GitHub - rsainir/MLClassifiers: Training classifier models (Decision Trees, Neural Networks, SVM, Naive Bayesian) to predict outcomes on datasets with both categorical and numerical features (UCI Datasets).

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Training classifier models (Decision Trees, Neural Networks, SVM, Naive Bayesian) to predict outcomes on datasets with both categorical and numerical features (UCI Datasets).

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Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
Classification.jl.ipynb		Classification.jl.ipynb
ClassificationReport.pdf		ClassificationReport.pdf
abalone.data		abalone.data
kddcup.data_10_percent_corrected		kddcup.data_10_percent_corrected
kddcup.names		kddcup.names
madelon_test.data		madelon_test.data
madelon_train.data		madelon_train.data
madelon_train.labels		madelon_train.labels
madelon_valid.data		madelon_valid.data
madelon_valid.labels		madelon_valid.labels

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Training classifier models (Decision Trees, Neural Networks, SVM, Naive Bayesian) to predict outcomes on datasets with both categorical and numerical features (UCI Datasets).

python machine-learning neural-network julia scikit-learn machine-learning-algorithms classification