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Project description

utilsds

Utilsds is a library that includes classes and functions used in data science projects such as:

  • ds_statistics:

    • test_kruskal_wallis: Perform the Kruskal-Wallis statistical test.
  • transform_data:

    • DataTransformer: Transform data using various methods.
  • data_processing:

    • encode_one_hot: Encode categorical features using one-hot encoding.
    • convert_numerical_to_categorized: Convert numerical features to categorized intervals.
    • scale_train_test: Scale training and testing datasets.
    • resample_X_y: Resample training data and target columns.
  • data_split:

    • train_test_validation_split: Split data into training, testing, and validation sets.
  • visualization:

    • MetricsPlot: Compare metrics for different parameter values.
    • Radar: Create radar plots for visualizing data.
    • cluster_characteristics: Analyze cluster characteristics.
    • comparison_density: Compare density distributions.
    • feature_distribution_box: Visualize feature distributions per cluster.
    • elbow_visualisation: Visualize the elbow method for clustering.
    • describe_clusters_metrics: Describe metrics for clusters.
    • category_null_variables: Visualize null variables in categorical data.
    • normal_distr_plots: Visualize normal distribution plots.
    • distplot_limitations: Visualize limitations of distplot.
    • boxplot_limitations: Visualize limitations of boxplot.
    • violinplot_limitations: Visualize limitations of violinplot.
    • countplot_limitations: Visualize limitations of countplot.
    • categorical_variable_perc: Visualize percentage of categorical variables.
    • spearman_correlation: Visualize spearman correlation.
    • CalculateCrammersV: Calculate Crammer's V.
  • data_ops:

    • DataOperations: Handle data operations with Google Cloud services (BigQuery and Cloud Storage).
  • confusion_matrix:

    • ConfusionMatrix: Generate and plot confusion matrices.
  • modeling:

    • Modeling: Manage modeling, metrics, and logging with Vertex AI.
  • hyperopt:

    • Hyperopt: Optimize hyperparameters using Hyperopt.
  • classifier:

    • Classifier: Fit, train, and manage classification models.
  • experiments:

    • VertexExperiments: Manage experiments with Vertex AI.

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