Rishabendran / 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 the necessary packages.
2. Read the given csv file and display the few contents of the data.
3. Assign the features for x and y respectively.
4. Split the x and y sets into train and test sets.
5. Convert the Alphabetical data to numeric using CountVectorizer.
6. Predict the number of spam in the data using SVC (C-Support Vector Classification) method of SVM (Support vector machine) in sklearn library.
7. Find the accuracy of the model.

Program:

/*
Program to implement the SVM For Spam Mail Detection..
Developed by: Rishabendran R
RegisterNumber:  212219040121
*/
import chardet
file = "/content/spam.csv"
with open(file,'rb') as rawdata:
	result = chardet.detect(rawdata.read(10000))
result
import pandas as pd
dataset = pd.read_csv("/content/spam.csv",encoding="windows-1252")
dataset.head()
dataset.info()
dataset.isnull().sum()
x=dataset["v1"].values
y=dataset["v2"].values
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=0)
from sklearn.feature_extraction.text import CountVectorizer 
cv=CountVectorizer() 
x_train=cv.fit_transform(x_train) 
x_test=cv.transform(x_test) 
from sklearn.svm import SVC 
svc=SVC() 
svc.fit(x_train,y_train) 
y_pred=svc.predict(x_test) 
y_pred
from sklearn import metrics 
accuracy=metrics.accuracy_score(y_test,y_pred) 
accuracy

Output:

Result:

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