborhoods with the highest crime rates, the reasons for these crimes, the most likely profile of those that can be victims of the crime and such other data from multiple angles are all collated. Using this data is modeled, various analysis techniques are built and upon that the most likely predictions are taken into consideration for the future crime prevention steps.
Enroll in this Machine Learning Training in Noida and master building predictive models.
Common Predictive Analysis Techniques and their uses :
| Technique | Deployment | Type |
| Logistic Regression | Assigning records to predefined class, predicting outcome | Classification |
| Linear Regression | Predicting continuous numerical outcome | Forecasting |
| Neural Networks | Estimating and classifying users | Prediction |
| Market Basket Analysis | Rule-based analysis for determining relationship | Affinity Grouping |
There is a need for powerful software tools without which the whole process of predictive analytics does not hold much ground. This software parses petabytes of data for recognizing patterns, the various relationship between various factors affecting the rise of crime rates, the complex interplay of different attributes that can show a clear sign of the plausibility of a certain crime at a certain place at a certain time are all pursued thanks to the power of advanced software.
This kind of a power at the fingertips of the policing professionals can at times be quite overwhelming. So due to this a different set of software that can convert all this information into easily digestible bit-sized nuggets of insights can be created. This could be in the form of bars charts, graphs, pie charts, interactive dashboards, reports and such other visually rich formats. All this aids and speeds the process of getting the right knowledge at the right time in order to prevent crime and mitigate the risks involved.
Looking to master Predictive Analytics? Check the Intellipaat comprehensive Data Analytics training.