Get fast inference on your data, with a range of tools/algorithms at your disposal.
Visualization included. Code snippets to see what's running under the hood, too.
[click here: https://mlcpt.herokuapp.com]
SUPPORTS ONLY CLASSIFICATION TASKS (for now..)
use the EXPLORE section
- The app expects you provide two separate files(train and test) so be sure to include those
- ...the rest is handled automatically, but you can fine-tune as you would like.
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DateTime feature engineering (datetime columns are automatically selected for you)
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Duplicated rows will be dropped
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Columns of type object will be transformed to lower case as to help with cleaning and duplicity(ex: Male and MALE ==> male)
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ID Field selection for final test file (i.e | ID* | target | | ------:| -----------:| | customerID001 | 1 | | customerID00109 | 0 | | . | . | | . | . |
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Target column is automatically collected (looks for "target", "claim", "prediction", "response" as shown above; you can also pick the desired target column)
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Retain Missing Data as you want(default is 50%) and you can also choose how to treat the missing data(default is mode)
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Visualization
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Selecting features to drop
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Monotonic or/and unique data dropping (ID has been stored before this)
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GetDummies on categorical features (drop_first is True)
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Choose scaler (Standardization or Normalization)
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Download your processed dataset(train, test)
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Auto dataset split (60% - Train, 30% - Validation and 10% - Test)
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(NEW*) SMOTE, RANDOM OVER/UNDER SAMPLER FOR IMBALANCED DATASET!
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Algorithm selection (Catboost, Knn, RandomForest and Xgboost)
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Detailed report on prediction
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Save Test prediction to obtain a baseline model score on your Hackathon
If your dataset fails to parse correctly then it's a sign for you to contribute to the project. Be sure to checkout on a new branch for any feature/fix you add. :)
To contribute: here
Did you get a score with our test prediction? Kindly include that below(screenshot or in writing)
- UmojaHack Nigeria: AXA Vehicle Insurance Claim Challenge by UmojaHack Africa (on ZINDI) - ~37.6% (72nd ranking) [base selections]
If you have a dataset(classification based) that fails using this app, kindly include it here as a PR.