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I will build python web apps for machine learning text classification using streamlit
Applied AI Engineer
I'm an Applied AI Engineer focused on practical AI applications and machine learning solutions.
Using Python, scikit-learn, and Streamlit, I build predictive models and interactive ML applications. M...
Informazioni su questo servizio
Need a web app to classify text?
I build text-classification apps using Python, Streamlit, and scikit-learn, with sentiment analysis as a core use case.
What the app can do
- Classify individual text and return a predicted label.
- Process CSV files by selecting the text column (Standard/Premium).
- Provide downloadable predictions and summary charts (Premium).
- Predict predefined labels rather than generate open-ended answers.
Example project: A social-media sentiment app that classifies text as Positive, Neutral, or Negative.
Package options
Basic uses an existing trained sentiment model for individual text inputs only; it is not retrained on your data. Standard trains one text classifier from a labeled dataset and supports individual and CSV batch predictions. Premium adds comparison of up to 2 models, tuning, and a results dashboard.
Before ordering
Share your goal, sample text, expected labels, and a labeled dataset for custom training. I'll confirm the scope before starting.
Please note: Results depend on data quality and text domain; specific accuracy is not guaranteed. Hosting, authentication, databases, payment systems, and complex integrations are excluded. LLM-based clas
Expertise:
Classificazione
•
Analisi sentimentale
Linguaggio di programmazione:
Python
Framework:
Scikit-learn
•
Altro
Strumenti:
Quaderno jupyter
•
Colab
Il mio portfolio
Altri servizi della categoria Data science e ML offerti da me
FAQ
What do you need from me to start?
Please share your project goal, sample text, expected labels, and any relevant dataset. Custom model training requires a suitable labeled dataset. I'll confirm the scope before starting.
What types of text can your app classify?
The app can support sentiment analysis and suitable text-classification tasks, such as assigning predefined categories to text. Feasibility depends on your requirements, data, and the selected package.
What is included in the Basic package?
Basic uses a suitable existing sentiment model for individual text inputs. It includes the Streamlit interface, sentiment predictions, source code, and setup instructions. Custom model training and CSV batch analysis are excluded.
Can you train a model using my own dataset?
Yes. Standard and Premium include custom text-classifier training using a suitable labeled dataset. The dataset must contain text examples and their correct labels.
Can I upload a CSV and analyze multiple text records?
Yes. Standard and Premium support CSV uploads, text-column selection, batch predictions, result previews, and downloadable predictions. File size and dataset suitability must remain within the agreed scope.
What is the difference between Standard and Premium?
Standard includes one trained text classifier and CSV batch analysis. Premium adds comparison of up to two models, tuning of the selected model, and a dashboard with charts and summary statistics.
Can you guarantee a specific accuracy?
No. Results depend on data quality, label consistency, text domain, and model suitability. I'll evaluate the model using appropriate metrics when model training is included.
Will you host the application online?
Hosting and cloud deployment are not included in the standard packages. The deliverable is a Streamlit application with source code and instructions for running it locally.
Do you build LLM chatbots or add authentication and databases?
These features are outside the standard scope. LLM-based classification may be discussed separately, but feasibility, provider requirements, and potential API costs must be confirmed before accepting the project.
