DATA SCIENCE PROJECT
“SPAM MAIL DETECTION”
PREPARED AND PRESENTED BY
SANJAI PRIYAN, XII-B
(CERTIFICATE PAGE)
(declaration page)
(acknowledgement page)
Introduction:-
This research project aims to develop a
robust machine learning model capable of accurately
detecting spam mails, significantly reducing unwanted
disturbances and safeguarding user privacy. By
leveraging advanced techniques in natural language
processing (NLP) and machine learning, this study will
analyze a comprehensive dataset of mail logs and
associated metadata to extract relevant features and
train a highly effective classification model.
Software Requirements :-
Python with Jupyter Notebook.
Or
Google Colab notebook with inbuilt
Python and Jupyter notebook (used
in this project).
Microsoft Excel to view or Edit the
training/test data.
ML concepts used in this project and
their definitions:-
Here are some key machine learning concepts that could
be used in a spam call detection project:
1. Supervised Learning:
Definition: A machine learning paradigm where the
model is trained on labeled data, meaning the
correct output (spam or not spam) is provided for
each input (call data).
Relevance: In spam mail detection, supervised
learning algorithms can be used to learn patterns
from historical data and make accurate predictions
on new, unseen mails.
2. Classification:
Definition: A machine learning task that involves
assigning a class label to a given data point.
Relevance: In spam call detection, classification
algorithms can be used to categorize incoming mails
as either spam or legitimate.
3. Feature Engineering:
Definition: The process of selecting and
transforming relevant features from raw data to
improve the performance of a machine learning
model.
Relevance: In spam mail detection, feature
engineering can involve extracting features like mail
length, content etc
4. Natural Language Processing (NLP):
Definition: A field of artificial intelligence that deals
with the interaction between computers and human
language.
Relevance: If mail transcripts are available, NLP
techniques can be used to analyze the content of the
mails and identify keywords or phrases that are
indicative of spam.
5. Model Evaluation:
Definition: The process of assessing the
performance of a machine learning model on a given
dataset.
Relevance: Model evaluation metrics like accuracy,
precision, recall, and F1-score can be used to
measure the effectiveness of the spam mail
detection model.
By effectively combining these concepts, a robust and
accurate spam mail detection system can be developed.
*Allthe data presented in this project are
collected and put in a Microsoft Excel
Document*
Program and Procedure:-
1. Open a “Google Colab” notebook
with inbuilt Python and Jupyter
Notebook.
2. Download the spam/ham
dataset from the following
“Google Drive” link
Link -
https://drive.google.com/file/d/1uzbhec5TW_OjFr4UU
ZkoMm0rpyvYdhZw/view
3. Import the downloaded
Dataset into the “Files” column
4. Start a new code line and
import dependencies namely
numpy(provides support for multi-dimensional
arrays and mathematical functions for scientific
computing), pandas(for analyzing, cleaning,
exploring, and manipulating data),
train_test_split(can split your dataset into subsets
that minimize the potential for bias in your evaluation and validation
process, TfidfVectorizer(assesses a word's
significance within a collection of documents),Logistic
Regression(aims to solve classification
problems),
Accuracy_score(computes the accuracy,
either the fraction (default) or the count (normalize=False) of
correct predictions ).
5. Import the mail dataset to the
pandas dataframe using the
read.csv command
6. Print the dataset and check for
reference.
7. Replace the missing/null
values with a null string.
8. Use the head() function to
print the first 5 rows of the
dataset for reference.
9. Check the number of rows and
columns and match it with the
original dataset file to check
for missing data.
10. The data’s are of two types in
this scenario, Spam and
Ham(not spam). The Spam
data is numbered/labeled as 0
and the Ham data is
numbered/labeled as 1.
11. Split the data into test data
and training data.
12. Transform the text data to
feature vectors that can be
used as input to the logistic
regression and also convert
y_train and y_test value as
integers.
13. Implement the logistic
regression model and train the
model with the training data’s
that were previously assigned.
14. Evaluate the training/test
model by checking the
accuracy of prediction of both.
15. Finally build the predictive
model and input the mail to
conclude if it’s spam or not.
THE OUTPUT OF THE PROGRAM
WOULD TELL US IF THE MAIL IS SPAM
OR NOT.
Result Interpretation:-
Interpreting the Results
1.High Accuracy:
oPositive: Indicates that your model is
generally accurate in classifying emails.
o Potential Pitfalls: A high accuracy might
mask issues in specific categories, such as
false positives or false negatives.
2.High Precision:
o Positive: Suggests that when your model
identifies an email as spam, it's likely to be
accurate.
o Potential Pitfalls: A high precision might
come at the cost of low recall, meaning the
model might miss some spam emails.
3.High Recall:
o Positive: Indicates that your model is
effective in identifying most spam emails.
o Potential Pitfalls: A high recall might
result in a higher number of false positives,
where legitimate emails are incorrectly
flagged as spam.
4.High F1-Score:
o Positive: This is a strong indicator of
overall model performance, balancing
precision and recall.
Conclusion:-
In conclusion, this project successfully
demonstrates the application of machine learning
techniques to effectively detect spam emails. By
leveraging a robust dataset and employing
advanced natural language processing techniques,
a highly accurate model was developed. The
model, trained on a diverse range of email content,
effectively distinguishes between legitimate and
spam emails.
The implementation of this spam detection system
has the potential to significantly enhance email
security and user experience. By filtering out
unwanted and potentially harmful messages, it can
help individuals and organizations save time,
reduce clutter, and protect sensitive information.
As technology continues to evolve and spam
tactics become increasingly sophisticated, further
research and development in this area are crucial.
Future work could explore the integration of deep
learning techniques, such as recurrent neural
networks or transformers, to improve model
performance and adaptability to emerging spam
trends.
BIBLIOGRAPH:-
1. SOURCE CODE :
https://www.youtube.com/@Siddhardhan
www.github.com
www.geeksforgeeks.com
2. IMAGES: All images used in this
document were screenshotted and pasted
using the snipping tool in the personal
computer
