Predictive analysis and its applications
Predictive analytics is an umbrella term. It refers to all those processes which we are using to predict future by data. Predictive analysis uses several techniques which includes data mining, Statistics modeling, machine learning and Artificial Intelligence.
There are following processes which involves Predictive analysis
Define project : Define the project outcomes, deliverable, scope of the effort, business objectives, identify the data sets that are going to be used.
Data collection : Data mining for predictive analytics prepares data from multiple sources for analysis. This provides a complete view of customer interactions.
Data analysis : Data Analysis is the process of inspecting, cleaning and modelling data with the objective of discovering useful information, arriving at conclusion
Statistics : Statistical Analysis enables to validate the assumptions, hypothesis and test them using standard statistical models.
Modelling : Predictive modelling provides the ability to automatically create accurate predictive models about future. There are also options to choose the best solution with multi-modal evaluation.
Deployment : Predictive model deployment provides the option to deploy the analytical results into everyday decision making process to get results, reports and output by automating the decisions based on the modelling.
Model monitoring : Models are managed and monitored to review the model performance to ensure that it is providing the results expected.
Predictive analysis Hypothetical case study:
ABC Hospital take initiative to improve their patient experience and transform their process from data driven approach .Initially they develop patient feed back App and ask some questions to patient and attendants who visit the hospital .They ask patients about Emergency services , On Time Doctors Round , Call bell Services Nursing services,Radiology services and Laboratory services basis on these they set the scoring for each questions and collect the data. Once it gets collected and data gets uploaded to database, then they do data cleaning ,analysis and statistical modeling on collected data and start to improve their service on available facts and figures .
When they collected around 2000+ data now they try to build a model which gives them real time picture on dashboard weather the patient is happy or sad and also measures which service impacts more to make patient Happy or Sad .It also Predict the transfer rate from existing hospital to other hospital due to lack of services
First they developed Regression model and later on they keeps improving and use Decision trees and Random Forest .
So after implementing the Predictive Analytics on their collected data Hospital is able to predict Patient Satisfaction and their chances to transfer/left the hospital because of hospital services.