Precision, Recall and F1-Score

 

import seaborn as sns

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score

# Assuming 'Outcome' is the target variable in your dataset

X_train, X_test, y_train, y_test = train_test_split(diabetes.loc[:, diabetes.columns != 'Outcome'], diabetes['Outcome'], stratify=diabetes['Outcome'], random_state=66)

# Create and train the KNN classifier

knn = KNeighborsClassifier(n_neighbors=9)

knn.fit(X_train, y_train)

# Print accuracy on training and test sets

print('Accuracy of K-NN classifier on training set: {:.2f}'.format(knn.score(X_train, y_train)))

print('Accuracy of K-NN classifier on test set: {:.2f}'.format(knn.score(X_test, y_test)))

# Generate and print confusion matrix

y_pred = knn.predict(X_test)

conf_matrix = confusion_matrix(y_test, y_pred)

print('\nConfusion Matrix:\n', conf_matrix)

# Calculate precision, recall, and f1-score

precision = precision_score(y_test, y_pred)

recall = recall_score(y_test, y_pred)

f1 = f1_score(y_test, y_pred)

print('\nPrecision: {:.2f}'.format(precision))

print('Recall: {:.2f}'.format(recall))

print('F1 Score: {:.2f}'.format(f1))

# Create a graphical representation of the confusion matrix using seaborn

plt.figure(figsize=(8, 6))

sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['No Diabetes', 'Diabetes'], yticklabels=['No Diabetes', 'Diabetes'])

plt.xlabel('Predicted')

plt.ylabel('Actual')

plt.title('Confusion Matrix')

plt.show()

Confusion Matrix - Graphical

 

import seaborn as sns

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

from sklearn.metrics import confusion_matrix

# Assuming 'Outcome' is the target variable in your dataset

X_train, X_test, y_train, y_test = train_test_split(diabetes.loc[:, diabetes.columns != 'Outcome'], diabetes['Outcome'], stratify=diabetes['Outcome'], random_state=66)

# Create and train the KNN classifier

knn = KNeighborsClassifier(n_neighbors=9)

knn.fit(X_train, y_train)

# Print accuracy on training and test sets

print('Accuracy of K-NN classifier on training set: {:.2f}'.format(knn.score(X_train, y_train)))

print('Accuracy of K-NN classifier on test set: {:.2f}'.format(knn.score(X_test, y_test)))

# Generate and print confusion matrix

y_pred = knn.predict(X_test)

conf_matrix = confusion_matrix(y_test, y_pred)

print('\nConfusion Matrix:\n', conf_matrix)

# Create a graphical representation of the confusion matrix using seaborn

plt.figure(figsize=(8, 6))

sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['No Diabetes', 'Diabetes'], yticklabels=['No Diabetes', 'Diabetes'])

plt.xlabel('Predicted')

plt.ylabel('Actual')

plt.title('Confusion Matrix')

plt.show()

Confusion Matrix

 

from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

from sklearn.metrics import confusion_matrix

# Assuming 'Outcome' is the target variable in your dataset

X_train, X_test, y_train, y_test = train_test_split(diabetes.loc[:, diabetes.columns != 'Outcome'], diabetes['Outcome'], stratify=diabetes['Outcome'], random_state=66)

# Create and train the KNN classifier

knn = KNeighborsClassifier(n_neighbors=9)

knn.fit(X_train, y_train)

# Print accuracy on training and test sets

print('Accuracy of K-NN classifier on training set: {:.2f}'.format(knn.score(X_train, y_train)))

print('Accuracy of K-NN classifier on test set: {:.2f}'.format(knn.score(X_test, y_test)))

# Generate and print confusion matrix

y_pred = knn.predict(X_test)

conf_matrix = confusion_matrix(y_test, y_pred)

print('\nConfusion Matrix:\n', conf_matrix)

Apriori Algorithm

 Hi all

Apriori Algorithm

 import numpy as nm

import matplotlib.pyplot as mtp  
import pandas as pd 
from apyori import apriori  
import urllib.request

url="http://localhost/web/hupaddpairs.php?item1="

dataset = pd.read_csv('item_set1.csv')  
transactions=[]  
for i in range(0, 32):
    transactions.append([str(dataset.values[i,j])  for j in range(0,5)]) 

vals=""
rules= apriori(transactions= transactions, min_support=0.003, min_confidence = 0.2, min_lift=3, min_length=2, max_length=2) 
results= list(rules)  
for item in results:
    pair = item[0]   
    items = [x for x in pair]  
    print("Rule: " + items[0] + " -> " + items[1])  
    print("Support: " + str(item[1]))  
    print("Confidence: " + str(item[2][0][2]))  
    print("Lift: " + str(item[2][0][3]))  
    print("=====================================")  
    vals=url+items[0]+"&item2="+items[1]
    print(vals)
    webUrl = urllib.request.urlopen(vals)
    vals=""

-------------------

Data

item_set1.csv

electronics.smartphone,	electronics.video.tv			
electronics.smartphone,	electronics.video.tv,appliances.kitchen.washer		
electronics.smartphone,	electronics.audio.headphone			
electronics.audio.headphone,electronics.smartphone	appliances.environment.vacuum,kids.skates	

Call a php program from Python

 Dear all

import urllib

url="http://localhost/huphand/huphand.php?code="

i=i+1

url=url+str(i)

webUrl = urllib.request.urlopen(url)

How to load model using keras in Python

 qn) How to load model using keras in Python

Ans)

from keras.models import load_model

modelmain = load_model("hup_bacteria_lstm.h5")

print(modelmain.summary())

How to save model using keras in Python

 Qn) How to save model using keras in Python

Ans)

model.save("hup_bacteria_lstm.h5")