PROG8

-

 from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

from sklearn import datasets


# Load dataset

iris = datasets.load_iris()

print("Iris Data set loaded...")


# Split the data into train and test samples

x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.1)

print("Dataset is split into training and testing...")

print("Size of training data and its label", x_train.shape, y_train.shape)

print("Size of testing data and its label", x_test.shape, y_test.shape)


# Prints Label no. and their names

for i in range(len(iris.target_names)):

    print("Label", i, "-", str(iris.target_names[i]))


# Create object of KNN classifier

classifier = KNeighborsClassifier(n_neighbors=12)


# Perform Training

classifier.fit(x_train, y_train)


# Perform testing

y_pred = classifier.predict(x_test)


# Display the results

print("Results of Classification using K-nn with K=12 ")

for r in range(0, len(x_test)):

    print(" Sample:", str(x_test[r]), " Actual-label:", str(y_test[r]), " Predicted-label:", str(y_pred[r]))


print("Classification Accuracy:", classifier.score(x_test, y_test))


Comments

Post a Comment

If you have any doubts let me know in comments

Popular posts from this blog

PROG9

-
 import matplotlib.pyplot as plt import pandas as pd import numpy as np def kernel(point, xmat, k):     m, n = np.shape(xmat)     weights = np.mat(np.eye((m)))  # eye - identity matrix     for j in range(m):         diff = point - X[j]         weights[j, j] = np.exp(diff * diff.T / (-2.0 * k**2))     return weights def localWeight(point, xmat, ymat, k):     wei = kernel(point, xmat, k)     W = (X.T * (wei * X)).I * (X.T * (wei * ymat.T))     return W def localWeightRegression(xmat, ymat, k):     m, n = np.shape(xmat)     ypred = np.zeros(m)     for i in range(m):         ypred[i] = xmat[i] * localWeight(xmat[i], xmat, ymat, k)     return ypred def graphPlot(X, ypred):     sortindex = X[:, 1].argsort(0)  # argsort - index of the smallest     xsort = X[sortindex][:, 0]     fig = plt.figure()     ax = fig.add_subplot(1, 1, 1)     ax.scatter(bill, tip, color='green')     ax.plot(xsort[:, 1], ypred[sortindex], color='red', linewidth=5)     plt.xlabel('Total bill')  
Read more

ADDING TABLE IN HTML

-
Image
TABLE IN HTML. We can create a Table in html and can list many items in a systematic way which looks good and neat whenever some one visits your page. there are few important tags in creation of a table. let us analyse them <table border="1"> through this tag we can set the border thickness,style and all... <tr> this specifies table row. </tr> closing table row. <th> table head it specifies the top heading of an item list. </th> closes the table head. lets see an example CODE OUTPUT
Read more