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Explore data like a Kaggle grandmaster

Automated Data Preparation for Machine Learning and More

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    Use hundreds of machine learning models to derive deep data insights

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    Tells you which columns are useful and not useful

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    Creates beautiful charts and reports

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    Cleans your data however large

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    Download a machine learning ready dataset in minutes

Kortical platform's Data Prep feature preview

Automating Exploratory Data Analysis

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Put the data into Kortical

Upload a CSV this can have free text columns, numeric columns, categorical columns, date columns.

You just need a title row for the column names and then rows of data.

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See if the shape of the data what you expected

Did they send you a bunch of customer demographics but not realise nobody ever fills this in? (Happens more than you'd think)

Or is an interesting sounding column actually filled with a placeholder value? Kortical will show you.

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See pretty chart of your data

With distributions, missing values, most frequent value, number of unique values and a host of other useful information.

Are there outliers? Need to report to a client? We've got you covered. Want to see plots against the target, no problem.

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Taking Exploratory Data Analysis To The Next Level

Feature importances chart image layered on top of screenshot of exploring columns that can be removed

Wouldn't it be cool if you just knew which features were important? Instead of sifting through hundreds of columns to find data-science gold, you had a tool that just told you up front?


Well now you do. Kortical runs hundreds of machine learning models at cloud scale to give you almost instant feature importances, making exploratory data analysis much less of a slog.

Training models and exploring large datasets can take AGES, when someone throws the kitchen sink at you most of the columns are probably useless. What you need is something with a lot of clever machine learning that can figure out which columns add no value to the predictions and tells you which columns to drop. Even better let's you download a dataset with those columns dropped.

Chart of Name feature vs Target Survived

Plotting text columns vs what you want to predict is traditionally a bit pointless as an analysis but by injecting machine learning into exploratory data analysis means we are able to pick out the most predictive words in the dataset and show how they interact with the target.

Target indicative scores with Actions summary screenshot

Do you want a tool that can figure out variables that leak, remove outliers, fix date formats, drop highly correlated columns, remove dodgy rows, impute missing values and so much more automatically, so you can just download a fixed up dataset in under 10 minutes and get on with the fun stuff of model building and feature engineering?

Get your invite now and start working smarter