feat: change E2E AutoML dataset to use bank marketing data

* Change E2E AutoML dataset to use bank marketing data

* Remove vs code config

notebooks/official/automl/automl_tabular_on_vertex_pipelines.ipynb

@@ -88,7 +88,8 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset you will be using is the [Safe Driver Prediction](https://www.kaggle.com/competitions/porto-seguro-safe-driver-prediction/data?select=train.csv) dataset for predicting the probability of an auto insurance policy holder filing a claim for a given incident."
+        "The dataset you will be using is [Bank Marketing](https://archive.ics.uci.edu/ml/datasets/bank+marketing).\n",
+        "The data is for direct marketing campaigns (phone calls) of a Portuguese banking institution. The binary classification goal is to predict if a client will subscribe a term deposit. For this notebook, we randomly selected 90% of the rows in the original dataset and saved them in a train.csv file hosted on Cloud Storage. To download the file, click [here](https://storage.googleapis.com/cloud-samples-data/vertex-ai/tabular-workflows/datasets/bank-marketing/train.csv)."
       ]
     },
     {
@@ -638,9 +639,9 @@
         "root_dir = os.path.join(BUCKET_URI, \"automl_tabular_pipeline\")\n",
         "prediction_type = \"classification\"\n",
         "optimization_objective = \"minimize-log-loss\"\n",
-        "target_column = \"target\"\n",
+        "target_column = \"deposit\"\n",
         "data_source_csv_filenames = (\n",
-        "    \"gs://cloud-samples-data/vertex-ai/tabular-workflows/datasets/safe-driver/train.csv\"\n",
+        "    \"gs://cloud-samples-data/vertex-ai/tabular-workflows/datasets/bank-marketing/train.csv\"\n",
         ")\n",
         "data_source_bigquery_table_path = None  # format: bq://bq_project.bq_dataset.bq_table\n",
         "\n",
@@ -659,63 +660,22 @@
         "weight_column = None\n",
         "\n",
         "features = [\n",
-        "    \"ps_ind_01\",\n",
-        "    \"ps_ind_02_cat\",\n",
-        "    \"ps_ind_03\",\n",
-        "    \"ps_ind_04_cat\",\n",
-        "    \"ps_ind_05_cat\",\n",
-        "    \"ps_ind_06_bin\",\n",
-        "    \"ps_ind_07_bin\",\n",
-        "    \"ps_ind_08_bin\",\n",
-        "    \"ps_ind_09_bin\",\n",
-        "    \"ps_ind_10_bin\",\n",
-        "    \"ps_ind_11_bin\",\n",
-        "    \"ps_ind_12_bin\",\n",
-        "    \"ps_ind_13_bin\",\n",
-        "    \"ps_ind_14\",\n",
-        "    \"ps_ind_15\",\n",
-        "    \"ps_ind_16_bin\",\n",
-        "    \"ps_ind_17_bin\",\n",
-        "    \"ps_ind_18_bin\",\n",
-        "    \"ps_reg_01\",\n",
-        "    \"ps_reg_02\",\n",
-        "    \"ps_reg_03\",\n",
-        "    \"ps_car_01_cat\",\n",
-        "    \"ps_car_02_cat\",\n",
-        "    \"ps_car_03_cat\",\n",
-        "    \"ps_car_04_cat\",\n",
-        "    \"ps_car_05_cat\",\n",
-        "    \"ps_car_06_cat\",\n",
-        "    \"ps_car_07_cat\",\n",
-        "    \"ps_car_08_cat\",\n",
-        "    \"ps_car_09_cat\",\n",
-        "    \"ps_car_10_cat\",\n",
-        "    \"ps_car_11_cat\",\n",
-        "    \"ps_car_11\",\n",
-        "    \"ps_car_12\",\n",
-        "    \"ps_car_13\",\n",
-        "    \"ps_car_14\",\n",
-        "    \"ps_car_15\",\n",
-        "    \"ps_calc_01\",\n",
-        "    \"ps_calc_02\",\n",
-        "    \"ps_calc_03\",\n",
-        "    \"ps_calc_04\",\n",
-        "    \"ps_calc_05\",\n",
-        "    \"ps_calc_06\",\n",
-        "    \"ps_calc_07\",\n",
-        "    \"ps_calc_08\",\n",
-        "    \"ps_calc_09\",\n",
-        "    \"ps_calc_10\",\n",
-        "    \"ps_calc_11\",\n",
-        "    \"ps_calc_12\",\n",
-        "    \"ps_calc_13\",\n",
-        "    \"ps_calc_14\",\n",
-        "    \"ps_calc_15_bin\",\n",
-        "    \"ps_calc_16_bin\",\n",
-        "    \"ps_calc_17_bin\",\n",
-        "    \"ps_calc_18_bin\",\n",
-        "    \"ps_calc_19_bin\",\n",
-        "    \"ps_calc_20_bin\",\n",
+        "    \"age\",\n",
+        "    \"job\",\n",
+        "    \"marital\",\n",
+        "    \"education\",\n",
+        "    \"default\",\n",
+        "    \"balance\",\n",
+        "    \"housing\",\n",
+        "    \"loan\",\n",
+        "    \"contact\",\n",
+        "    \"day\",\n",
+        "    \"month\",\n",
+        "    \"duration\",\n",
+        "    \"campaign\",\n",
+        "    \"pdays\",\n",
+        "    \"previous\",\n",
+        "    \"poutcome\",\n",
         "]\n",
         "transformations = generate_auto_transformation(features)\n",
         "transform_config_path = os.path.join(root_dir, f\"transform_config_{uuid.uuid4()}.json\")\n",