Things You Should Know About Predictive Analytics

Posted by Linda Hudson on April 24th, 2019

Predictive analytics is the use of statistical algorithms, historical data, and machine learning techniques for predicting future events. The aim is to go beyond knowing what has happened in the past to give the best assessment of what will occur in the future.

Importance of Predictive Analytics

Organizations now use predictive analytics for solving difficult problems and for uncovering new opportunities. Common uses are:

Detecting Frauds

Combining multiple analytics methods improves the pattern of detection and prevents criminal behavior. As cybersecurity is becoming a major concern, high-performance behavioral analytics examines all the actions on a network in real time to spot the abnormalities which may indicate fraud, zero-day vulnerabilities, and advanced persistent threats.

Optimizing Marketing Campaigns

The analytics is used to determine customer responses and purchases, and to promote cross-selling opportunities. Predictive models help businesses to attract, retain and grow their profitable customers.

Improving Operations

Most of the companies use predictive models for forecasting inventory and managing resources. Airline industries use it for setting the ticket prices. Hotels predict the number of guests for any given night to maximize the occupancy and increase the revenue. Predictive analytics enables businesses to function more efficiently.

Reduces Risk

Checking the credit score to assess a buyer’s likelihood of default for purchase is the most common example of analytics. A credit score is a number which is generated by the predicted model which incorporates all the relevant data required to check the creditworthiness.

Step-by-Step Workflow for a predictive analytics

1. Import data from various sources- databases, web archives, and spreadsheets.

Data sources include energy load data in a CSV file and the national weather data showing dew point and temperature.

2. Cleaning the data by removing outliers and combining the data sources.

Identify missing data, data spikes, and anomalous points to remove them from the data. Then aggregate different sources together by creating a single table having the temperature, energy load, and dew point.

3. Use statistics, curve fitting tools, or machine learning for developing an accurate predictive model.

Energy forecasting is a complex process having many variables, so you’ll have to choose neutral networks for building and training predictive model. Iterate through the training data set for trying different approaches. When the training is completed, try the model against new data to check its performance.

4. Integrate the model into the load forecasting system.

Once you have a model which accurately forecasts the load, you can move it to your production system and make the analytics available to devices and software programs including web apps, mobile devices, and server.