Spam Email Detection¶

Source Code

Imports¶

In [1]:

import warnings
warnings.simplefilter('ignore')

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score

import warnings warnings.simplefilter('ignore') import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score

In [2]:

from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier

from sklearn.ensemble import StackingClassifier

from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import StackingClassifier

Load Dataset¶

In [3]:

df =  pd.read_csv("https://raw.githubusercontent.com/KalyanM45/Spam-Email-Detection/refs/heads/main/Data%20Source/SPAM.csv")
df

df = pd.read_csv("https://raw.githubusercontent.com/KalyanM45/Spam-Email-Detection/refs/heads/main/Data%20Source/SPAM.csv") df

Out[3]:

	Category	Message	Unnamed: 2	Unnamed: 3	Unnamed: 4
0	ham	Go until jurong point, crazy.. Available only ...	NaN	NaN	NaN
1	ham	Ok lar... Joking wif u oni...	NaN	NaN	NaN
2	spam	Free entry in 2 a wkly comp to win FA Cup fina...	NaN	NaN	NaN
3	ham	U dun say so early hor... U c already then say...	NaN	NaN	NaN
4	ham	Nah I don't think he goes to usf, he lives aro...	NaN	NaN	NaN
...	...	...	...	...	...
5567	spam	This is the 2nd time we have tried 2 contact u...	NaN	NaN	NaN
5568	ham	Will Ì_ b going to esplanade fr home?	NaN	NaN	NaN
5569	ham	Pity, * was in mood for that. So...any other s...	NaN	NaN	NaN
5570	ham	The guy did some bitching but I acted like i'd...	NaN	NaN	NaN
5571	ham	Rofl. Its true to its name	NaN	NaN	NaN

5572 rows × 5 columns

In [4]:

data = df.drop(labels=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1)

data = df.drop(labels=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1)

In [5]:

data.isnull().sum()

data.isnull().sum()

Out[5]:

	0
Category	0
Message	0

dtype: int64

In [6]:

data.dtypes

data.dtypes

Out[6]:

	0
Category	object
Message	object

dtype: object

In [7]:

data.loc[data['Category'] == 'spam', 'Category',] = 0
data.loc[data['Category'] == 'ham', 'Category',] = 1

data.loc[data['Category'] == 'spam', 'Category',] = 0 data.loc[data['Category'] == 'ham', 'Category',] = 1

In [8]:

data['Category'].value_counts()

data['Category'].value_counts()

Out[8]:

	count
Category
1	4825
0	747

dtype: int64

In [9]:

X = data['Message']

Y = data['Category']

X = data['Message'] Y = data['Category']

In [10]:

X

X

Out[10]:

	Message
0	Go until jurong point, crazy.. Available only ...
1	Ok lar... Joking wif u oni...
2	Free entry in 2 a wkly comp to win FA Cup fina...
3	U dun say so early hor... U c already then say...
4	Nah I don't think he goes to usf, he lives aro...
...	...
5567	This is the 2nd time we have tried 2 contact u...
5568	Will Ì_ b going to esplanade fr home?
5569	Pity, * was in mood for that. So...any other s...
5570	The guy did some bitching but I acted like i'd...
5571	Rofl. Its true to its name

5572 rows × 1 columns

dtype: object

In [11]:

Y

Y

Out[11]:

	Category
0	1
1	1
2	0
3	1
4	1
...	...
5567	0
5568	1
5569	1
5570	1
5571	1

5572 rows × 1 columns

dtype: object

Preprocess Data¶

In [12]:

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)

In [13]:

print(X.shape)
print(X_train.shape)
print(X_test.shape)

print(X.shape) print(X_train.shape) print(X_test.shape)

(5572,)
(4457,)
(1115,)

In [14]:

feature_extraction = TfidfVectorizer(min_df = 1, stop_words='english', lowercase=True)

feature_extraction = TfidfVectorizer(min_df = 1, stop_words='english', lowercase=True)

In [15]:

X_train_features = feature_extraction.fit_transform(X_train)
X_test_features = feature_extraction.transform(X_test)

X_train_features = feature_extraction.fit_transform(X_train) X_test_features = feature_extraction.transform(X_test)

In [16]:

Y_train = Y_train.astype('int')
Y_test = Y_test.astype('int')

Y_train = Y_train.astype('int') Y_test = Y_test.astype('int')

Logistic Regression¶

In [17]:

lr = LogisticRegression()
lr.fit(X_train_features, Y_train)


lr_train = lr.predict(X_train_features)
lr_test = lr.predict(X_test_features)


lr_train_acc = accuracy_score(Y_train, lr_train)
lr_test_acc = accuracy_score(Y_test, lr_test)


lr_precision = precision_score(Y_test, lr_test)
lr_recall = recall_score(Y_test, lr_test)
lr_f1 = f1_score(Y_test, lr_test)


print("Logistic Regression:\n")
print("Training Data Accuracy:", lr_train_acc)
print("Testing Data Accuracy :", lr_test_acc)

print("Precision             :", lr_precision)
print("Recall                :", lr_recall)
print("F1 Score              :", lr_f1)

lr = LogisticRegression() lr.fit(X_train_features, Y_train) lr_train = lr.predict(X_train_features) lr_test = lr.predict(X_test_features) lr_train_acc = accuracy_score(Y_train, lr_train) lr_test_acc = accuracy_score(Y_test, lr_test) lr_precision = precision_score(Y_test, lr_test) lr_recall = recall_score(Y_test, lr_test) lr_f1 = f1_score(Y_test, lr_test) print("Logistic Regression:\n") print("Training Data Accuracy:", lr_train_acc) print("Testing Data Accuracy :", lr_test_acc) print("Precision :", lr_precision) print("Recall :", lr_recall) print("F1 Score :", lr_f1)

Logistic Regression:

Training Data Accuracy: 0.9661207089970832
Testing Data Accuracy : 0.9623318385650225
Precision             : 0.959
Recall                : 0.9989583333333333
F1 Score              : 0.9785714285714285

Decision Trees¶

In [18]:

dtrees = DecisionTreeClassifier()
dtrees.fit(X_train_features, Y_train)


dt_train = dtrees.predict(X_train_features)
dt_test = dtrees.predict(X_test_features)


dt_train_acc = accuracy_score(Y_train, dt_train)
dt_test_acc = accuracy_score(Y_test, dt_test)


dt_precision = precision_score(Y_test, dt_test)
dt_recall = recall_score(Y_test, dt_test)
dt_f1 = f1_score(Y_test, dt_test)


print("Decision Tress:\n")
print("Training Data Accuracy:", dt_train_acc)
print("Testing Data Accuracy :", dt_test_acc)

print("Precision             :", dt_precision)
print("Recall                :", dt_recall)
print("F1 Score              :", dt_f1)

dtrees = DecisionTreeClassifier() dtrees.fit(X_train_features, Y_train) dt_train = dtrees.predict(X_train_features) dt_test = dtrees.predict(X_test_features) dt_train_acc = accuracy_score(Y_train, dt_train) dt_test_acc = accuracy_score(Y_test, dt_test) dt_precision = precision_score(Y_test, dt_test) dt_recall = recall_score(Y_test, dt_test) dt_f1 = f1_score(Y_test, dt_test) print("Decision Tress:\n") print("Training Data Accuracy:", dt_train_acc) print("Testing Data Accuracy :", dt_test_acc) print("Precision :", dt_precision) print("Recall :", dt_recall) print("F1 Score :", dt_f1)

Decision Tress:

Training Data Accuracy: 1.0
Testing Data Accuracy : 0.9659192825112107
Precision             : 0.9723360655737705
Recall                : 0.9885416666666667
F1 Score              : 0.9803719008264463

KNN¶

In [19]:

knn = KNeighborsClassifier()
knn.fit(X_train_features, Y_train)


knn_train = knn.predict(X_train_features)
knn_test = knn.predict(X_test_features)


knn_train_acc = accuracy_score(Y_train, knn_train)
knn_test_acc = accuracy_score(Y_test, knn_test)


knn_precision = precision_score(Y_test, knn_test)
knn_recall = recall_score(Y_test, knn_test)
knn_f1 = f1_score(Y_test, knn_test)


print("K Nearest Neighbors:\n")
print("Training Data Accuracy:", knn_train_acc)
print("Testing Data Accuracy :", knn_test_acc)

print("Precision             :", knn_precision)
print("Recall                :", knn_recall)
print("F1 Score              :", knn_f1)

knn = KNeighborsClassifier() knn.fit(X_train_features, Y_train) knn_train = knn.predict(X_train_features) knn_test = knn.predict(X_test_features) knn_train_acc = accuracy_score(Y_train, knn_train) knn_test_acc = accuracy_score(Y_test, knn_test) knn_precision = precision_score(Y_test, knn_test) knn_recall = recall_score(Y_test, knn_test) knn_f1 = f1_score(Y_test, knn_test) print("K Nearest Neighbors:\n") print("Training Data Accuracy:", knn_train_acc) print("Testing Data Accuracy :", knn_test_acc) print("Precision :", knn_precision) print("Recall :", knn_recall) print("F1 Score :", knn_f1)

K Nearest Neighbors:

Training Data Accuracy: 0.9199012788871438
Testing Data Accuracy : 0.905829596412556
Precision             : 0.9014084507042254
Recall                : 1.0
F1 Score              : 0.9481481481481482

Random Forest¶

In [20]:

rf = RandomForestClassifier()
rf.fit(X_train_features, Y_train)


rf_train = rf.predict(X_train_features)
rf_test = rf.predict(X_test_features)


rf_train_acc = accuracy_score(Y_train, rf_train)
rf_test_acc = accuracy_score(Y_test, rf_test)


rf_precision = precision_score(Y_test, rf_test)
rf_recall = recall_score(Y_test, rf_test)
rf_f1 = f1_score(Y_test, rf_test)


print("Random Forest:\n")
print("Training Data Accuracy:", rf_train_acc)
print("Testing Data Accuracy :", rf_test_acc)

print("Precision             :", rf_precision)
print("Recall                :", rf_recall)
print("F1 Score              :", rf_f1)

rf = RandomForestClassifier() rf.fit(X_train_features, Y_train) rf_train = rf.predict(X_train_features) rf_test = rf.predict(X_test_features) rf_train_acc = accuracy_score(Y_train, rf_train) rf_test_acc = accuracy_score(Y_test, rf_test) rf_precision = precision_score(Y_test, rf_test) rf_recall = recall_score(Y_test, rf_test) rf_f1 = f1_score(Y_test, rf_test) print("Random Forest:\n") print("Training Data Accuracy:", rf_train_acc) print("Testing Data Accuracy :", rf_test_acc) print("Precision :", rf_precision) print("Recall :", rf_recall) print("F1 Score :", rf_f1)

Random Forest:

Training Data Accuracy: 1.0
Testing Data Accuracy : 0.979372197309417
Precision             : 0.9775739041794088
Recall                : 0.9989583333333333
F1 Score              : 0.9881504379185987

Stacking Model¶

In [21]:

estimators = [ ('lr', lr), ('dtree', dtrees), ('knn', knn), ('rf', rf) ]
stack = StackingClassifier(estimators, final_estimator = SVC(kernel='linear'))
stack.fit(X_train_features, Y_train)


stack_train = stack.predict(X_train_features)
stack_test = stack.predict(X_test_features)


stack_train_acc = accuracy_score(Y_train, stack_train)
stack_test_acc = accuracy_score(Y_test, stack_test)


stack_precision = precision_score(Y_test, stack_test)
stack_recall = recall_score(Y_test, stack_test)
stack_f1 = f1_score(Y_test, stack_test)


print("Stacking Classifier:\n")
print("Training Data Accuracy:", stack_train_acc)
print("Testing Data Accuracy :", stack_test_acc)

print("Precision             :", stack_precision)
print("Recall                :", stack_recall)
print("F1 Score              :", stack_f1)

estimators = [ ('lr', lr), ('dtree', dtrees), ('knn', knn), ('rf', rf) ] stack = StackingClassifier(estimators, final_estimator = SVC(kernel='linear')) stack.fit(X_train_features, Y_train) stack_train = stack.predict(X_train_features) stack_test = stack.predict(X_test_features) stack_train_acc = accuracy_score(Y_train, stack_train) stack_test_acc = accuracy_score(Y_test, stack_test) stack_precision = precision_score(Y_test, stack_test) stack_recall = recall_score(Y_test, stack_test) stack_f1 = f1_score(Y_test, stack_test) print("Stacking Classifier:\n") print("Training Data Accuracy:", stack_train_acc) print("Testing Data Accuracy :", stack_test_acc) print("Precision :", stack_precision) print("Recall :", stack_recall) print("F1 Score :", stack_f1)

Stacking Classifier:

Training Data Accuracy: 0.9995512676688355
Testing Data Accuracy : 0.9856502242152466
Precision             : 0.987603305785124
Recall                : 0.9958333333333333
F1 Score              : 0.991701244813278

Metrics Visualization¶

In [22]:

train_acc_list = {"LR":lr_train_acc,
                  "DT":dt_train_acc,
                  "KNN":knn_train_acc,
                  "RF":rf_train_acc,
                  "STACK":stack_train_acc}

test_acc_list = {"LR":lr_test_acc,
                  "DT":dt_test_acc,
                  "KNN":knn_test_acc,
                  "RF":rf_test_acc,
                  "STACK":stack_test_acc}

precision_list = {"LR":lr_precision,
                  "DT":dt_precision,
                  "KNN":knn_precision,
                  "RF":rf_precision,
                  "STACK":stack_precision}

recall_list = {"LR":lr_recall,
               "DT":dt_recall,
               "KNN":knn_recall,
               "RF":rf_recall,
               "STACK":stack_recall}

f1_list = {"LR":lr_f1,
               "DT":dt_f1,
               "KNN":knn_f1,
               "RF":rf_f1,
               "STACK":stack_f1}

train_acc_list = {"LR":lr_train_acc, "DT":dt_train_acc, "KNN":knn_train_acc, "RF":rf_train_acc, "STACK":stack_train_acc} test_acc_list = {"LR":lr_test_acc, "DT":dt_test_acc, "KNN":knn_test_acc, "RF":rf_test_acc, "STACK":stack_test_acc} precision_list = {"LR":lr_precision, "DT":dt_precision, "KNN":knn_precision, "RF":rf_precision, "STACK":stack_precision} recall_list = {"LR":lr_recall, "DT":dt_recall, "KNN":knn_recall, "RF":rf_recall, "STACK":stack_recall} f1_list = {"LR":lr_f1, "DT":dt_f1, "KNN":knn_f1, "RF":rf_f1, "STACK":stack_f1}

In [23]:

a1 =  pd.DataFrame.from_dict(train_acc_list, orient = 'index', columns = ["Traning Accuracy"])
a2 =  pd.DataFrame.from_dict(test_acc_list, orient = 'index', columns = ["Testing Accuracy"])
a3 =  pd.DataFrame.from_dict(precision_list, orient = 'index', columns = ["Precision Score"])
a4 =  pd.DataFrame.from_dict(recall_list, orient = 'index', columns = ["Recall Score"])
a5 =  pd.DataFrame.from_dict(f1_list, orient = 'index', columns = ["F1 Score"])

org = pd.concat([a1, a2, a3, a4, a5], axis = 1)
org

a1 = pd.DataFrame.from_dict(train_acc_list, orient = 'index', columns = ["Traning Accuracy"]) a2 = pd.DataFrame.from_dict(test_acc_list, orient = 'index', columns = ["Testing Accuracy"]) a3 = pd.DataFrame.from_dict(precision_list, orient = 'index', columns = ["Precision Score"]) a4 = pd.DataFrame.from_dict(recall_list, orient = 'index', columns = ["Recall Score"]) a5 = pd.DataFrame.from_dict(f1_list, orient = 'index', columns = ["F1 Score"]) org = pd.concat([a1, a2, a3, a4, a5], axis = 1) org

Out[23]:

	Traning Accuracy	Testing Accuracy	Precision Score	Recall Score	F1 Score
LR	0.966121	0.962332	0.959000	0.998958	0.978571
DT	1.000000	0.965919	0.972336	0.988542	0.980372
KNN	0.919901	0.905830	0.901408	1.000000	0.948148
RF	1.000000	0.979372	0.977574	0.998958	0.988150
STACK	0.999551	0.985650	0.987603	0.995833	0.991701

In [24]:

alg = ['LR','DT','KNN','RF','STACK']
plt.plot(alg,a1)
plt.plot(alg,a2)
plt.plot(alg,a3)
plt.plot(alg,a4)
plt.plot(alg,a5)
legend = ['Traning Accuracy', 'Testing Accuracy', 'Precision Score', 'Recall Score', 'F1 Score']
plt.title("METRICS COMPARISION")
plt.legend(legend)
plt.show()

alg = ['LR','DT','KNN','RF','STACK'] plt.plot(alg,a1) plt.plot(alg,a2) plt.plot(alg,a3) plt.plot(alg,a4) plt.plot(alg,a5) legend = ['Traning Accuracy', 'Testing Accuracy', 'Precision Score', 'Recall Score', 'F1 Score'] plt.title("METRICS COMPARISION") plt.legend(legend) plt.show()

Test & Save¶

In [25]:

input_mail = ["Hi this is  kalyan"]

input_mail_features = feature_extraction.transform(input_mail)

prediction = stack.predict(input_mail_features)

if(prediction == 0):
    print("SPAM MAIL")
else:
    print("HAM MAIL")

input_mail = ["Hi this is kalyan"] input_mail_features = feature_extraction.transform(input_mail) prediction = stack.predict(input_mail_features) if(prediction == 0): print("SPAM MAIL") else: print("HAM MAIL")

HAM MAIL

In [26]:

import pickle

import pickle

In [27]:

pickle.dump(stack, open("stack_model.pkl", "wb"))

pickle.dump(stack, open("stack_model.pkl", "wb"))

In [28]:

pickle.dump(feature_extraction, open("features.pkl", "wb"))

pickle.dump(feature_extraction, open("features.pkl", "wb"))