5. AI AND MACHINE LEARNING VTU LAB | READ NOW → VTULOOP

MACHINE LEARNING VTU LAB- NAÏVE BAYESIAN CLASSIFIER

Program 5. WRITE A PROGRAM TO IMPLEMENT THE NAÏVE BAYESIAN CLASSIFIER FOR A SAMPLE TRAINING DATA SET STORED AS A .CSV FILE. COMPUTE THE ACCURACY OF THE CLASSIFIER, CONSIDERING FEW TEST DATA SETS.

Program Code – lab5.py

# import necessary libarities
import pandas as pd
from sklearn import tree
from sklearn.preprocessing import LabelEncoder
from sklearn.naive_bayes import GaussianNB

# load data from CSV
data = pd.read_csv('tennisdata.csv')
print("THe first 5 values of data is :\n",data.head())

# obtain Train data and Train output
X = data.iloc[:,:-1]
print("\nThe First 5 values of train data is\n",X.head())

y = data.iloc[:,-1]
print("\nThe first 5 values of Train output is\n",y.head())

# Convert then in numbers 
le_outlook = LabelEncoder()
X.Outlook = le_outlook.fit_transform(X.Outlook)

le_Temperature = LabelEncoder()
X.Temperature = le_Temperature.fit_transform(X.Temperature)

le_Humidity = LabelEncoder()
X.Humidity = le_Humidity.fit_transform(X.Humidity)

le_Windy = LabelEncoder()
X.Windy = le_Windy.fit_transform(X.Windy)

print("\nNow the Train data is :\n",X.head())

le_PlayTennis = LabelEncoder()
y = le_PlayTennis.fit_transform(y)
print("\nNow the Train output is\n",y)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.20)

classifier = GaussianNB()
classifier.fit(X_train,y_train)

from sklearn.metrics import accuracy_score
print("Accuracy is:",accuracy_score(classifier.predict(X_test),y_test))

MACHINE LEARNING Program Execution – lab5.ipynb

Jupyter Notebook program execution.

# import necessary libarities
import pandas as pd
from sklearn import tree
from sklearn.preprocessing import LabelEncoder
from sklearn.naive_bayes import GaussianNB

# load data from CSV
data = pd.read_csv('tennisdata.csv')
print("THe first 5 values of data is :\n",data.head())

THe first 5 values of data is :
Outlook Temperature Humidity Windy PlayTennis
0 Sunny Hot High False No
1 Sunny Hot High True No
2 Overcast Hot High False Yes
3 Rainy Mild High False Yes
4 Rainy Cool Normal False Yes

# obtain Train data and Train output
X = data.iloc[:,:-1]
print("\nThe First 5 values of train data is\n",X.head())

The First 5 values of train data is
Outlook Temperature Humidity Windy
0 Sunny Hot High False
1 Sunny Hot High True
2 Overcast Hot High False
3 Rainy Mild High False
4 Rainy Cool Normal False

y = data.iloc[:,-1]
print("\nThe first 5 values of Train output is\n",y.head())

The first 5 values of Train output is
0 No
1 No
2 Yes
3 Yes
4 Yes
Name: PlayTennis, dtype: object

# Convert then in numbers 
le_outlook = LabelEncoder()
X.Outlook = le_outlook.fit_transform(X.Outlook)

le_Temperature = LabelEncoder()
X.Temperature = le_Temperature.fit_transform(X.Temperature)

le_Humidity = LabelEncoder()
X.Humidity = le_Humidity.fit_transform(X.Humidity)

le_Windy = LabelEncoder()
X.Windy = le_Windy.fit_transform(X.Windy)

print("\nNow the Train data is :\n",X.head())

Now the Train data is :
Outlook Temperature Humidity Windy
0 2 1 0 0
1 2 1 0 1
2 0 1 0 0
3 1 2 0 0
4 1 0 1 0

le_PlayTennis = LabelEncoder()
y = le_PlayTennis.fit_transform(y)
print("\nNow the Train output is\n",y)

Now the Train output is
[0 0 1 1 1 0 1 0 1 1 1 1 1 0]

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.20)

classifier = GaussianNB()
classifier.fit(X_train,y_train)

from sklearn.metrics import accuracy_score
print("Accuracy is:",accuracy_score(classifier.predict(X_test),y_test))

Accuracy is: 0.6666666666666666

Download Dataset

Dataset