Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/sagartr/fake-email-detector-using-machine-learning-

Developed a spam detection model using logistic regression, achieving high accuracy by leveraging TF-IDF vectorization and label encoding techniques.
https://github.com/sagartr/fake-email-detector-using-machine-learning-

csv email-phishing ipynb-jupyter-notebook python

Last synced: 6 days ago
JSON representation

Developed a spam detection model using logistic regression, achieving high accuracy by leveraging TF-IDF vectorization and label encoding techniques.

Awesome Lists containing this project

README 

        
# FAKE-EMAIL-DETECTOR-USING-MACHINE-LEARNING-

Developed a spam detection model using logistic regression, achieving high accuracy by  leveraging TF-IDF vectorization and label encoding techniques.



Phishing detection is critical in cyber security to identify and mitigate fraudulent activities. This practical implementation demonstrates the use of Logistic Regression, a supervised learning algorithm, to classify emails as spam or ham (legitimate).



Key Components of Practical Implementation:



1. Loading the Dataset: The dataset is loaded using `pandas`. We assume the dataset `mail_data.csv` contains email messages categorized as 'spam' or 'ham'.



   import pandas as pd

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

   print(df.head())



2. Handling Missing Values: Replace any missing values in the dataset with empty strings.



   data = df.where((pd.notnull(df)), '')



3. Data Inspection: Inspect the dataset to understand its structure and size.



   print(data.info())

   print(data.shape)

   

4. Label Encoding: Convert the 'Category' column to numerical values where 'spam' is 0 and 'ham' is 1.



   data.loc[data['Category'] == 'spam', 'category'] = 0

   data.loc[data['Category'] == 'ham', 'category'] = 1



5. Extracting Features and Labels: Separate the message content and the labels.



   X = data['Message']

   Y = data['Category']



6. Splitting the Dataset: Split the dataset into training and testing sets (80% training, 20% testing).



   from sklearn.model_selection import train_test_split

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



7. TF-IDF Vectorization: Transform the text data into numerical features using TfidfVectorizer.



   from sklearn.feature_extraction.text import TfidfVectorizer

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

   X_train_features = feature_extraction.fit_transform(X_train)

   X_test_features = feature_extraction.transform(X_test)



8. Label Encoding: Encode the target labels as numerical values.



   from sklearn.preprocessing import LabelEncoder

   label_encoder = LabelEncoder()

   Y_train_encoded = label_encoder.fit_transform(Y_train)

   Y_test_encoded = label_encoder.transform(Y_test)



9. Training the Model: Train the Logistic Regression model on the training data.



   from sklearn.linear_model import LogisticRegression

   model = LogisticRegression()

   model.fit(X_train_features, Y_train_encoded)



10. Accuracy on Training Data: Evaluate the model's accuracy on the training data.



    from sklearn.metrics import accuracy_score

    prediction_on_training_data = model.predict(X_train_features)

    accuracy_on_training_data = accuracy_score(Y_train_encoded, prediction_on_training_data)

    print('Accuracy on training data:', accuracy_on_training_data)



11. Accuracy on Testing Data: Evaluate the model's accuracy on the testing data.



    prediction_on_test_data = model.predict(X_test_features)

    accuracy_on_test_data = accuracy_score(Y_test_encoded, prediction_on_test_data)

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



12. Predicting New Data: Predict the category of a new email message.



    input_your_mail = ["Your input email message here"]

    input_data_features = feature_extraction.transform(input_your_mail)

    prediction = model.predict(input_data_features)

    print(prediction)