2. What is Spam?
Spam is unsolicited and unwanted email. It often
contains malicious content or scams.Spam can
be difficult to detect because it can come in
many forms and use various tactics to avoid
detection. However, with the use of deep
learning and CNNs,we can build more effective
spam detection systems.
3. What are Convolutional Neural Networks?
Convolutional neural networks
(CNNs) are a type of deep learning
model that is particularly effective for
image recognition tasks.CNNs use
convolutional layers to extract
features from images,and then use
fully connected layers to make
predictions based on those features.In
spam detection,we can use CNNs to
extract features from email messages.
4. Preparing the Data
Before we can train our spam detection system,
we need to prepare our data.This involves
cleaning and preprocessing the data, and
splitting it into training and testing sets. We will
also need to vectorize our text data so that it can
be used as input to our CNN.
import tensorflow as tf
from tensorflow.keras.datasets
import imdb
from
tensorflow.keras.preprocessing.seque
nce import pad_sequences
import numpy as np
import pandas as pd
5. Training the Model
Once our data is prepared,we can
begin training our CNN model.This
involves defining the architecture
of the model,compiling it with
appropriate loss and optimization
functions,and then fitting it to our
training data.We will also need to
tune our model's hyperparameters
to achieve the best performance.
7. Evaluating theM odel
After our model is trained,we need to
evaluate its performance on our testing
data.We will use accuracy and
precision metrics to measure how well
our model is able to detect spam.We
can also use confusion matrices and
ROC curves to gain a deeper
understanding of our model's
performance.
8. import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
data=pd.read_csv('/content/spam.csv')
data
data.columns
data.info()
data.isna().sum()
9. data['Spam']=data['Category'].apply(lambda x:1 if x=='spam' else 0)
data.head(5)
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(data.Message,data.Spa
m,test_size=0.25)
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(data.Message,data.Spa
m,test_size=0.25)
from sklearn.naive_bayes import MultinomialNB
10. from sklearn.pipeline import Pipeline
clf=Pipeline([
('vectorizer',CountVectorizer()),
('nb',MultinomialNB())
])
clf.fit(X_train,y_train)
11. emails=[
'Sounds great! Are you home now?',
'Will u meet ur dream partner soon? Is ur career
off 2 a flying start? 2 find out free, txt HORO
followed by ur star sign, e. g. HORO ARIES'
]
clf.predict(emails)
clf.score(X_test,y_test)
12. Conclusion
In conclusion,deep learning and CNNs are powerful tools for
building effective spam detection systems. By using these
technologies, we can improve the accuracy and precision of our
spam detection,and better protect ourselves from malicious
content and scams.With the knowledge gained from this
presentation,you are now equipped to build your own spam
detection system using deep learning and CNNs in Python.
13. Thanks!
Do you have any questions? addyouremail@freepik.com
+
91620 421838
yourcompany.com
