ml2

#practicals no 2 email spam



import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn import metrics

df=pd.read_csv('emails.csv')

df.head()

df.columns

df.isnull().sum()

df.dropna(inplace = True)

df.drop(['Email No.'],axis=1,inplace=True)
X = df.drop(['Prediction'],axis = 1)
y = df['Prediction']

from sklearn.preprocessing import scale
X = scale(X)
# split into train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)


from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=7)
 
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)

print("Prediction",y_pred)

print("KNN accuracy = ",metrics.accuracy_score(y_test,y_pred))

print("Confusion matrix",metrics.confusion_matrix(y_test,y_pred))

# cost C = 1
model = SVC(C = 1)

# fit
model.fit(X_train, y_train)

# predict
y_pred = model.predict(X_test)

metrics.confusion_matrix(y_true=y_test, y_pred=y_pred)

print("SVM accuracy = ",metrics.accuracy_score(y_test,y_pred))