Thanks a lot, sir. That was very well explained.

DONE

Simple linear regression is a statistical method used to model the relationship between two variables by fitting a linear equation to observed data. It involves one independent variable (predictor) and one dependent variable (outcome). The equation of the line is typically expressed as Y=a+bXY=a+bX, where YY is the predicted value, aa is the y-intercept, bb is the slope of the line, and XX is the independent variable. The goal is to minimize the difference between the observed values and the values predicted by the model, often using the least squares method. This technique is widely used for forecasting and understanding relationships in various fields such as economics, biology, and social sciences.

done once again

I learned about Regression: --> Linear regression and its types: 1: Simple Linear regression 2: Multiple Linear regression --> How to fit the model --> How to predict the model --> Evaluation metrics: Mean Square Error (MSE), Root Mean Square Error (RMSE), R-squared (R2)

https://www.kaggle.com/muhammadrameez242 , ye meri kaggle ki I'd hai yha par Ap mukamal assignments simple roman urdu mein mill jai gi codes mein

Done

Done

maza aya

Well explained lecture.. pure knowledge in easy way