November 17 2022
Last updated
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Last updated
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Our dataset details page has a new look and feel
You can now register your text or categorical datasets and view detailed profiling information.
Data summary:
Details about the features, missing or duplicate values
Insights:
We now compute insights about features with "similar distributions", "skewness", and "negative values" and more about different features in your datasets.
Variable Details
Summary of different features in your dataset
Frequency charts, word cloud, pie charts, letter details for categorical variables
Quantile statistics, KDE Plot, QQ Plot, and Box plot for numeric variables
Correlation
For your registered datasets, you could see Pearson Correlation, Spearman Correlation, and KendallTau Correlation.
Missing Value Chart
Missing values in your dataset can be easily visualized through a frequency bar chart.
For text-based datasets, we now support probabilistic topic modeling based on LDA/ HDP.
MarkovML's Python Package is now supported inside Jupyterhub.
Simply import markov
to get started.
MarkovML's Jupyter Notebook now hasdataprep
a library pre-installed. You can profile. You can quickly run an EDA on datasets
We have improved our onboarding experience. After signing up, users are shown some ways to use MarkovML.
Users who are part of multiple workspaces can now easily switch between them.
Enterprise users can easily use MarkovML for work and personal use cases. Data between each workspace is stored separately.
switch
button on the top header barmarkov switch
allows users to change their active workspace while working with MarkovML's python package