How Missing Data Analysis Lab uses Flask, Bayesian optimization, and MongoDB in one regression workflow
Missing Data Analysis Lab is a Flask-based Python application that streamlines the machine learning workflow for regression tasks involving missing data. It supports dataset upload or synthesis, performs missingness analysis, compares imputation methods, trains and optimizes models using Bayesian optimization via Optuna, and delivers results through a lightweight dashboard. The system optionally uses MongoDB for persistent storage of experiments, datasets, and user sessions, enabling reproducibility and result tracking. This integrated approach combines data analysis, modeling, and visualization in a single deployable app.
- ▪The application uses Flask to serve both the API and a frontend dashboard for end-to-end missing-data analysis and model evaluation.
- ▪It supports multiple imputation methods including mean, median, iterative imputation, and row dropping, along with regression models like linear regression, ridge, lasso, and random forest.
- ▪Bayesian optimization is implemented using Optuna to tune model hyperparameters and improve performance based on validation metrics.
- ▪MongoDB is used for optional persistence of dataset metadata, experiment results, user authentication, and performance trends.
- ▪Generated outputs include evaluation metrics (MSE, RMSE, R2, MAE), diagnostic plots, predictions, and comparison visualizations stored in the results directory.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3901872) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Sangeeth Macherla Posted on Apr 28 How Missing Data Analysis Lab uses Flask, Bayesian optimization, and MongoDB in one regression workflow #python #mongodb #datascience #machinelearning Missing Data Analysis Lab is a Flask-served Python application for studying missing-value behavior, comparing imputation strategies, training regression models, and surfacing experiment results through a lightweight dashboard.
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