Hack, Learn, Secure: Your Cybersecurity Playground

TAST 1 : Model Steps – Spam Classification using Naive Bayes Algorithm

• Step 1 : Import relevant libraries: a library is simply a collection of codes or modules of codes that can be used in a program for specific operations.

 
          sudo apt install -y python3-pandas
          sudo apt install -y python3-numpy

          import numpy
          import pandas
          
        

• Step 2 : Import sklearn library: Simple and efficient tools for predictive data analysis
• sklearn - https://scikit-learn.org/stable

          sudo apt install -y python3-sklearn

          from sklearn.feature_extraction.text import CountVectorizer
          from sklearn.model_selection import train_test_split
          from sklearn.naive_bayes import MultinomialNB
          
        

• Step 3 : Load the dataset: Dataset is from AIDevNepal’s GitHub repository which has a combination of spam and non-spam emails. The target of the spam will be 1 if it is not spam then it will be 0.

          data = pd.read_csv('https://raw.githubusercontent.com/AiDevNepal/ai-saturdays-workshop-8/master/data/spam.csv')
          data['target'] = np.where(data['target']=='spam', 1, 0)
          
        

• Step 4 : Divide the dataset: Split the data into training and test sets.

          X_train, X_test, Y_train, Y_test = train_test_split(data['text'], data['target'], random_state=0)
          
        

• Step 5 : Train the Model: Train a multinomial Naive Bayes model using Sklearn API with the dataset created.Actually, naive Bayes is a performance machine learning algorithm on small datasets.

          model.fit(X_train, Y_train)
          
        

• Step 6 : Predict the results: Test the trained Naive Bayes model with the test data.

          test_predictions = model.predict(X_test_vectorized)
          
        

TASK 2 : Model Steps – Spam Classification using Logistic Regression

• Step 1 : Import relevant libraries: A library is simply a collection of codes or modules of codes that can be used in a program for specific operations.

          sudo apt install -y python3-pandas
          sudo apt install -y python3-numpy

          import numpy
          import pandas
          
        

• Step 2 : Import sklearn library: Simple and efficient tools for predictive data analysis

          sudo apt install -y python3-sklearn
          from sklearn.feature_extraction.text import CountVectorizer
          from sklearn.model_selection import train_test_split
          from sklearn.linear_model import LogisticRegression
          
        

• Step 3 : Loading Data: The dataset is loaded from a CSV file using pandas. This dataset contains emails labeled as 'spam' or 'ham' (not spam).

          data = pd.read_csv('https://raw.githubusercontent.com/AiDevNepal/ai-saturdays-workshop-8/master/data/spam.csv')
          data['target'] = np.where(data['target']=='spam', 1, 0)
          
        

• Step 4 : Train and Test Data – Splitting: The dataset is split into training and test sets using train_test_split.

          X_train, X_test, Y_train, Y_test = train_test_split(data['text'], data['target'], random_state=0)
          
        

• Step 5 : Model Training:

          model = LogisticRegression(max_iter=1000)
          model.fit(X_train_vectorized, Y_train)
          
        

• Step 6 : Model Prediction and Testing: