A powerful data science console in R / Shiny.
Demo @ https://bolaka.shinyapps.io/data_exploration
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Loading data (implemented).
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Generate meta data for all the features/variables and identify the target/s (implemented).
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Visualize statistics about rows and columns and take actions if required i.e. drop sparse rows and columns (implemented).
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Univariate analysis - histograms for continuous and bar plots for categorical features. Also interactively remove outliers from charts just by clicking on them! (implemented).
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Important features - This step is just conjecture and needs more thought but the idea is to be able to identify useful / important features by fitting a simple model and looking at say the top ten influencers impacting the target (if continuous). Not sure how this can be done for a categorical target (roadmap).
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Bi-variate/Multivariate analysis - With knowlegde about the important influencers from the previous step, plot scatter charts, eat maps, box plots etc... to better understand relationships between features / variables (in progress...).
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Collinearity and Multicollinearity - Eliminate highly correlated pairs / groups of features (not yet implemented).
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Missing value treatment (not yet implemented).
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Outlier treatment (not yet implemented).
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Feature transformations (not yet implemented).
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Feature engineering (not yet implemented).
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Predictive modeling (roadmap).