Spam Message Detection (Using NLP)
Introduction
What is Spam Message ?
Spam is any kind of unwanted, unsolicited digital communication that gets sent out in bulk. Often spam is sent via email, but it can also be distributed via text messages, phone calls, or social media. Spam Detection A machine-learning model with a set of examples of spam and ham messages and let it find the relevant patterns that separate the two different categories.
A general approach to detecting a spam message is using supervised learning. Supervised Learning is an algorithm trained on input data that has been labeled for a particular output. The algorithm learns by comparing its actual output with the correct output to find errors. Text classification refers to labeling sentences or documents, such as email spam classification and sentiment analysis.
Approach
Data acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer.
Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. A data set (or dataset) is a collection of data.
Training a model simply means learning (determining) good values for all the weights and the bias from labeled examples. In supervised learning, a machine learning algorithm builds a model by examining many examples and attempting to find a model that minimizes loss.
Splitting up into Training and Test sets are common best practices. This allows to tune various parameters of the algorithm without making judgements that specifically conform to training data.
Some Model Training Algorithms
Linear SVC method applies a linear kernel function to perform classification and it performs well with a large number of samples.
Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable.
Model testing is referred to as the process where the performance of a fully trained model is evaluated on a testing set. A metric is a quantitative measurement of data, in relation to what you are actually measuring. Your data point may be just a number. Common Metrics are accuracy, recall, precision,fl-score.
Application
The application of a model for prediction using a new data refer to as Deployment.
- Messaging Platforms
- Mailing Service
Implementation
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