Assignment: RNN and ConvNets $(60$ points$)$ The data file $$data$.$csv$$ contains $3$ time series x$1,$ x$2,$ and y along with the corresponding date column. The data ranges from beginning of $2019$ to the end of Feb. of $2020.$ The objective of this problem is to make predictions for y for March $1$st and $2$nd in $2020.1.$ Explore regular feedforward neural network models for this problem. $(10$ points$)($a$)$ Report the unnormalized MAE of the test set on your best model. $($b$)$ Plot the loss curves for training and validation sets for the best model. $($c$)$ What are the predicted values of y for March $1$st and March $2$nd$?2.$ Explore recurrent neural network models for this problem. $(10$ points$)($a$)$ Report the unnormalized MAE of the test set on your best model. $($b$)$ Plot the loss curves for training and validation sets for the best model. $($c$)$ What are the predicted values of y for March $1$st and March $2$nd$?3.$ Explore $1$d convolutional neural network models for this problem. $(10$ points$)($a$)$ Report the unnormalized MAE of the test set on your best model. $($b$)$ Plot the loss curves for training and validation sets for the best model. $($c$)$ What are the predicted values of y for March $1$st and March $2$nd$?$ In this problem, you need to apply all ML foundation and techniques that you$$ve learned in the program so far. These include but are not limited to: data cleaning, data imputation for missing values, full exploratory analysis along with relative visualisations, statistical tests, data normalization, data split, addressing overfitting$/$underfitting$,$ tuning hyperparameters, model assessment, etc.. Feel free to apply the techniques you$$ve learned in yo