Done

Done

I enjoyed a lot Aamar baba

I learned in this video (1-MAE, 2-MSE, 3-RMSE, 4-R-squared, 5-Adjusted R-squared, and 6-MAPE).

I learned in this lecture (MAE, MSE, RMSE, R-squared, Adjusted R-squared, and MAPE).

To make the right regression model we need to find the different values from these metrics (1) MAE - Mean Absolute Error (2) MSE - Mean Squared Error (3) RMSE - Root Mean Squared Error (4) R^2 - R-Squared Error (5) AdjR62 - Adjusted R-Squared Error (6) ME - Mean Error (6) MAPE - Mean Absolute Percentage Error.

AOA, I also learned about Regression metrics which are1-MAE The absolute difference between prediction and actual observation Interpretation: lower values are better. A value of 0 indicates no error Value cannot be negative2-MSE Average squared difference between the estimated value and actual value Interpretation: Like MAE, lower values are better MSE is more sensitive to outliers than MAE3-RMSE The square root of the mean of the squared errors Interpretation: RMSE is more sensitive to outliers than MAE4-R-squared Coefficient of determination Interpretation: value ranges from 0 to 1. A higher R-squared indicates a better fit between the model and the data5-Adjusted R-squared A modified version of R-squared Adjust the number of predictors in the model Interpretation: compares the explanatory power of regression model that contain different numbers of predictors6-MAPE Provides error in terms of percentage Can be infinite or undefined for y_i = 0And I also learned about how to choose the right metrics for regression.ALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey,Ameen.