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Streamline Analyst

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Basic Information

Streamline Analyst is an open-source, LLM-powered data analysis agent implemented as a Streamlit application that automates end-to-end data analysis workflows. It is designed to help users of varying expertise perform tasks such as data cleaning, preprocessing, target variable identification, dataset partitioning, model selection and training, and visualization without extensive manual coding. The app offers an interactive automated workflow where users select a data file, choose an analysis mode, and start processing. It integrates LLM recommendations to choose appropriate preprocessing strategies and models, provides real-time calculation of model indicators, and produces downloadable processed data and trained models. The project can be run locally with Python and requires an OpenAI API key for advanced LLM-backed operations. The README also references a hosted Streamlit demo and emphasizes one-time use of uploaded data and API keys for privacy.

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App URL
https://github.com/Wilson-ZheLin/Streamline-Analyst
Github Repository
https://github.com/Wilson-ZheLin/Streamline-Analyst/blob/main/README.md

App Details

Features
Key features include automatic target variable identification by LLMs, multiple null value handling strategies (mean, median, mode, interpolation, new category), automated encoding suggestions (one-hot, integer mapping, label encoding), PCA-based dimensionality reduction, duplicate resolution, Box-Cox transformation and normalization, and class balancing recommendations such as random oversampling, SMOTE, and ADASYN. The tool recommends and initiates model training and supports classification, clustering and regression model families. Supported models listed include logistic regression, random forest, SVM, gradient boosting, Gaussian Naive Bayes, AdaBoost, XGBoost, K-means, DBSCAN, Gaussian mixture models, linear regression, ridge, lasso, elastic net, and gradient boosting regression. Visualization tools include single- and multi-attribute plots, 3D plotting, word clouds, world heat maps, and standard evaluation plots and metrics computed in real time.
Use Cases
Streamline Analyst accelerates exploratory data analysis and model prototyping by automating repetitive preprocessing and model selection tasks and surfacing LLM-driven recommendations tailored to the dataset. It reduces the need for deep data science expertise by offering one-click workflows that handle missing values, encoding, scaling, dimensionality reduction, balancing and train/test splitting. Real-time metrics and visualizations help users interpret model performance and clustering quality during experiments. Downloadable processed datasets and trained models enable further analysis or deployment. The application can be run locally for privacy-sensitive data and documents an estimated cost per end-to-end GPT-4 turbo request. Overall, it is useful for rapid prototyping, educational purposes, and accelerating common analytics tasks.

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