Tell us how you approached solving document automatic classification tasks.

For automatic document classification, I usually start with data analysis: studying the document structure, format, presence of metadata, and target categories. Then I perform text preprocessing — removing stop words, normalizing words (lemmatization or stemming), and converting text into numerical representations using TF-IDF or word vectorization (e.g., Word2Vec, FastText).

Next, I choose a classification model — simple tasks can be handled with logistic regression or naive Bayes classifier, while more complex tasks may require decision trees, random forests, or neural networks (e.g., LSTM or transformers).

An example using sklearn and TF-IDF:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline

# Example data
texts = ["Finance document", "Technical report", "News article"]
labels = ["finance", "technology", "news"]

# Create a pipeline for vectorization and classification
model = make_pipeline(TfidfVectorizer(), LogisticRegression())

# Train the model
model.fit(texts, labels)

# Prediction
print(model.predict(["Budget report"]))

After training, it is important to evaluate the quality using metrics (accuracy, precision, recall) and, if necessary, improve preprocessing or select other models.