PLEASE FIX the errors of the program it's not running

import pandas as pd

import numpy as np

from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import Perceptron

from sklearn.metrics import accuracy_score

import matplotlib.pyplot as plt

from matplotlib.colors import ListedColormap

from sklearn.model_selection import GridSearchCV

tr_data = pd.read_csv("http://venus.cs.qc.cuny.edu/~gan/cs371/hw/train.csv")

te_data = pd.read_csv("http://venus.cs.qc.cuny.edu/~gan/cs371/hw/test.csv")

tr_data = tr_data.as_matrix()

train_x = [[x[1], x[2]] for x in tr_data]

train_y = [x[0] for x in tr_data]

te_data = te_data.as_matrix()

test_x = [[x[1], x[2]] for x in te_data]

test_y = [x[0] for x in te_data]

plt.figure(figsize=(10, 6))

cm_bright = ListedColormap(['#0000FF', '#FF0000'])

clf_b = Perceptron(random_state=241)

clf_b.fit(train_x, train_y)

plot_x = [x[0] for x in test_x]

plot_y = [x[1] for x in test_x]

x_min, x_max = min(plot_x) - 1, max(plot_x) + 1

y_min, y_max = min(plot_y) - 1, max(plot_y) + 1

xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),

np.arange(y_min, y_max, 2))

z = clf_b.predict(np.c_[xx.ravel(), yy.ravel()])

z = z.reshape(xx.shape)

plt.contourf(xx, yy, z, cmap=plt.cm.coolwarm, alpha=0.8)

plt.scatter(plot_x, plot_y, c=test_y, cmap=cm_bright)

plt.title('Before scaling features accuracy = 66%')

#plt.show()

plt.figure(figsize=(10, 6))

scaler = StandardScaler()

X_train_scaled = scaler.fit_transform(train_x)

X_test_scaled = scaler.transform(test_x)

clf_a = Perceptron(random_state=241)

clf_a.fit(X_train_scaled, train_y)

plot_x = [x[0] for x in X_test_scaled]

plot_y = [x[1] for x in X_test_scaled]

x_min, x_max = min(plot_x) - 1, max(plot_x) + 1

y_min, y_max = min(plot_y) - 1, max(plot_y) + 1

xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),

np.arange(y_min, y_max, 0.02))

z = clf_a.predict(np.c_[xx.ravel(), yy.ravel()])

z = z.reshape(xx.shape)

plt.contourf(xx, yy, z, cmap=plt.cm.coolwarm, alpha=0.8)

plt.scatter(plot_x, plot_y, c=test_y, cmap=cm_bright)

plt.title('After scaling features accuracy = 85%')

predicted = clf_b.predict(test_x)

before_scale = accuracy_score(test_y, predicted)

predicted = clf_a.predict(X_test_scaled)

after_scale = accuracy_score(test_y, predicted)

plt.show()

from sklearn.svm import SVC

from sklearn.model_selection import GridSearchCV

from sklearn.model_selection import train_test_split

from sklearn.datasets import make_circles

from sklearn.model_selection import StratifiedShuffleSplit

X, y = make_circles(n_samples=300, noise=0.2, factor=0.5, random_state=241)

X = scaler.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

cv = StratifiedShuffleSplit(n_splits=5, test_size=0.25, random_state=241)

C_range = np.logspace(-5, 5, num=10)

gamma_range = np.logspace(-8, 3, num=11)

parametrs = dict(kernel=['rbf'], gamma=gamma_range, C=C_range)

grid = GridSearchCV(SVC(), param_grid=parametrs, cv=cv)

grid.fit(X_train, y_train)

x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5

y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5

xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),

np.arange(y_min, y_max, 0.02))

plt.figure(figsize=(10, 4))

plt.subplot(1, 2, 1)

plt.xticks(())

plt.yticks(())

z = grid.predict(np.c_[xx.ravel(), yy.ravel()])

predicted = grid.predict(X_test)

z = z.reshape(xx.shape)

plt.contourf(xx, yy, z, cmap=plt.cm.coolwarm, alpha=0.8)

plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright)

plt.title("SVM with RBF kernel. Accuracy = %.2f" % (accuracy_score(y_test, predicted)))

plt.subplot(1, 2, 2)

plt.xticks(())

plt.yticks(())

clf = Perceptron(random_state=241)

clf.fit(X_train, y_train)

z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

predicted = clf.predict(X_test)

z = z.reshape(xx.shape)

plt.contourf(xx, yy, z, cmap=plt.cm.coolwarm, alpha=0.8)

plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright)

plt.title("Perceptron. Accuracy = %.2f" % (accuracy_score(y_test, predicted)))

plt.show()

plt.figure(figsize=(12, 8))

C_range = [0.1, 1, 10]

gamma_range = [0.01, 0.1, 1]

k = 1

for C in C_range:

for gamma in gamma_range:

plt.subplot(len(C_range), len(gamma_range), k)

plt.xticks(())

plt.yticks(())

k = k + 1

clf = SVC(kernel='rbf', C=C, gamma=gamma)

clf.fit(X_train, y_train)

z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

z = z.reshape(xx.shape)

plt.contourf(xx, yy, z, cmap=plt.cm.coolwarm, alpha=0.8)

plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright)

predicted = clf.predict(X_test)

acc = accuracy_score(y_test, predicted)

plt.title("gamma=%.2f, C=%.2f acc=%.2f" % (gamma, C, acc), size='medium')

plt.show()

1, Compare different algorithms of Sklearn (svm, DecisionTree, Naive Bayes, Stochastic Gradient Descent, Multi-layer Perceptron) on train.csv and test.csv. 2, Report the final prediction result of test set train on train set using different machine learning algorithms. There are five different label for each instance which are openness, conscientiousness, extraversion, agreeableness and neuroticism. Report the prediction result of each of them independently. (Multi class classification)