optie 2 EDA schrappen

Author: devhelpr

src/assets/synthetic-data.tsx

@@ -241,13 +241,13 @@ def run():
         'realCorrelations': real_data.corr().to_json(orient="records"),
         'syntheticCorrelations': np.abs(df_numeric.corr() - synth_df_numeric.corr()).to_json(orient="records"),
         'reports' : [
-            {
-                'reportType': 'heading',
-                'headingKey': 'syntheticData.explanatoryDataAnalysisTitle'
-            },
-            {'reportType': 'univariateDistributionRealData'},
-            {'reportType': 'bivariateDistributionRealData'},
-            {'reportType': 'correlationRealData'},
+            # {
+            #    'reportType': 'heading',
+            #    'headingKey': 'syntheticData.explanatoryDataAnalysisTitle'
+            # },
+            # {'reportType': 'univariateDistributionRealData'},
+            # {'reportType': 'bivariateDistributionRealData'},
+            # {'reportType': 'correlationRealData'},
             {
                 'reportType': 'heading',
                 'headingKey': 'syntheticData.cartModelTitle'

src/locales/en.json

@@ -101,11 +101,11 @@
             "heading": "0. Preview of data"
         },
         "columnsInDataset": "1. Data types detection",
-        "explanatoryDataAnalysisTitle": "2. Explanatory data analysis",
-        "cartModelTitle": "3. CART model",
+        "_explanatoryDataAnalysisTitle": "2. Explanatory data analysis",
+        "cartModelTitle": "2. CART model",
         "cartModelDescription": "The CART (Classification and Regression Trees) method generates synthetic data by learning patterns from real data through a decision tree that splits data into homogeneous groups based on feature values. It predicts averages for numerical data and assigns the most common category for categorical data, using these predictions to create new synthetic points.",
-        "evaluationOfGeneratedDataTitle": "4. Evaluation of generated data",
-        "outputDataTitle": "5. Output data",
+        "evaluationOfGeneratedDataTitle": "3. Evaluation of generated data",
+        "outputDataTitle": "4. Output data",
         "diagnosticsTitle": "Diagnostic Results:",
         "correlationDifference": "Correlation difference: {{correlationDifference}}"
     },

src/locales/nl.json

@@ -101,11 +101,11 @@
             "heading": "0. Preview van de data"
         },
         "columnsInDataset": "1. Detectie van datatypes",
-        "explanatoryDataAnalysisTitle": "2. Explanatory data analyse",
-        "cartModelTitle": "3. CART model",
+        "_explanatoryDataAnalysisTitle": "2. Explanatory data analyse",
+        "cartModelTitle": "2. CART model",
         "cartModelDescription": "De CART-methode (Classification and Regression Trees) genereert synthetische data door patronen uit echte data te leren via een beslisboom die de data opdeelt in homogene groepen op basis van kenmerkwaarden. Voor numerieke data voorspelt de methode gemiddelden, en voor categorische data wijst het de meest voorkomende categorie toe. Deze voorspellingen worden vervolgens gebruikt om nieuwe synthetische gegevenspunten te creëren.",
-        "evaluationOfGeneratedDataTitle": "4. Evaluatie van gegenereerde data",
-        "outputDataTitle": "5. Output data",
+        "evaluationOfGeneratedDataTitle": "3. Evaluatie van gegenereerde data",
+        "outputDataTitle": "4. Output data",
         "diagnosticsTitle": "Diagnostische Resultaten:",
         "correlationDifference": "Correlatie verschil: {{correlationDifference}}"
     },