vasanthamukilan / implementation-of-svm-for-spam-mail-detection

Implementation-of-SVM-For-Spam-Mail-Detection

AIM:

To write a program to implement the SVM For Spam Mail Detection.

Equipments Required:

1. Hardware – PCs
2. Anaconda – Python 3.7 Installation / Jupyter notebook

Algorithm

1. Import neccessary libraries required.
2. Load the dataset using pd.read_csv.
3. Use CountVectorizer to convert text data into a matrix of token counts.
4. Create an SVM model with a linear kernel.
5. Print the accuracy and classification report.

Program:

/*
Program to implement the SVM For Spam Mail Detection..
Developed by: Vasanthamukilan M
RegisterNumber: 212222230167
*/
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import svm
from sklearn.metrics import classification_report, accuracy_score
df=pd.read_csv('/content/spam.csv',encoding='ISO-8859-1')
df.head()
vectorizer =CountVectorizer()
x=vectorizer.fit_transform(df['v2'])
y=df['v1']
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,random_state=42)
model=svm.SVC(kernel='linear')
model.fit(x_train,y_train)
predictions=model.predict(x_test)
print("Accuracy:",accuracy_score(y_test,predictions))
print("Classification Report:")
print(classification_report(y_test,predictions))

Output:

i.) Dataset:

ii.) Accuracy and Classification report:

Result:

Thus the program to implement the SVM For Spam Mail Detection is written and verified using python programming.