README.md

Usage Examples

Datasets:

• [Iris dataset]
  • scikit-learn site
  • wikipedia
• Boston housing dataset
  • scikit-learn

creating iris-colored_acatter_matrix

You can create colored_acatter_matrix very easily ( 3 lines of code):

    plotter=DataPlots(df=iris_df, ggplot=True)
    fig=plotter.colored_scatter_matrix(df=iris_df,colored_column_name="Target")
    fig.savefig("iris-colored_acatter_matrix.png")

show me the complete code

simple example of iris dataset classification python

show me the code

• load iris dataset
• create a DecisionTreeClassifier
• create a ModelUtils

        prd_lbl, actl_lbl = "PrdictedIrisClass", "IrisClass"
        mu = ModelUtils(
                df            = iris_df,
                model           = tree_clf,
                predicted_lbl = "PrdictedIrisClass",
                actual_lbl    = "IrisClass"
                )

• split and train the model

        mu.split_and_train()

• test the model

        results_df = mu.test_model()

• evaluate results using plot_confusion_matrix

        evp = EvaluationPlots(df=results_df, actual_lbl=mu.actual_lbl, predicted_lbl=mu.predicted_lbl)
        evp.plot_confusion_matrix(confusion_matrix=mu.confusion_matrix(), classes_lst=mu.model.classes_)
        plt.savefig("confusion_matrix.png", bbox_inches='tight')

resulting this plot:

• evaluate results using plot_confusion_matrix

    cr = mu.classification_report(y_pred=results_df[prd_lbl], y_true=results_df[actl_lbl])
    evp.plot_classification_report(cr)
    plt.savefig("confusion_matrix.png", bbox_inches='tight')