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I will build a document clustering system with PDF text extraction
Build Intelligent AI Web Apps and NLP Solutions for Data
I’m a Data Scientist with strong Machine learning and NLP background. I build intelligent tools like deploying ML models, PDF and CSV analyzers and document clustering systems that turn messy data int...
About this Gig
Title: Automated Document Organization & NLP Analysis
Hi! If youre overwhelmed by a massive pile of PDF documents, I can help you organize them using AI-powered NLP.
I don't just group files by basic keywords. I use advanced semantic embeddings to understand the actual meaning of your text, ensuring your documents are categorized logically and accurately.
What I provide:
- Smart PDF Extraction: Ill handle the messy work of pulling and cleaning text from your PDF files.
- AI Clustering: Using K-Means and Sentence Transformers, Ill group your documents based on their actual topics.
- Optimal K-Selection: I use Silhouette Scores to scientifically find the best number of categories for your data.
- Interactive Visuals: Youll receive clear Plotly charts to see how your documents relate to one another.
- Keyword Insights: Ill extract the most representative terms for each group so you know exactly whats inside.
- Custom App (Premium): A full Streamlit dashboard for easy, real-time document analysis.
I focus on accuracy and clean code. Message me today to discuss your project!
Programming language:
Python
Frameworks:
Scikit-learn
•
Panda
Tools:
Jupyter Notebook
•
Colab
My Portfolio
Other Data Science & ML Services I Offer
FAQ
What kind of PDF documents can you process?
I can process almost any text-based PDF, including research papers, business reports, and articles.
Can you process Microsoft Word (.docx) files also?
Yes, absolutely! While the standard version of my tool is optimized for PDFs, I can easily modify the data ingestion pipeline to handle .docx and .doc files.
How do you ensure the clusters are accurate?
I use a "Silhouette Score" analysis to mathematically determine the most logical number of groups for your data. This ensures the clusters aren't just random but are based on actual semantic density.
Do I need to provide the "Topics" beforehand?
No! This is "Unsupervised Learning," meaning the AI identifies the patterns and groups the documents itself.
Is my data secure?
Absolutely. I process your data locally on my secure development environment. Once the project is delivered and accepted, I delete your documents from my system unless you request otherwise.
Can I run the Streamlit dashboard on my own computer?
Yes. If you choose the Premium package, I provide a requirements.txt file and a .devcontainer configuration, making it easy to run the app locally in VS Code or deploy it to the cloud.
