Document classification:

Document classification, also known as text classification, is the process of categorizing

documents into predefined classes or categories based on their content. It involves using machine

learning algorithms and natural language processing techniques to automatically assign labels or

tags to documents according to their topics, themes, or characteristics.

The document classification process typically involves the following steps:

1. Data Collection: Gather a collection of documents that need to be classified. These documents

can be text files, emails, articles, social media posts, or any other form of textual data.

2. Preprocessing: Clean and preprocess the text data to remove noise, such as special characters,

punctuation, and stop words (commonly used words like "and," "the," "is" that do not carry

significant meaning).

3. Feature Extraction: Convert the preprocessed text data into numerical features that can be used by machine learning algorithms. Common techniques for feature extraction include bag-of-

words, TF-IDF (Term Frequency-Inverse Document Frequency), word embeddings, and n-

grams.

4. Model Training: Select a machine learning algorithm, such as Naive Bayes, Support Vector

Machines (SVM), Logistic Regression, or neural networks, and train it using labeled examples of

documents. During training, the algorithm learns patterns and relationships between the features

and the corresponding document categories.

5. Model Evaluation: Evaluate the performance of the trained model using a separate set of

labeled documents (a test set) to assess its accuracy, precision, recall, and F1-score.

6. Model Deployment: Once the model has been trained and evaluated, it can be deployed to

classify new, unseen documents into the predefined categories.