EMAIL SPAM DETECTION: ENHANCING

SECURITY AND USER EXPERIENCE

An Application Development Report Submitted
In partial fulfillment of the requirements for the award of the degree of

Bachelor of Technology
in
Information Technology

by
A. Tarun 22N31A1207
D. Vinay raj 22N31A1254
Abishek Dubey 22N31A1203

Department of Information Technology

Malla Reddy College of Engineering & Technology
(Autonomous Institution- UGC, Govt. of India)
(Affiliated to JNTUH, Hyderabad, Approved by AICTE, NBA &NAAC with ‘A’Grade)
Maisammaguda, Kompally, Dhulapally, Secunderabad – 500100
website: www.mrcet.ac.in
 Malla Reddy College of Engineering & Technology
(Autonomous Institution- UGC, Govt. of India)
(Affiliated to JNTUH, Hyderabad, Approved by AICTE, NBA &NAAC with ‘A’ Grade)
Maisammaguda, Kompally, Dhulapally,Secunderabad – 500100
website: www.mrcet.ac.in

CERTIFICATE

This is to certify that this is the bonafide record of the project entitled “EMAIL SPAM DETECTION:

ENHANCING SECURITY AND USER EXPERIENCE” by A.TARUN(22N31A1207),

D.VINAYRAJ(22N31A1254) and ABISHEK(22N31A1203) of B.Tech in the partial fulfillment of the

requirements for the degree of Bachelor of Technology in Information Technology,

Department of IT during the year 2024-2025.

Internal Guide Head of the Department

Ms. K. Swetha Dr. G. Sharada
Associate professor Professor

External Examiner
 ABSTRACT

This project aims to enhance email spam detection accuracy using Python, focusing on
comparing traditional machine learning algorithms to identify the most effective spam
filtering technique. We integrate the model into a web application using Flask, allowing easy
access for users to filter out spam emails quickly and accurately. By reducing exposure to
phishing, scams, and malware, this system helps users avoid potential financial and data
losses. Future improvements may include advanced NLP techniques for greater accuracy,
making this tool even more robust and secure.
This project aims to enhance email spam detection accuracy using Python, focusing on
comparing traditional machine learning algorithms to identify the most effective spam
filtering technique. We integrate the model into a web application using Flask, allowing easy
access for users to filter out spam emails quickly and accurately. By reducing exposure to
phishing, scams, and malware, this system helps users avoid potential financial and data
losses. Future improvements may include advanced NLP techniques for greater accuracy,
making this tool even more robust and secure.
 TABLE OF CONTENTS

S.NO TITLE PG.NO

ABSRACT
1 INTRODUCTION 01
1.1 PURPOSE AND OBJECTIVES 01
1.2 EXISTING AND PROPOSED SYSTEM 03

2 APPLICATION DESCRIPTION
2.1 HARDWARE & SOFTWARE REQUIREMENTS 4
2.2 METHODOLOGY 5

3 SYSTEM DESIGN
3.1 ARCHITECTURE AND DIAGRAMS 6

4 IMPLEMENTATION 10
4.1 SOURCE CODE AND OUTPUT SCREENS 11

5 CONCLUSION 25

BIBLIOGRAPHY 30
 1. INTRODUCTION

Email Spam Detection project, which leverages machine learning to accurately distinguish
between spam and ham (non-spam) emails. The core objective of our project is to enhance
email security by identifying unwanted or harmful emails, thereby protecting users from
spam. By using advanced machine learning algorithms, we can effectively classify emails
based on their content, patterns, and characteristics. Our solution aims to minimize false
positives while maintaining high accuracy in detecting spam.
In addition to the powerful backend system, our project includes a user-friendly website
interface designed to provide a smooth and intuitive user experience. We focused on
creating a high-quality UI that allows users to easily navigate, review email
classifications, and customize spam filters according to their preferences. We believe that
this project will contribute to improving email security and user satisfaction.

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1.1 PURPOSE AND OBJECTIVES

PURPOSE:
Our Email Spam Detection project focuses on the automatic classification of incoming emails
into two categories: spam and ham. Spam refers to unsolicited, irrelevant, or malicious
messages, while ham refers to legitimate, useful messages. This project uses machine
learning algorithms to analyze email content and metadata to classify them accordingly.The
purpose of email spam detection is to identify and filter out unwanted or harmful emails, such
as advertisements, scams, or phishing attempts, to protect users and keep their inboxes
relevant and secure.

Objectives:

The main objective of email spam detection is to accurately identify and prevent spam
messages from reaching users' inboxes, reducing the risk of exposure to scams, malware, and
phishing attacks, while ensuring legitimate emails are delivered reliably. So, in order to
ensure the email is spam or ham the web based platform “EMAIL SPAM DETECTION” is
used. In these web based platform we used a huge number of mails in order to train the
machine learning model to predict the accurate results.

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 1.2 EXISTING AND PROPOSED SYSTEM

EXISTING SYSTEM

Emails are filtered using predefined rules based on keywords, sender reputation, and
blacklists. Naïve Bayes classifies emails as spam or not based on word frequency
probabilities. Emails are flagged as spam if they match common behavioral patterns or
known spam signatures. Algorithms are likely used classify emails by learning from labeled
datasets. Emails from known spammers are automatically filtered using external spam
databases and reputation systems.

PROPOSED SYSTEM

In the existing systems it covers many crucial aspects, that are certainly included in the
project. While, there are several additional factors and enhancements that should be
considered for more advanced and comprehensive spam detection project. A web-based
platform is developed where users can upload the messages and E mails and verify whether
they are spam or ham. In the training phase we are also going to include the suspicious URLs.
If the URL is reported by the multiple users it is considered as the spam message.
Continuosly making the updates on spam filters based on the evolving user behaviour and
preference.Developing the methods to detect spam without affecting and
compromising user's privacy

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 2.APPLICATION DESCRIPTION

2.1 SOFTWARE AND HARDWARE REQUIREMENTS

SOFTWARE REQUIREMENT SPECIFICATION

Operating System : Linux (Ubuntu/CentOS), Windows (optional)

Programming Language : Python 3.7 or Higher

Data Processing Libraries : Pandas, NumPy

Development Environment : VS Code

Technology used : Machine learning

HARDWARE REQUIREMENT SPECIFICATION

CPU : Quad-core processer

RAM : 4 GB (basic)

Storage : ROM of basic 64GB

Internet : High-Speed Internet Connection

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 3. SYSTEM DESIGN

3.1 ARCHITECTURE DIAGRAM

5
 4. IMPLEMENTATION

4.1 SOURCE CODE AND OUTPUT SCREENS

4.1.1 SOURCE CODE

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load the dataset

df = pd.read_csv('mail_data.csv')
data = df.where(pd.notnull(df), '')

# Label encoding: 0 for spam, 1 for ham

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

X = data['Message']
Y = data['Category'].astype(int)

# Split the dataset

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

# Feature extraction using TfidfVectorizer

vectorizer = TfidfVectorizer(min_df=1, stop_words='english', lowercase=True)
X_train_features = vectorizer.fit_transform(X_train)
X_test_features = vectorizer.transform(X_test)

# Logistic Regression model

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

# Predictions and accuracy on training data

prediction_on_training_data = model.predict(X_train_features)
accuracy_on_training_data = accuracy_score(Y_train, prediction_on_training_data)
print('Accuracy on training data:', accuracy_on_training_data)

# Predictions and accuracy on test data

prediction_on_test_data = model.predict(X_test_features)
accuracy_on_test_data = accuracy_score(Y_test, prediction_on_test_data)

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print('Accuracy on test data:', accuracy_on_test_data)

# Predict on a new email

input_email = ["I hope you're all doing well. Please find attached the agenda for our meeting
next Monday at 10 AM."]
input_features = vectorizer.transform(input_email)
prediction = model.predict(input_features)

# Output result
if prediction[0] == 1:
print("Ham mail")
else:
print("Spam mail")

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4.2 OUTPUT SCREENS

Fig.4.2.1 Legitimate url

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 5.CONCLUSION

In conclusion, email spam detection plays a crucial role in enhancing user security,
preserving inbox quality, and improving productivity by minimizing interruptions from
unsolicited messages. Effective spam detection systems protect users from potential threats,
such as phishing and malware, while maintaining the seamless flow of legitimate
communication. Continuous advancements in machine learning and AI help these systems
adapt to evolving spam tactics, ensuring reliable and accurate filtering over time. In the web
based platform where the Emails are detected we further train our machine learning model
such that the model can also able to detect not only Emails but also the suspicious URLs, a
simple text messages etc.
Email spam detection is essential for maintaining secure, organized, and efficient
communication in both personal and professional settings. By effectively filtering out
unwanted or malicious emails, these systems prevent exposure to risks like phishing, fraud,
and data breaches, helping protect sensitive information and maintain user trust. As spam
tactics grow more sophisticated, ongoing advancements in AI and machine learning become
vital for refining detection algorithms, ensuring they can adapt and accurately distinguish
between spam and legitimate emails.

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 6.BIBLIOGRAPHY
For successfully completing my project “EMAIL SPAM DETCTION” I have refered to the
following references and websites:

 www.kaggle.com
 www.flask.com
 www.youtube.com

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