Features

Features from the current release.

Data Loading and Handling

• get_dataset: Load a dataset.

• get_training_test_data: Split the dataset into training and test sets.

• load_large_dataset: Load a large dataset efficiently.

• reduce_data_memory_useage: Reduce memory usage of the dataset.

Data Cleaning and Manipulation

• drop_columns: Drop specified columns from the dataset.

• fix_missing_values: Handle missing values in the dataset.

• fix_unbalanced_dataset: Address class imbalance in a classification dataset.

• filter_data: Filter data based on specified conditions.

• remove_duplicates: Remove duplicate rows from the dataset.

• rename_columns: Rename columns in the dataset.

• replace_values: Replace specified values in the dataset.

• reset_index: Reset the index of the dataset.

• set_index: Set a specific column as the index.

• sort_index: Sort the index of the dataset.

• sort_values: Sort the values of the dataset.

Data Formatting and Transformation

• categorical_to_datetime: Convert categorical columns to datetime format.

• categorical_to_numerical: Convert categorical columns to numerical format.

• numerical_to_categorical: Convert numerical columns to categorical format.

• column_binning: Bin values in a column into specified bins.

Exploratory Data Analysis

• eda: Perform exploratory data analysis on the dataset.

• eda_visual: Visualize exploratory data analysis results.

• pandas_profiling: Generate a Pandas Profiling report for the dataset.

• sweetviz_profile_report: Generate a Sweetviz Profile Report for the dataset.

• count_column_categories: Count the categories in a categorical column.

• unique_elements_in_columns: Get the unique elements that exist in each column in the dataset.

Feature Engineering

• extract_date_features: Extract date-related features from a datetime column.

• polyreg_x: Get the polynomial regression x for independent variables after specifying the degree.

• select_features: Select relevant features for modeling.

• select_dependent_and_independent: Select dependent and independent variables.

Data Preprocessing

• scale_independent_variables: Scale independent variables in the dataset.

• remove_outlier: Remove outliers from the dataset.

• split_data: Split the dataset into training and test sets.

Model Building and Evaluation

• poly_get_optimal_degree: Find the best degree for polynomial regression.

• get_bestK_KNNregressor: Find the best K value for KNN regression.

• train_model_regressor: Train a regression model.

• regressor_predict: Make predictions using a regression model.

• regressor_evaluation: Evaluate the performance of a regression model.

• regressor_model_testing: Test a regression model.

• polyreg_graph: Visualize a polynomial regression graph.

• simple_linregres_graph: Visualize a regression graph.

• build_multiple_regressors: Build multiple regression models.

• build_multiple_regressors_from_features: Build regression models using selected features.

• build_single_regressor_from_features: Build a single regression model using selected features.

• get_bestK_KNNclassifier: Find the best K value for KNN classification.

• train_model_classifier: Train a classification model.

• classifier_predict: Make predictions using a classification model.

• classifier_evaluation: Evaluate the performance of a classification model.

• classifier_model_testing: Test a classification model.

• classifier_graph: Visualize a classification graph.

• build_multiple_classifiers: Build multiple classification models.

• build_multiple_classifiers_from_features: Build classification models using selected features.

• build_single_classifier_from_features: Build a single classification model using selected features.

Data Aggregation and Summarization

• group_data: Group and summarize data based on specified conditions.

Data Type Handling

• select_datatype: Select columns of a specific datatype in the dataset.