I am trying to find the KNN for the dataset provided but i keep getting the error "ValueError: Input contains NaN, infinity or a value too large for dtype('float64')." and i dont know why. Please help.
I am trying to find the KNN for the dataset provided but i keep getting the error "ValueError: Input contains NaN, infinity or a value too large for dtype('float64')." and i dont know why. Please help.
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Question
I am trying to find the KNN for the dataset provided but i keep getting the error "ValueError: Input contains NaN, infinity or a value too large for dtype('float64')." and i dont know why. Please help.
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![# -*- coding: utf-8 -*-
Created on Sat Apr 24 14:58:26 2021
@author: David
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import pandas as pd
def plot_decision_regions (X, y, classifier, test_idx=None, resolution=0.02):
# setup marker generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('gray', 'indigo', 'purple','yellow' , 'gray')
cmap = ListedColormap(colors [:len(np.unique(y))])
# plot the decision surface
x1_min, x1_max = X[:, 0].min() - .25, X[:, 0].max() + .25
x2_min, x2 max = X[:, 1].min() - .25, X[:, 1].max() + .25
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
z = classifier.predict(np.array( (xx1. ravel(), xx2. ravel()1).T)
Z = Z. reshape(xx1. shape)
plt.contourf(xx1, xx2, z, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max(0)
# plot all samples
X_test, y_test = X[test_idx, :), yltest_idx]
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0], y=xly == cl, 1),
alpha-0.8, с-стар (idx),
marker-markers (idx], label=cl)
# highlight test samples
if test_idx:
X_test, y_test = X[test_idx, :), yltest_idx]
pīt.scatter(X_test[:, 0], x test[:, 1], c=",
alpha=1.0, linewidth=1, marker='o',
s=55, label='test set')
# Importing the dataset
dataset = pd. read_csv(r'/Users/jaylenmealing/Downloads/VisitJamaica_today.csv', sep="a")
X = dataset.iloc[:, [4, 121].values
y = dataset.iloc[:, 6].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.fit_transform(X_test)
X_combined_std = np.vstack( (X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
knn. fit(X_train_std, y_train)
plot_decision_regions (X_combined_std, y_combined,
classifier=knn, test_idx=range(600,725))
plt.title('K-NN (Training set)')
plt.xlabel('').
plt.ylabel('')
plt.show()
# Training the K-NN model on the Training set
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
print(np.concatenate( (y_pred. reshape(len(y_pred),1), y_test.reshape(len(y_test), 1)),1))
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy = accuracy_score(y_test, y_pred)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fd3cbdb7f-3494-41c7-819d-7c68b07b6e70%2F48d5b520-5c98-47ba-8c12-c94c3bf752cd%2Ftzf1m6s_processed.png&w=3840&q=75)
Transcribed Image Text:# -*- coding: utf-8 -*-
Created on Sat Apr 24 14:58:26 2021
@author: David
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import pandas as pd
def plot_decision_regions (X, y, classifier, test_idx=None, resolution=0.02):
# setup marker generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('gray', 'indigo', 'purple','yellow' , 'gray')
cmap = ListedColormap(colors [:len(np.unique(y))])
# plot the decision surface
x1_min, x1_max = X[:, 0].min() - .25, X[:, 0].max() + .25
x2_min, x2 max = X[:, 1].min() - .25, X[:, 1].max() + .25
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
z = classifier.predict(np.array( (xx1. ravel(), xx2. ravel()1).T)
Z = Z. reshape(xx1. shape)
plt.contourf(xx1, xx2, z, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max(0)
# plot all samples
X_test, y_test = X[test_idx, :), yltest_idx]
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0], y=xly == cl, 1),
alpha-0.8, с-стар (idx),
marker-markers (idx], label=cl)
# highlight test samples
if test_idx:
X_test, y_test = X[test_idx, :), yltest_idx]
pīt.scatter(X_test[:, 0], x test[:, 1], c=",
alpha=1.0, linewidth=1, marker='o',
s=55, label='test set')
# Importing the dataset
dataset = pd. read_csv(r'/Users/jaylenmealing/Downloads/VisitJamaica_today.csv', sep="a")
X = dataset.iloc[:, [4, 121].values
y = dataset.iloc[:, 6].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.fit_transform(X_test)
X_combined_std = np.vstack( (X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
knn. fit(X_train_std, y_train)
plot_decision_regions (X_combined_std, y_combined,
classifier=knn, test_idx=range(600,725))
plt.title('K-NN (Training set)')
plt.xlabel('').
plt.ylabel('')
plt.show()
# Training the K-NN model on the Training set
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
print(np.concatenate( (y_pred. reshape(len(y_pred),1), y_test.reshape(len(y_test), 1)),1))
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy = accuracy_score(y_test, y_pred)
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