Input Attributes Output Example Alt Bar Fri Hun Pat Price Rain Res Туре Est Will Wait X1 Yes No No Yes Some $$$ No Yes X2 Yes No No Yes Full $ SS French 0-10 y₁ = Yes No Thai 30-60 y2 = No X3 No Yes No No Some $ No No Burger 0-10 y3 = Yes X4 Yes No Yes Yes Full $ Yes No Thai 10-30 Y4 = Yes X5 Yes No Yes No Full $$$ No Yes French >60 y5 = No X6 No Yes No Yes Some $$ Yes Yes Italian 0-10 Y6 = Yes X7 No Yes No No None $ Yes No Burger 0-10 y7 = No X8 No No No Yes Some $$ Yes Yes Thai 0-10 y8 = Yes X9 No Yes Yes No Full $ Yes No Burger >60 y9 = No X10 Yes Yes Yes Yes Full $$$ No Yes Italian 10-30 y10 = No X11 No No No No None $ No No Thai 0-10 Y11=No X12 Yes Yes Yes Yes Full $ No No Burger 30-60 Y12 = Yes Apply Naïve Bayes Classifier to classify the following two samples. : import pandas as pd file_path='/Users//Downloads/Data 2/ExampleTrainDataset.csv' filepath2 = '/Users//Downloads/Data data = pd.read_csv(file_path) data2 = pd.read_csv(filepath2) 2/ExampleTestDataset.csv' print(data) print(data2) x1 x2 x3 Y 012345678 2 3.0 2 0 2 3.0 4 0 2 3 9.0 1 0 3 1 0.5 2 0 4 2.0 1 0 5 7 2.0 1 1 3 2.0 5 1 5 2.0 2 1 8 2 4.0 3 1 2 226 11234 9 3 2.0 0 x1 1 1 x2 x3 Y 2 6 3 7 4 3 32243 3 1 0 2 4 0 2 2 1 4 3 31 3 1 1 1

Programming langauge: Python Prompt: Pick a genre of your choice and provide average movie’s ratings by the following four time intervals during which the movies were released withiin 10 years group (i.e. 1970 to 1979, 1980 to 1989, 1990 to 1999,  2000 to 2009) Mock dataframe: *attached screenshot*

in c++ programming plz

import numpy as npfrom scipy.optimize import minimizeimport matplotlib.pyplot as pltimport pickle #Implement the function calAnaliticalSolution def calAnaliticalSolution(X,y):    # Inputs:                                                             # X = N x d     # y = N x 1                                                                   # Output:     # w = d x 1 (which is the parameter calculated from data X and label y using alalytical solution)​    # IMPLEMENT THIS METHOD - REMOVE THE NEXT LINE    w = np.zeros((X.shape[0],1))​    return w #Implement the function calRegressionError def calRegressionError(w,Xtest,ytest):    # Inputs:    # w = d x 1    # Xtest = N x d    # ytest = N x 1    # Output:    # mse = scalar value (which is the error of the regression model)​    # IMPLEMENT THIS METHOD - REMOVE THE NEXT LINE    mse = 0​    return mse Xtrain,ytrain,Xtest,ytest = pickle.load(open('diabetes.pickle','rb'))  ​x1 = np.ones((len(Xtrain),1))x2 = np.ones((len(Xtest),1))​Xtrain_i =…