5. AI AND MACHINE LEARNING VTU LAB | READ NOW

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

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# 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))
# 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))
# 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.

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# 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())
# 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())
# 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

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

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

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# 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())
# 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())
# 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

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

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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))
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))
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

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