Guideline : A$.$ The goal: forecasting the variable Y using $30$ features $($X$1,...,$X$30).$

B$.$ Use Python programming language to do all questions.

$$ Use the first $80\%$ of data for training and remaining $20\%$ of the data for prediction.

$$ Use the following performance metrics: RMSE, MAE, MAD, MAPE.

$................$

About the dataset: Dataset has $30$ explainatory variables

i$.$e$.($X$1,$X$2,...$X$30)$

and one Y to forecast

$................$

Please provide codes for python using libraries : import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.preprocessing import MinMaxScaler

import tensorflow as tf

import keras

from tensorflow.keras import layers

from keras.models import Sequential

from keras.layers import Dense, LSTM$,$ GRU, Dropout, Input

keras.utils.set$\_$random$\_$seed$(123)$

$.........................$

task:

$1.$ Implement the statistical models to forecast Y: linear regression model $($of your choice$),$ Gaussian Process.

$2.$ Implement the machine learning models to forecast Y: kNN$,$ SVR$,$ regression trees

$3.$ Implement the following deep learning models to forecast Y: CNN$,$ LSTM$,$ bi$-$LSTM$,$ GRU, Bi$-$GRU.

$4.$ Use optimization technique of your choice $($Bayesian optimization or random search$)$ to tune the predictive models. Indicate what optimization technique is used and to what models it was applied and to which parameters.

$5.$ It is suggested to consider other models$/$approaches$/$techniques to improve the results.

$6.$ For each data set, indicate the best model. Explain its superiority.

$7.$ For each data set, indicate the worst model. Explain its underperformance.