Spam messages are unwanted and unsolicited messages usually sent in bulk for advertisement purposes. Spam messages are annoying, and they sometimes pose a threat as well. Any unrelated email could have malware that can cause harm to your property. To avoid such situations, we use filters to detect spam messages.
Spam filtering automatically identifies and separates unwanted messages/emails from legitimate content. Almost all modern-day applications, like Gmail, have a built-in spam filter. They automatically decide and put unwanted messages in a folder named "Spam," and all the important messages are move toward the main "Inbox".
Spam is usually filtered via two techniques. Rule-based filtering, which applies pre-defined criteria, and machine learning-based filtering, which uses algorithms to recognize spam patterns and learn from data. Combining these methods ensures accurate and effective spam removal, improving user experience and communication security.
We can create our spam filter using machine learning algorithms in R programming language. Spam filtering is a classification problem. Classification is a supervised machine learning method where the model tries to predict the correct label of a given input data. In the spam filter, we classify the incoming text/message either as spam or not spam. We can use multiple ML models that can work efficiently for classification problems, i.e., Single Vector Machines (SVM), Naive Bayes, or Random Forest. We will use the SVM model, often considered the most famous and efficient model in the classification paradigm.
SVM is used to classify data by defining a decision boundary that perfectly separates the two classes. In our case, spam or not spam.
To solve this problem using R, we have to take the following steps:
Data preparation: Import the dataset, then pick the appropriate features. Clean the data, remove unwanted variables or dummy values, and remove all the entries with empty values.
Data splitting: Use the caTools library to divide the dataset into training and test sets. For reproducibility, use a random seed.
Scaling features: Use the scale function scale(data[-1])
to apply feature scaling to the training and test sets. Because it helps normalize the feature values within a dataset, feature scaling is significant in machine learning. When the input features are of similar scales, many machine learning algorithms, such as gradient descent-based optimization techniques, perform better.
Model fitting: Use the library e1071
to fit an SVM model to the training set.
svm_model <- svm(formula, data, type, kernel)
Here’s what each parameter means:
formula
: This is a formula specifying the relationship between your target variable and predictor variables. It typically follows the form target ~ feature1 + feature2 + …
data: The dataframe containing your training data.type
: This specifies the type of SVM model. For classification, you’ll use “C-classification” or “nu-classification” depending on the type of classification task (C-classification for multi-class and binary classification, nu-classification for one-class classification).kernel
: This defines the type of kernel function to be used. For example, “linear” for a linear kernel, “radial” for a radial basis function (RBF) kernel, etc.Prediction and evaluation: Utilize the trained SVM mode to forecast the target variable for the test set. To evaluate the accuracy of predictions, create a confusion matrix.
Visualization: Utilize the ElemStatLearn
library to visualize the training set results. Create a grid of points, forecast SVM results, and plot the outcomes alongside the data.
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