data-analyzer 0.0.1

Auto EDA package

• Automatically detects numeric and categorical features
• User may manually assign numeric and categorical features using set_numeric_features() and set_categorical_features() methods if the feature detection is incorrect (recommended).
• get_data_structure_summary() provides basic information like head, tail, data types, missing value info, etc.
• get_categorical_features_summary() provides information like count of unique values, unique values, data distribution, etc of categorical features.
• get_numeric_features_summary() provides summary of numeric features along with distribution plots.
• plot_correlation_matrix() plots the Pearson and Spearman correlation matrices of all the numeric features.
• plot_chi_square_result() plots the p-values of the chi-square tests performed between categorical features.
• plot_numeric_vs_numeric() plots scatter plots between the numeric features.
• plot_categorical_vs_categorical() plots stacked bar plots between the categorical features.
• a.plot_mutual_information(target) plots a bar plot showing the mutual information score between the input features and target.
• a.get_vif() returns the VIF scores of all the numeric features.
• plot_categorical_vs_numeric() plots violin plots between all the categorical features and numeric features.