In this repositery i am going to upload few notebboks on different types on Regression analysis
- Simple linear regression
- Multiple linear regression
- Polynomial regression
- SVM Regression
- Decission tree regression
- Random Forest regression
Linear regression have few assumptions.Before proceeding forward we need to make sure the data is met with assumptions
- Linearity : There should be a linear relation between independent varibles and dependent varibles
- Homoscedaticity : Residuals should be normally distrubuted and should have equal variance
- No Endogeneity : Residuals and independent variables should be correlated
- No Autocorrelation : There should be no correlation in residual terms
- No MultiCollinearity : Independent varibles should not be correlated
If the assumptions are not met we could transform the data or we could use non-linear regression model
- Simple linear regression
- Multiple linear regression
- Polynomial regression
- SVM regression
- Decission forest regression
- Random forest regression