51aa8d25-8019-4094-88d4-172269a62ac3.pdf Apr 2026

This feature aims to classify documents (like the one identified by 51aa8d25-8019-4094-88d4-172269a62ac3.pdf ) into predefined categories (e.g., Research Paper, Review Article, Conference Proceeding, etc.) and report the confidence level of the classification.

# Train a classifier X_train, X_test, y_train, y_test = train_test_split(X_train, train_labels, test_size=0.2) clf = RandomForestClassifier().fit(X_train, y_train) 51aa8d25-8019-4094-88d4-172269a62ac3.pdf

# Assume a list of labeled documents for training train_texts = [...] train_labels = [...] This feature aims to classify documents (like the

# Usage file_path = 'path/to/51aa8d25-8019-4094-88d4-172269a62ac3.pdf' category, confidence = classify_document(file_path) print(f"Category: {category}, Confidence: {confidence}") This example provides a simplistic view and might need adjustments based on the actual content and structure of your PDF documents. y_test = train_test_split(X_train

# Function to classify a new document def classify_document(file_path): pdf_file_obj = open(file_path, 'rb') pdf_reader = PyPDF2.PdfFileReader(pdf_file_obj) num_pages = pdf_reader.numPages text = '' for page in range(num_pages): page_obj = pdf_reader.getPage(page) text += page_obj.extractText() pdf_file_obj.close() # Classify doc_vector = vectorizer.transform([text]) prediction = clf.predict(doc_vector) confidence = clf.predict_proba(doc_vector).max() return prediction[0], confidence

import PyPDF2 from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier