Answer to Question #322920 in Python for Shilpa

Question

Naïve-Bayes Assignment

1. Load the dataset using pandas

2. Extract data fromOutcome column is a variable named Y

3. Extract data from every column except Outcome column in a variable named X

4. Divide the dataset into two parts for training and testing in 70% and 30% proportion

5. Create and train Naïve Bayes Model on training set

6. Make predictions based on the testing set using the trained model

7. Check the performance by calculating the confusion matrix and accuracy score of the model

Answer 1

# Import statements 
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split 
from sklearn.metrics import accuracy_score, confusion_matrix
import pandas as pd

# Read the data.
data = pd.read_csv('data.csv')
# Assign the features to the variable X, and the labels to the variable Y. 
X = data[:,:-1]
Y = data['Outcome']
#split the data
X_train,X_test,Y_train,Y_test = train_test_split(X,Y, test_size=0.3)

model = GaussianNB()

# TODO: Fit the model.
model.fit(X_train,Y_train)
# TODO: Make predictions. Store them in the variable y_pred.
y_pred = model.predict(X_test)

# TODO: Calculate the accuracy and assign it to the variable acc.
acc = accuracy_score(Y_test,y_pred)
cm = confusion_matrix(y_pred,Y_test)

print(cm)
print(acc)