Merge pull request #750 from brightics/brtc-template-0427-3

delete description

va-server/visual-analytics/public/static/model-template/data/classification.json

@@ -4756,6 +4756,5 @@
     "GAP_WIDTH": 55,
     "GAP_HEIGHT": 40
   },
-  "problemList": [],
-  "description": "<p>■ Dataset : US Census income dataset (modified from http://archive.ics.uci.edu/ml/datasets/Census+Income)<br /> . y: &gt;50K, &lt;=50K.<br /> . x: age, fnlwgt, education-num, gender, capital-gain, capital-loss, hours-per-week<br />■ Target: Develop the income prediction model.</p><p>■ Analytics process</p><p> . Exploratory Data Analysis : Identify the characteristics of the data</p><p> . Analysis: Divide the sample into train set and test set and preform KNN Classification and Random Forest Classification.<br /> . Interpret results : Achieved accuracy 0.747 for KNN and 0.803 for Random Forest.</p>"
+  "problemList": []
 }

va-server/visual-analytics/public/static/model-template/data/clustering.json

@@ -7244,6 +7244,5 @@
         "GAP_WIDTH": 55,
         "GAP_HEIGHT": 40
       },
-      "problemList": [],
-      "description": "<p>■ Dataset : Iris <br /> . y: species<br /> . x: sepal_length, sepal_width, petal_length, petal_width<br />■ Target: Clustering of individual iris by features<br />■ Analytics process<br /> . Pre-Procession: One hot encoding for categorical variables (species)<br /> . Exploratory Data Analysis : Identify the characteristics of the data<br /> . Clustering : K-Means, Gaussian Mixture, Hierarchical clustering<br /> . Apply results : Profiling for cluster, Labeling test data</p>"
+      "problemList": []
     }

va-server/visual-analytics/public/static/model-template/data/regression.json

@@ -7411,6 +7411,5 @@
         "GAP_WIDTH": 55,
         "GAP_HEIGHT": 40
       },
-      "problemList": [],
-      "description": "<p>■ Dataset: abalone dataset (retrieved from http://archive.ics.uci.edu/ml/datasets/Abalone)<br /> . y: rings<br /> . x: Sex, Length, Diameter, Height, Whole weight, Shucked weight, Viscera weight, Shell weight<br />■ Target: Develop model for the age of abalones<br />■ Analytics process<br /> . Exploratory Data Analysis : Identify the characteristics of the data<br /> . Analysis: Perform linear regression, XGB regression and penalized linear regreession+random forest regression and perform evaluation<br /> . Interpret results : RMSE for linear regression is 2.200, XGB Regression is 2.187 and penalized+random forest is RMSE.</p>"
+      "problemList": []
     }

va-server/visual-analytics/public/static/model-template/data/scripts.json

@@ -3353,6 +3353,5 @@
             "GAP_WIDTH": 55,
             "GAP_HEIGHT": 40
           },
-          "problemList": [],
-          "description": "<p>■ Dataset : Iris <br /> . y: species<br /> . x: sepal_length, sepal_width, petal_length, petal_width<br />■ Target: Preprocessing and Exploratory Data Analysis<br />■ Analytics process<br /> . Exploratory Data Analysis : Identify the characteristics of the data<br /> . Data preprocessing: Data preprocessing using Query Executor and Python Script<br /> . Investigate summary statistics: Investigate distribution of data via summary statistics.</p>"
+          "problemList": []
         }

va-server/visual-analytics/public/static/model-template/data/text_analytics.json

@@ -7147,6 +7147,5 @@
         "GAP_WIDTH": 55,
         "GAP_HEIGHT": 40
       },
-      "problemList": [],
-      "description": "<p>■ Dataset : US state of the union dataset<br /> . variables: year, president, party, statement<br />■ Target: Perform sentiment analysis for the state of the union, and analyze it via linear regression</p><p>■ Analytics process<br /> . Exploratory Data Analysis : Identify the characteristics of the data<br /> . Preprocessing: Preprocess the text data<br /> . Analysis: Doc2Vec, followed by SVM classification</p><p> . Interpret result: Doc2Vec vectors successfully predicted the party of the president</p>"
+      "problemList": []
     }