Predictive analytics
- 2. What is Predictive Analytics It is used to make prediction about unknown future events. It uses many techniques from data mining, machine learning, statistics, artificial intelligence.
- 3. Why is predictive analytics important?
- 4. Process
- 5. Common Predictive Analytics Methods Regression Regression models predict a number – for example, how much revenue a customer will generate over the next year or the number of months before a component will fail on a machine. Classification Classification models predict class membership. For instance, if we try to classify whether someone is likely to leave, whether he will respond to a thing, whether he’s a good or bad credit risk, etc. Usually, the model results are in the form of 0 or 1, with 1 being the event we are targeting.
- 7. Linear Regression It is one of the most widely used modeling technique. In this technique, the dependent variable is continuous, independent variable(s) can be continuous or discrete and nature of regression line is linear. Linear Regression establishes a relationship between dependent variable (Y) and one or more independent variables (X) using a best fit straight line (regression line).
- 8. Equation Y=a+bX Y=the dependent variable a=intercept b=slope x=the independent variable
- 9. EXAMPLE X is independent data Y is dependent data Simple Linear Regression
- 10. Multiple Linear Regression Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. We could use multiple regression to understand whether daily cigarette consumption can be predicted based on smoking duration, age when started smoking, smoker type, income and gender.
- 11. Equation
- 12. Example y1= -1.30365 + 0.79291 *x1 + 0.75452 *x2 Sample data Y vs y1 Y vs y1
- 13. Application Banking and Financial Services. Retail Government & Public Sector Manufacturing Health Care etc.
- 14. Thank You!!