This example prepares Titanic passenger data for classification by skipping the Data Preparation phase to get clean data for further analysis.
Run AutoDataprep to get the optimized data with the following specifications:
- Set task_type to Classification.
- Set the verbose level to 2 to obtain detailed information about intermediate steps.
- Set skip_phases to Data_Preparation.
- Load the titanic dataset.
>>> load_example_data("teradataml", "titanic") - Create the DataFrame.
>>> titanic = DataFrame.from_table("titanic") - Create an instance of AutoDataPrep.
>>> acls = AutoDataPrep(task_type="Classification", verbose=2, skip_phases=["Data_Preparation"]) - Fit the data.
>>> acls.fit(titanic, titanic.survived)
1. Feature Exploration -> 2. Feature Engineering__________________________________| 0% - 0/6 2026-04-07 03:33:05,577 | INFO | Feature Exploration started 2026-04-07 03:33:05,578 | INFO | Data Overview: 2026-04-07 03:33:05,620 | INFO | Total Rows in the data: 891 2026-04-07 03:33:05,621 | INFO | Total Columns in the data: 12 2026-04-07 03:33:07,149 | INFO | Column Summary: ColumnName Datatype NonNullCount NullCount BlankCount ZeroCount PositiveCount NegativeCount NullPercentage NonNullPercentage 0 age INTEGER 714 177 NaN 7.0 707.0 0.0 19.865320 80.134680 1 fare FLOAT 891 0 NaN 15.0 876.0 0.0 0.000000 100.000000 2 embarked VARCHAR(20) CHARACTER SET LATIN 889 2 0.0 NaN NaN NaN 0.224467 99.775533 3 sex VARCHAR(20) CHARACTER SET LATIN 891 0 0.0 NaN NaN NaN 0.000000 100.000000 4 name VARCHAR(1000) CHARACTER SET LATIN 891 0 0.0 NaN NaN NaN 0.000000 100.000000 5 sibsp INTEGER 891 0 NaN 608.0 283.0 0.0 0.000000 100.000000 6 passenger INTEGER 891 0 NaN 0.0 891.0 0.0 0.000000 100.000000 7 cabin VARCHAR(20) CHARACTER SET LATIN 204 687 0.0 NaN NaN NaN 77.104377 22.895623 8 parch INTEGER 891 0 NaN 678.0 213.0 0.0 0.000000 100.000000 9 ticket VARCHAR(20) CHARACTER SET LATIN 891 0 0.0 NaN NaN NaN 0.000000 100.000000 10 pclass INTEGER 891 0 NaN 0.0 891.0 0.0 0.000000 100.000000 11 survived INTEGER 891 0 NaN 549.0 342.0 0.0 0.000000 100.000000 2026-04-07 03:33:08,579 | INFO | Statistics of Data: ATTRIBUTE StatName StatValue 0 age MAXIMUM 80.000000 1 age STANDARD DEVIATION 14.536483 2 age PERCENTILES(25) 20.000000 3 age PERCENTILES(50) 28.000000 4 fare COUNT 891.000000 5 fare MINIMUM 0.000000 6 fare MAXIMUM 512.329200 7 fare MEAN 32.204208 8 fare STANDARD DEVIATION 49.693429 9 fare PERCENTILES(25) 7.910400 2026-04-07 03:33:08,728 | INFO | Categorical Columns with their Distinct values: ColumnName DistinctValueCount cabin 147 embarked 3 name 891 sex 2 ticket 681 2026-04-07 03:33:09,886 | INFO | Futile columns in dataset: ColumnName 0 name 1 ticket 2026-04-07 03:33:12,237 | INFO | Columns with outlier percentage :- ColumnName OutlierPercentage 0 sibsp 5.162738 1 parch 23.905724 2 fare 13.019080 3 age 20.763187 1. Feature Exploration -> 2. Feature Engineering 2026-04-07 03:33:12,424 | INFO | Feature Engineering started ... 2026-04-07 03:33:12,425 | INFO | Handling duplicate records present in dataset ... 2026-04-07 03:33:12,547 | INFO | Analysis completed. No action taken. 2026-04-07 03:33:12,547 | INFO | Total time to handle duplicate records: 0.12 sec 2026-04-07 03:33:12,548 | INFO | Handling less significant features from data ... 2026-04-07 03:33:13,390 | INFO | Removing Futile columns: ['ticket', 'name'] 2026-04-07 03:33:13,391 | INFO | Sample of Data after removing Futile columns: passenger survived pclass sex age sibsp parch fare cabin embarked automl_id 0 122 0 3 male NaN 0 0 8.0500 None S 11 1 40 1 3 female 14.0 1 0 11.2417 None C 10 2 631 1 1 male 80.0 0 0 30.0000 A23 S 18 3 570 1 3 male 32.0 0 0 7.8542 None S 15 4 326 1 1 female 36.0 0 0 135.6333 C32 C 13 5 305 0 3 male NaN 0 0 8.0500 None S 21 6 734 0 2 male 23.0 0 0 13.0000 None S 14 7 61 0 3 male 22.0 0 0 7.2292 None C 22 8 469 0 3 male NaN 0 0 7.7250 None Q 8 9 183 0 3 male 9.0 4 2 31.3875 None S 16 891 rows X 11 columns 2026-04-07 03:33:13,672 | INFO | Total time to handle less significant features: 1.12 sec 2026-04-07 03:33:13,672 | INFO | Handling Date Features ... 2026-04-07 03:33:13,672 | INFO | Analysis Completed. Dataset does not contain any feature related to dates. No action needed. 2026-04-07 03:33:13,672 | INFO | Total time to handle date features: 0.00 sec 2026-04-07 03:33:13,673 | INFO | Checking Missing values in dataset ... 2026-04-07 03:33:13,853 | INFO | Columns with their missing values: embarked: 2 cabin: 687 age: 177 2026-04-07 03:33:14,499 | INFO | Deleting rows of these columns for handling missing values: ['embarked'] 2026-04-07 03:33:14,643 | INFO | Sample of dataset after removing 2 rows: passenger survived pclass sex age sibsp parch fare cabin embarked automl_id 0 80 1 3 female 30.0 0 0 12.4750 None S 12 1 122 0 3 male NaN 0 0 8.0500 None S 11 2 591 0 3 male 35.0 0 0 7.1250 None S 19 3 40 1 3 female 14.0 1 0 11.2417 None C 10 4 265 0 3 female NaN 0 0 7.7500 None Q 9 5 530 0 2 male 23.0 2 1 11.5000 None S 17 6 326 1 1 female 36.0 0 0 135.6333 C32 C 13 7 305 0 3 male NaN 0 0 8.0500 None S 21 8 734 0 2 male 23.0 0 0 13.0000 None S 14 9 61 0 3 male 22.0 0 0 7.2292 None C 22 889 rows X 11 columns 2026-04-07 03:33:14,932 | INFO | Dropping these columns for handling missing values: ['cabin'] 2026-04-07 03:33:14,932 | INFO | Sample of dataset after removing 1 columns: passenger survived pclass sex age sibsp parch fare embarked automl_id 0 122 0 3 male NaN 0 0 8.0500 S 11 1 734 0 2 male 23.0 0 0 13.0000 S 14 2 61 0 3 male 22.0 0 0 7.2292 C 22 3 40 1 3 female 14.0 1 0 11.2417 C 10 4 80 1 3 female 30.0 0 0 12.4750 S 12 5 345 0 2 male 36.0 0 0 13.0000 S 20 6 326 1 1 female 36.0 0 0 135.6333 C 13 7 305 0 3 male NaN 0 0 8.0500 S 21 8 265 0 3 female NaN 0 0 7.7500 Q 9 9 530 0 2 male 23.0 2 1 11.5000 S 17 889 rows X 10 columns 2026-04-07 03:33:15,307 | INFO | Total time to find missing values in data: 1.63 sec 2026-04-07 03:33:15,308 | INFO | Imputing Missing Values ... 2026-04-07 03:33:15,549 | INFO | Columns with their imputation method: age: mean 2026-04-07 03:33:16,923 | INFO | Sample of dataset after Imputation: passenger survived pclass sex age sibsp parch fare embarked automl_id 0 122 0 3 male 29 0 0 8.0500 S 11 1 734 0 2 male 23 0 0 13.0000 S 14 2 61 0 3 male 22 0 0 7.2292 C 22 3 326 1 1 female 36 0 0 135.6333 C 13 4 570 1 3 male 32 0 0 7.8542 S 15 5 835 0 3 male 18 0 0 8.3000 S 23 6 80 1 3 female 30 0 0 12.4750 S 12 7 345 0 2 male 36 0 0 13.0000 S 20 8 265 0 3 female 29 0 0 7.7500 Q 9 9 530 0 2 male 23 2 1 11.5000 S 17 889 rows X 10 columns 2026-04-07 03:33:17,503 | INFO | Time taken to perform imputation: 2.19 sec 2026-04-07 03:33:17,504 | INFO | Performing encoding for categorical columns ... 2026-04-07 03:33:22,090 | INFO | ONE HOT Encoding these Columns: ['sex', 'embarked'] 2026-04-07 03:33:22,090 | INFO | Sample of dataset after performing one hot encoding: survived pclass sex_0 sex_1 age sibsp parch fare embarked_0 embarked_1 embarked_2 automl_id passenger 387 0 3 0 1 1 5 2 46.900 0 0 1 27 448 1 1 0 1 34 0 0 26.550 0 0 1 43 713 1 1 0 1 48 1 0 52.000 0 0 1 51 19 0 3 1 0 31 1 0 18.000 0 0 1 59 59 1 2 1 0 5 1 2 27.750 0 0 1 75 324 1 2 1 0 22 1 1 29.000 0 0 1 83 263 0 1 0 1 52 1 1 79.650 0 0 1 67 856 1 3 1 0 18 0 1 9.350 0 0 1 35 591 0 3 0 1 35 0 0 7.125 0 0 1 19 122 0 3 0 1 29 0 0 8.050 0 0 1 11 889 rows X 13 columns 2026-04-07 03:33:22,187 | INFO | Time taken to encode the columns: 4.68 sec 2026-04-07 03:33:22,187 | INFO | Skipping Data_Preparation phase as requested./6 - Retrieve the data.
>>> datas = acls.get_data() >>> print(datas)
{'feature_engineering_output': survived pclass sex_0 sex_1 age sibsp parch fare embarked_0 embarked_1 embarked_2 automl_id passenger 387 0 3 0 1 1 5 2 46.900 0 0 1 27 448 1 1 0 1 34 0 0 26.550 0 0 1 43 713 1 1 0 1 48 1 0 52.000 0 0 1 51 19 0 3 1 0 31 1 0 18.000 0 0 1 59 59 1 2 1 0 5 1 2 27.750 0 0 1 75 324 1 2 1 0 22 1 1 29.000 0 0 1 83 263 0 1 0 1 52 1 1 79.650 0 0 1 67 856 1 3 1 0 18 0 1 9.350 0 0 1 35 591 0 3 0 1 35 0 0 7.125 0 0 1 19 122 0 3 0 1 29 0 0 8.050 0 0 1 11} - Delete the deployed data.Deletion of data can be partial or complete.
- Partial delete using fs_method:
>>> adp.delete_data(table_name='titanic_deploy', fs_method='pca')
Removed pca_train table successfully.
- Remove all data (complete):
>>> adp.delete_data(table_name='titanic_deploy')
Removed lasso_train table successfully. Removed rfe_train table successfully. Deployed data removed successfully.
- Partial delete using fs_method: