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Linear Regression Implement Linear Regression on the given Dataset. You need to implement gradient descent from scratch i.e. you cannot use any libraries for training the model (You may use numpy, but libraries like sklearn are not allowed).

Linear Regression Implement Linear Regression on the given Dataset. You need to implement gradient descent from scratch i.e. you cannot use any libraries for training the model (You may use numpy, but libraries like sklearn are not allowed).

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Linear Regression Implement Linear Regression on the given Dataset. You need to implement gradient descent from scratch i.e. you cannot use any libraries for training the model (You may use numpy, but libraries like sklearn are not allowed). Dataset: Housing Price Prediction Dataset (a) Section B (Scratch Implementation) (d) You will need to perform K-Fold cross-validation (K=2-5) in this exercise (implement from scratch). What is the optimal value of K? Justify it in your report along with the table for the mean RMSE of K-values and K-value. (b) / Plot the RMSE V/s iteration graph for all models trained with optimal value of K for K-Fold cross-validation. RMSE should be reported on the train and val set. (c) Iodify your Regression implementation by including L1 (LASSO) and L2 (Ridge Regression) regularization. Implement both regularization functions from scratch and train the model again. Try different values of the regularization pa- rameter and report the best one. Plot similar RMSE V/s iteration graph as before (train and val loss). 7 Implement the normal equation (closed form) for linear regression and get the optimal parameters directly for each fold (optimal K). Report the RMSE on respective validation sets.
Transcribed Image Text:Linear Regression Implement Linear Regression on the given Dataset. You need to implement gradient descent from scratch i.e. you cannot use any libraries for training the model (You may use numpy, but libraries like sklearn are not allowed). Dataset: Housing Price Prediction Dataset (a) Section B (Scratch Implementation) (d) You will need to perform K-Fold cross-validation (K=2-5) in this exercise (implement from scratch). What is the optimal value of K? Justify it in your report along with the table for the mean RMSE of K-values and K-value. (b) / Plot the RMSE V/s iteration graph for all models trained with optimal value of K for K-Fold cross-validation. RMSE should be reported on the train and val set. (c) Iodify your Regression implementation by including L1 (LASSO) and L2 (Ridge Regression) regularization. Implement both regularization functions from scratch and train the model again. Try different values of the regularization pa- rameter and report the best one. Plot similar RMSE V/s iteration graph as before (train and val loss). 7 Implement the normal equation (closed form) for linear regression and get the optimal parameters directly for each fold (optimal K). Report the RMSE on respective validation sets.
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