Regression analysis and its application in AI

 REGRESSION

Regression analysis can be defined as a technique used to model  the relationship between a dependent variable and or more independent variables. It is fundamental in machine learning especially when you want to build models that can predict continuous values such as weather, car price etc.

Applications of Regression

1. Predictive modelling

Used to forecast future values based on historical data. This technique is mainly used by weather forecasters.

2. Time series analysis

Used to analyze data collected overtime. Say for example shop store, website traffic etc.

3. Anomaly detection

Used to identify data points that deviate significantly from expected pattern, which can explain fraud.

Python libraries and frameworks that deal with regression analysis

You can use scikit-learn or statsmodels for regression analysis in python.

Types of Regression

There are several types of regression analysis. 

1. Linear regression

The most common type of regression. Used to establish a linear relationship between a dependent variable and or more independent variables.

2. Logistic regression

Used to predict categorical outcomes i.e yes or no, instead of continuous values.

3. Polynomial

Captures more complex relationships by fitting a non-linear curve to the data.

Conclusion

Apart from the types mentioned above we have also Lasso and Ridge regression.