Advanced_Regression

Here, I used the different types of data for better understanding about the Advanced regression.

Simple Linear Regression: Adverstising dataset

Multiple Linear Regression: Advertising Dataset

Modeling non-linear relationships using data transformation: AR - Examples-1.5 (Time and Distance dataset)

Modeling non-linear relationship using Polynomial Regression: AR- Examples-1.6 (Marks Dataset)

In the python file, First I checked the relationship between the target and independent variable after that the steps is below :

• Splitting the dataset into X and y
• Building the regression
• Predictions on the basis of model
• Find the value of R-squared
• Visualizing the model fit (Regression plot)
• Model Coefficients: beta0 and beta1 (In the equation of Simple Linear Regression)
• Metrics to assess model performance (RSS, MSE, RMSE)
• Residuals Analysis
• Residuals analysis vs Predictions plots
• Distributions of errors (Distribution plot for check the normality of error of normality)
• Matrix Multiplications ($\widehat{\beta}=(X^{T}.X)^{-1}.X^{T}.Y$)
• Data transformation