From 5e97589317ba5b8c9955bec1cec8ff96666ca734 Mon Sep 17 00:00:00 2001 From: Matt Dancho Date: Thu, 9 Jan 2025 22:39:45 -0500 Subject: [PATCH] #16 OOP Upgrade Feature Engineering Agent Example --- examples/data_cleaning_agent.ipynb | 126 - examples/feature_engineering_agent.ipynb | 54954 ++++++++++++++++++++- 2 files changed, 54493 insertions(+), 587 deletions(-) diff --git a/examples/data_cleaning_agent.ipynb b/examples/data_cleaning_agent.ipynb index d0c15f8..d80c6a0 100644 --- a/examples/data_cleaning_agent.ipynb +++ b/examples/data_cleaning_agent.ipynb @@ -1187,132 +1187,6 @@ "data_cleaning_agent.get_recommended_cleaning_steps(markdown=True)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Explore the agent documentation for more information" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;31mType:\u001b[0m DataCleaningAgent\n", - "\u001b[0;31mString form:\u001b[0m \n", - "\u001b[0;31mFile:\u001b[0m ~/Desktop/course_code/ai-data-science-team/ai_data_science_team/agents/data_cleaning_agent.py\n", - "\u001b[0;31mDocstring:\u001b[0m \n", - "Creates a data cleaning agent that can process datasets based on user-defined instructions or default cleaning steps. \n", - "The agent generates a Python function to clean the dataset, performs the cleaning, and logs the process, including code \n", - "and errors. It is designed to facilitate reproducible and customizable data cleaning workflows.\n", - "\n", - "The agent performs the following default cleaning steps unless instructed otherwise:\n", - "\n", - "- Removing columns with more than 40% missing values.\n", - "- Imputing missing values with the mean for numeric columns.\n", - "- Imputing missing values with the mode for categorical columns.\n", - "- Converting columns to appropriate data types.\n", - "- Removing duplicate rows.\n", - "- Removing rows with missing values.\n", - "- Removing rows with extreme outliers (values 3x the interquartile range).\n", - "\n", - "User instructions can modify, add, or remove any of these steps to tailor the cleaning process.\n", - "\n", - "Parameters\n", - "----------\n", - "model : langchain.llms.base.LLM\n", - " The language model used to generate the data cleaning function.\n", - "n_samples : int, optional\n", - " Number of samples used when summarizing the dataset. Defaults to 30. Reducing this number can help \n", - " avoid exceeding the model's token limits.\n", - "log : bool, optional\n", - " Whether to log the generated code and errors. Defaults to False.\n", - "log_path : str, optional\n", - " Directory path for storing log files. Defaults to None.\n", - "file_name : str, optional\n", - " Name of the file for saving the generated response. Defaults to \"data_cleaner.py\".\n", - "overwrite : bool, optional\n", - " Whether to overwrite the log file if it exists. If False, a unique file name is created. Defaults to True.\n", - "human_in_the_loop : bool, optional\n", - " Enables user review of data cleaning instructions. Defaults to False.\n", - "bypass_recommended_steps : bool, optional\n", - " If True, skips the default recommended cleaning steps. Defaults to False.\n", - "bypass_explain_code : bool, optional\n", - " If True, skips the step that provides code explanations. Defaults to False.\n", - "\n", - "Methods\n", - "-------\n", - "update_params(**kwargs)\n", - " Updates the agent's parameters and rebuilds the compiled state graph.\n", - "ainvoke(user_instructions: str, data_raw: pd.DataFrame, max_retries=3, retry_count=0)\n", - " Cleans the provided dataset asynchronously based on user instructions.\n", - "invoke(user_instructions: str, data_raw: pd.DataFrame, max_retries=3, retry_count=0)\n", - " Cleans the provided dataset synchronously based on user instructions.\n", - "explain_cleaning_steps()\n", - " Returns an explanation of the cleaning steps performed by the agent.\n", - "get_log_summary()\n", - " Retrieves a summary of logged operations if logging is enabled.\n", - "get_state_keys()\n", - " Returns a list of keys from the state graph response.\n", - "get_state_properties()\n", - " Returns detailed properties of the state graph response.\n", - "get_data_cleaned()\n", - " Retrieves the cleaned dataset as a pandas DataFrame.\n", - "get_data_raw()\n", - " Retrieves the raw dataset as a pandas DataFrame.\n", - "get_data_cleaner_function()\n", - " Retrieves the generated Python function used for cleaning the data.\n", - "get_recommended_cleaning_steps()\n", - " Retrieves the agent's recommended cleaning steps.\n", - "\n", - "Examples\n", - "--------\n", - "```python\n", - "import pandas as pd\n", - "from langchain_openai import ChatOpenAI\n", - "from ai_data_science_team.agents import DataCleaningAgent\n", - "\n", - "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n", - "\n", - "data_cleaning_agent = DataCleaningAgent(\n", - " model=llm, n_samples=50, log=True, log_path=\"logs\", human_in_the_loop=True\n", - ")\n", - "\n", - "df = pd.read_csv(\"https://raw.githubusercontent.com/business-science/ai-data-science-team/refs/heads/master/data/churn_data.csv\")\n", - "\n", - "data_cleaning_agent.invoke(\n", - " user_instructions=\"Don't remove outliers when cleaning the data.\",\n", - " data_raw=df,\n", - " max_retries=3,\n", - " retry_count=0\n", - ")\n", - "\n", - "cleaned_data = data_cleaning_agent.get_data_cleaned()\n", - "\n", - "response = data_cleaning_agent.response\n", - "```\n", - "\n", - "Returns\n", - "--------\n", - "DataCleaningAgent : langchain.graphs.CompiledStateGraph \n", - " A data cleaning agent implemented as a compiled state graph. \n", - "\u001b[0;31mInit docstring:\u001b[0m\n", - "Initialize the agent with provided parameters.\n", - "\n", - "Parameters:\n", - " **params: Arbitrary keyword arguments representing the agent's parameters." - ] - } - ], - "source": [ - "?data_cleaning_agent" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/examples/feature_engineering_agent.ipynb b/examples/feature_engineering_agent.ipynb index c9f8b84..1d96756 100644 --- a/examples/feature_engineering_agent.ipynb +++ b/examples/feature_engineering_agent.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ "import pandas as pd\n", "from pprint import pprint\n", "\n", - "from ai_data_science_team.agents import make_feature_engineering_agent" + "from ai_data_science_team.agents import FeatureEngineeringAgent" ] }, { @@ -66,16 +66,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "ChatOpenAI(client=, async_client=, root_client=, root_async_client=, model_name='gpt-4o-mini', model_kwargs={}, openai_api_key=SecretStr('**********'))" + "ChatOpenAI(client=, async_client=, root_client=, root_async_client=, model_name='gpt-4o-mini', model_kwargs={}, openai_api_key=SecretStr('**********'))" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -491,7 +491,7 @@ "[7043 rows x 21 columns]" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -512,23 +512,23 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "feature_engineering_agent = make_feature_engineering_agent(\n", + "feature_engineering_agent = FeatureEngineeringAgent(\n", " model = llm, \n", " log=LOG, \n", " log_path=LOG_PATH\n", @@ -552,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -560,32 +560,22 @@ "output_type": "stream", "text": [ "---FEATURE ENGINEERING AGENT----\n", + " * RECOMMEND FEATURE ENGINEERING STEPS\n", " * CREATE FEATURE ENGINEERING CODE\n", - " * EXECUTING AGENT CODE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":12: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " File saved to: /Users/mdancho/Desktop/course_code/ai-data-science-team/logs/feature_engineer.py\n", + " * EXECUTING AGENT CODE\n", " * EXPLAIN AGENT CODE\n" ] } ], "source": [ - "response = feature_engineering_agent.invoke({\n", - " \"target_variable\": \"Churn\",\n", - " \"data_raw\": df.to_dict(),\n", - " \"max_retries\":3, \n", - " \"retry_count\":0\n", - "})" + "feature_engineering_agent.invoke(\n", + " target_variable=\"Churn\",\n", + " user_instructions=\"Inspect the data. Make any new features and transformations that you think will be useful for predicting the target variable.\",\n", + " data_raw=df,\n", + " max_retries=3,\n", + " retry_count=0,\n", + ")" ] }, { @@ -599,211 +589,54098 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['messages',\n", - " 'data_raw',\n", - " 'target_variable',\n", - " 'feature_engineer_function',\n", - " 'feature_engineer_error',\n", - " 'data_engineered',\n", - " 'max_retries',\n", - " 'retry_count']" + "{'messages': [AIMessage(content='# Feature Engineering Agent:\\n\\n The `feature_engineer` function performs several key feature engineering steps on the input dataset (`data_raw`). Here’s a concise breakdown of these steps:\\n\\n1. **Convert Data Types**: \\n - Converts the \\'TotalCharges\\' column to numeric type, coercing errors into NaNs.\\n - Changes the \\'Churn\\' column to a categorical type for better representation.\\n\\n2. **Remove Unique Value Features**: \\n - Drops the \\'customerID\\' column since it is unique for each entry and does not contribute to model training.\\n\\n3. **Remove Constant Features**: \\n - Identifies and removes columns that have only one unique value, as they provide no useful information.\\n\\n4. **High Cardinality Categorical Encoding**: \\n - For specified high cardinality features (\\'InternetService\\', \\'Contract\\', \\'PaymentMethod\\'), it consolidates infrequent categories (those making up less than 5% of the data) into a single category labeled \"Other\".\\n\\n5. **One-Hot Encoding**: \\n - Applies one-hot encoding to multiple categorical features for model compatibility, avoiding the dummy variable trap by dropping the first category.\\n\\n6. **Convert Boolean to Integer**: \\n - Converts the \\'SeniorCitizen\\' column from Boolean to integer type (0 or 1).\\n - Encodes the \\'Churn\\' target variable into numeric codes.\\n\\n7. **Analyze for Additional Features**:\\n - Creates new features:\\n - **AvgMonthlyCharge**: Calculates average monthly charges per tenure (with NaN handling).\\n - **ServiceCount**: Sums various service-related features to count the total services utilized by a customer.\\n\\n8. **Final Review**:\\n - Leaves room for additional transformations if necessary, ensuring the data is ready for machine learning models.\\n\\nThis function ultimately prepares the dataset by cleaning, encoding, and creating relevant features for better performance in predictive modeling.', additional_kwargs={}, response_metadata={}, role='feature_engineering_agent')],\n", + " 'user_instructions': 'Inspect the data. Make any new features and transformations that you think will be useful for predicting the target variable.',\n", + " 'recommended_steps': '\\n\\n# Recommended Feature Engineering Steps:\\n1. **Convert Data Types**: \\n - Convert `TotalCharges` from object to float, as it represents numerical values.\\n - Ensure `Churn` is treated as a categorical variable for encoding.\\n\\n2. **Remove Unique Value Features**: \\n - Remove `customerID` as it has unique values equal to the size of the dataset (7043).\\n\\n3. **Remove Constant Features**: \\n - Check for any columns that have the same value across all rows and remove them if found. \\n\\n4. **High Cardinality Categorical Encoding**: \\n - Analyze categorical features for high cardinality. For features like `InternetService`, `Contract`, and `PaymentMethod`, if they have more than 5% unique values, consolidate infrequent categories into the \"other\" category.\\n\\n5. **One-Hot Encoding**: \\n - Apply One-Hot Encoding to categorical features: `gender`, `Partner`, `Dependents`, `PhoneService`, `MultipleLines`, `InternetService`, `OnlineSecurity`, `OnlineBackup`, `DeviceProtection`, `TechSupport`, `StreamingTV`, `StreamingMovies`, `Contract`, `PaperlessBilling`, and `PaymentMethod`.\\n\\n6. **Convert Boolean to Integer**: \\n - Convert `SeniorCitizen`, `Churn`, and any categorical features that represent binary values into integers (1 for True, 0 for False) after one-hot encoding.\\n\\n7. **Create Datetime Features**: \\n - If there are any datetime columns in future data, create relevant features such as year, month, day, day of the week, etc. (not applicable here based on provided data).\\n\\n8. **Encode Target Variable**: \\n - If `Churn` is categorical, apply Label Encoding to convert it into numeric form (0 and 1).\\n\\n9. **Analyze for Additional Features**: \\n - Examine existing features for potential new feature creation, such as:\\n - Creating a feature for `MonthlyCharges` divided by `tenure` to understand the average monthly charge per period.\\n - Creating a feature that sums the number of services subscribed (e.g., OnlineSecurity, OnlineBackup, etc.) to gauge customer engagement.\\n\\n10. **Final Review**: \\n - Review the newly engineered features for any additional transformations or combinations that could enhance predictive performance before finalizing the dataset. \\n\\nNo additional steps are required beyond those outlined above.',\n", + " 'data_raw': {'customerID': {0: '7590-VHVEG',\n", + " 1: '5575-GNVDE',\n", + " 2: '3668-QPYBK',\n", + " 3: '7795-CFOCW',\n", + " 4: '9237-HQITU',\n", + " 5: '9305-CDSKC',\n", + " 6: '1452-KIOVK',\n", + " 7: '6713-OKOMC',\n", + " 8: '7892-POOKP',\n", + " 9: '6388-TABGU',\n", + " 10: 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" 799: 'No',\n", + " 800: 'No',\n", + " 801: 'No',\n", + " 802: 'No internet service',\n", + " 803: 'Yes',\n", + " 804: 'Yes',\n", + " 805: 'No',\n", + " 806: 'No',\n", + " 807: 'No',\n", + " 808: 'Yes',\n", + " 809: 'No',\n", + " 810: 'Yes',\n", + " 811: 'No',\n", + " 812: 'No internet service',\n", + " 813: 'No',\n", + " 814: 'No internet service',\n", + " 815: 'No',\n", + " 816: 'No',\n", + " 817: 'Yes',\n", + " 818: 'Yes',\n", + " 819: 'Yes',\n", + " 820: 'No',\n", + " 821: 'No',\n", + " 822: 'Yes',\n", + " 823: 'No',\n", + " 824: 'No internet service',\n", + " 825: 'Yes',\n", + " 826: 'No',\n", + " 827: 'No internet service',\n", + " 828: 'Yes',\n", + " 829: 'No',\n", + " 830: 'Yes',\n", + " 831: 'No internet service',\n", + " 832: 'Yes',\n", + " 833: 'Yes',\n", + " 834: 'No',\n", + " 835: 'Yes',\n", + " 836: 'No',\n", + " 837: 'No',\n", + " 838: 'No',\n", + " 839: 'No internet service',\n", + " 840: 'No',\n", + " 841: 'No',\n", + " 842: 'No internet service',\n", + " 843: 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" 977: 'No',\n", + " 978: 'Yes',\n", + " 979: 'No',\n", + " 980: 'No internet service',\n", + " 981: 'No internet service',\n", + " 982: 'No',\n", + " 983: 'No',\n", + " 984: 'No internet service',\n", + " 985: 'Yes',\n", + " 986: 'No',\n", + " 987: 'No internet service',\n", + " 988: 'No internet service',\n", + " 989: 'No internet service',\n", + " 990: 'No',\n", + " 991: 'No internet service',\n", + " 992: 'No internet service',\n", + " 993: 'No internet service',\n", + " 994: 'No internet service',\n", + " 995: 'Yes',\n", + " 996: 'No',\n", + " 997: 'No',\n", + " 998: 'No internet service',\n", + " 999: 'No',\n", + " ...},\n", + " 'DeviceProtection': {0: 'No',\n", + " 1: 'Yes',\n", + " 2: 'No',\n", + " 3: 'Yes',\n", + " 4: 'No',\n", + " 5: 'Yes',\n", + " 6: 'No',\n", + " 7: 'No',\n", + " 8: 'Yes',\n", + " 9: 'No',\n", + " 10: 'No',\n", + " 11: 'No internet service',\n", + " 12: 'Yes',\n", + " 13: 'Yes',\n", + " 14: 'Yes',\n", + " 15: 'Yes',\n", + " 16: 'No internet service',\n", + 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" 616: 'No',\n", + " 617: 'Yes',\n", + " 618: 'No',\n", + " 619: 'No',\n", + " 620: 'No',\n", + " 621: 'No',\n", + " 622: 'Yes',\n", + " 623: 'No internet service',\n", + " 624: 'No internet service',\n", + " 625: 'Yes',\n", + " 626: 'No',\n", + " 627: 'Yes',\n", + " 628: 'Yes',\n", + " 629: 'No',\n", + " 630: 'Yes',\n", + " 631: 'No',\n", + " 632: 'Yes',\n", + " 633: 'No internet service',\n", + " 634: 'Yes',\n", + " 635: 'Yes',\n", + " 636: 'Yes',\n", + " 637: 'No',\n", + " 638: 'Yes',\n", + " 639: 'No',\n", + " 640: 'No internet service',\n", + " 641: 'Yes',\n", + " 642: 'Yes',\n", + " 643: 'No',\n", + " 644: 'No internet service',\n", + " 645: 'Yes',\n", + " 646: 'No',\n", + " 647: 'Yes',\n", + " 648: 'No',\n", + " 649: 'No',\n", + " 650: 'No',\n", + " 651: 'No',\n", + " 652: 'No internet service',\n", + " 653: 'No internet service',\n", + " 654: 'No internet service',\n", + " 655: 'Yes',\n", + " 656: 'Yes',\n", + " 657: 'Yes',\n", + " 658: 'No internet service',\n", + " 659: 'No 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service',\n", + " 405: 'No internet service',\n", + " 406: 'No',\n", + " 407: 'Yes',\n", + " 408: 'No internet service',\n", + " 409: 'No internet service',\n", + " 410: 'No',\n", + " 411: 'No internet service',\n", + " 412: 'No',\n", + " 413: 'No',\n", + " 414: 'No',\n", + " 415: 'No',\n", + " 416: 'Yes',\n", + " 417: 'No internet service',\n", + " 418: 'Yes',\n", + " 419: 'No',\n", + " 420: 'No internet service',\n", + " 421: 'No',\n", + " 422: 'Yes',\n", + " 423: 'No internet service',\n", + " 424: 'No',\n", + " 425: 'No',\n", + " 426: 'Yes',\n", + " 427: 'Yes',\n", + " 428: 'No',\n", + " 429: 'No',\n", + " 430: 'No internet service',\n", + " 431: 'Yes',\n", + " 432: 'No',\n", + " 433: 'No',\n", + " 434: 'No internet service',\n", + " 435: 'No',\n", + " 436: 'No internet service',\n", + " 437: 'Yes',\n", + " 438: 'Yes',\n", + " 439: 'No',\n", + " 440: 'Yes',\n", + " 441: 'Yes',\n", + " 442: 'Yes',\n", + " 443: 'Yes',\n", + " 444: 'Yes',\n", + " 445: 'Yes',\n", + " 446: 'No',\n", + " 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971: 'No',\n", + " 972: 'No',\n", + " 973: 'No',\n", + " 974: 'Yes',\n", + " 975: 'Yes',\n", + " 976: 'No',\n", + " 977: 'No',\n", + " 978: 'Yes',\n", + " 979: 'No',\n", + " 980: 'No internet service',\n", + " 981: 'No internet service',\n", + " 982: 'No',\n", + " 983: 'Yes',\n", + " 984: 'No internet service',\n", + " 985: 'No',\n", + " 986: 'No',\n", + " 987: 'No internet service',\n", + " 988: 'No internet service',\n", + " 989: 'No internet service',\n", + " 990: 'No',\n", + " 991: 'No internet service',\n", + " 992: 'No internet service',\n", + " 993: 'No internet service',\n", + " 994: 'No internet service',\n", + " 995: 'No',\n", + " 996: 'No',\n", + " 997: 'Yes',\n", + " 998: 'No internet service',\n", + " 999: 'Yes',\n", + " ...},\n", + " 'Contract': {0: 'Month-to-month',\n", + " 1: 'One year',\n", + " 2: 'Month-to-month',\n", + " 3: 'One year',\n", + " 4: 'Month-to-month',\n", + " 5: 'Month-to-month',\n", + " 6: 'Month-to-month',\n", + " 7: 'Month-to-month',\n", + " 8: 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" 467: 'One year',\n", + " 468: 'Two year',\n", + " 469: 'Month-to-month',\n", + " 470: 'One year',\n", + " 471: 'Month-to-month',\n", + " 472: 'Month-to-month',\n", + " 473: 'Month-to-month',\n", + " 474: 'Month-to-month',\n", + " 475: 'Month-to-month',\n", + " 476: 'Month-to-month',\n", + " 477: 'Month-to-month',\n", + " 478: 'Month-to-month',\n", + " 479: 'Month-to-month',\n", + " 480: 'Two year',\n", + " 481: 'Month-to-month',\n", + " 482: 'Month-to-month',\n", + " 483: 'One year',\n", + " 484: 'Month-to-month',\n", + " 485: 'Two year',\n", + " 486: 'Month-to-month',\n", + " 487: 'Two year',\n", + " 488: 'Two year',\n", + " 489: 'Month-to-month',\n", + " 490: 'Two year',\n", + " 491: 'Month-to-month',\n", + " 492: 'Month-to-month',\n", + " 493: 'One year',\n", + " 494: 'Month-to-month',\n", + " 495: 'One year',\n", + " 496: 'Month-to-month',\n", + " 497: 'Two year',\n", + " 498: 'Month-to-month',\n", + " 499: 'Month-to-month',\n", + " 500: 'One year',\n", + " 501: 'Two year',\n", + " 502: 'One year',\n", + " 503: 'Month-to-month',\n", + " 504: 'Two year',\n", + " 505: 'Month-to-month',\n", + " 506: 'Month-to-month',\n", + " 507: 'Month-to-month',\n", + " 508: 'Month-to-month',\n", + " 509: 'Month-to-month',\n", + " 510: 'One year',\n", + " 511: 'Month-to-month',\n", + " 512: 'Month-to-month',\n", + " 513: 'Month-to-month',\n", + " 514: 'Month-to-month',\n", + " 515: 'Month-to-month',\n", + " 516: 'One year',\n", + " 517: 'Month-to-month',\n", + " 518: 'Month-to-month',\n", + " 519: 'Month-to-month',\n", + " 520: 'One year',\n", + " 521: 'Month-to-month',\n", + " 522: 'Month-to-month',\n", + " 523: 'Month-to-month',\n", + " 524: 'Month-to-month',\n", + " 525: 'One year',\n", + " 526: 'Two year',\n", + " 527: 'One year',\n", + " 528: 'Two year',\n", + " 529: 'Two year',\n", + " 530: 'Two year',\n", + " 531: 'Two year',\n", + " 532: 'Month-to-month',\n", + " 533: 'One year',\n", + " 534: 'Month-to-month',\n", + " 535: 'Month-to-month',\n", + " 536: 'Month-to-month',\n", + " 537: 'One year',\n", + " 538: 'One year',\n", + " 539: 'Two year',\n", + " 540: 'Two year',\n", + " 541: 'Month-to-month',\n", + " 542: 'Month-to-month',\n", + " 543: 'Month-to-month',\n", + " 544: 'Month-to-month',\n", + " 545: 'One year',\n", + " 546: 'Month-to-month',\n", + " 547: 'One year',\n", + " 548: 'One year',\n", + " 549: 'Month-to-month',\n", + " 550: 'Two year',\n", + " 551: 'Two year',\n", + " 552: 'Two year',\n", + " 553: 'Two year',\n", + " 554: 'Month-to-month',\n", + " 555: 'One year',\n", + " 556: 'Month-to-month',\n", + " 557: 'One year',\n", + " 558: 'Month-to-month',\n", + " 559: 'Month-to-month',\n", + " 560: 'One year',\n", + " 561: 'Month-to-month',\n", + " 562: 'Month-to-month',\n", + " 563: 'Two year',\n", + " 564: 'Month-to-month',\n", + " 565: 'Two year',\n", + " 566: 'One year',\n", + " 567: 'Two year',\n", + " 568: 'Month-to-month',\n", + " 569: 'One year',\n", + " 570: 'Two year',\n", + " 571: 'Two year',\n", + " 572: 'One year',\n", + " 573: 'One year',\n", + " 574: 'Month-to-month',\n", + " 575: 'Two year',\n", + " 576: 'One year',\n", + " 577: 'Month-to-month',\n", + " 578: 'Month-to-month',\n", + " 579: 'Month-to-month',\n", + " 580: 'Month-to-month',\n", + " 581: 'Month-to-month',\n", + " 582: 'Month-to-month',\n", + " 583: 'Month-to-month',\n", + " 584: 'Month-to-month',\n", + " 585: 'Month-to-month',\n", + " 586: 'Two year',\n", + " 587: 'One year',\n", + " 588: 'Two year',\n", + " 589: 'Two year',\n", + " 590: 'Two year',\n", + " 591: 'One year',\n", + " 592: 'Two year',\n", + " 593: 'Month-to-month',\n", + " 594: 'Month-to-month',\n", + " 595: 'Month-to-month',\n", + " 596: 'Two year',\n", + " 597: 'Month-to-month',\n", + " 598: 'One year',\n", + " 599: 'Two year',\n", + " 600: 'Month-to-month',\n", + " 601: 'One year',\n", + " 602: 'One year',\n", + " 603: 'Two year',\n", + " 604: 'One year',\n", + " 605: 'Two year',\n", + " 606: 'Two year',\n", + " 607: 'One year',\n", + " 608: 'Two year',\n", + " 609: 'Month-to-month',\n", + " 610: 'Month-to-month',\n", + " 611: 'Month-to-month',\n", + " 612: 'Two year',\n", + " 613: 'One year',\n", + " 614: 'Two year',\n", + " 615: 'Month-to-month',\n", + " 616: 'Month-to-month',\n", + " 617: 'One year',\n", + " 618: 'Month-to-month',\n", + " 619: 'Month-to-month',\n", + " 620: 'Month-to-month',\n", + " 621: 'One year',\n", + " 622: 'One year',\n", + " 623: 'Two year',\n", + " 624: 'Month-to-month',\n", + " 625: 'Month-to-month',\n", + " 626: 'Month-to-month',\n", + " 627: 'One year',\n", + " 628: 'Two year',\n", + " 629: 'Month-to-month',\n", + " 630: 'Month-to-month',\n", + " 631: 'Month-to-month',\n", + " 632: 'Two year',\n", + " 633: 'One year',\n", + " 634: 'One year',\n", + " 635: 'Month-to-month',\n", + " 636: 'Two year',\n", + " 637: 'Two year',\n", + " 638: 'One year',\n", + " 639: 'Month-to-month',\n", + " 640: 'Two year',\n", + " 641: 'Month-to-month',\n", + " 642: 'Month-to-month',\n", + " 643: 'One year',\n", + " 644: 'Month-to-month',\n", + " 645: 'Month-to-month',\n", + " 646: 'Month-to-month',\n", + " 647: 'One year',\n", + " 648: 'Month-to-month',\n", + " 649: 'Month-to-month',\n", + " 650: 'Month-to-month',\n", + " 651: 'Month-to-month',\n", + " 652: 'Two year',\n", + " 653: 'Month-to-month',\n", + " 654: 'Month-to-month',\n", + " 655: 'Month-to-month',\n", + " 656: 'Two year',\n", + " 657: 'One year',\n", + " 658: 'One year',\n", + " 659: 'Month-to-month',\n", + " 660: 'Month-to-month',\n", + " 661: 'Month-to-month',\n", + " 662: 'Month-to-month',\n", + " 663: 'Two year',\n", + " 664: 'Two year',\n", + " 665: 'Month-to-month',\n", + " 666: 'Month-to-month',\n", + " 667: 'Month-to-month',\n", + " 668: 'Month-to-month',\n", + " 669: 'Two year',\n", + " 670: 'One year',\n", + " 671: 'Two year',\n", + " 672: 'Month-to-month',\n", + " 673: 'Month-to-month',\n", + " 674: 'Month-to-month',\n", + " 675: 'Two year',\n", + " 676: 'One year',\n", + " 677: 'Month-to-month',\n", + " 678: 'Month-to-month',\n", + " 679: 'Month-to-month',\n", + " 680: 'Month-to-month',\n", + " 681: 'Two year',\n", + " 682: 'Month-to-month',\n", + " 683: 'One year',\n", + " 684: 'Month-to-month',\n", + " 685: 'Month-to-month',\n", + " 686: 'One year',\n", + " 687: 'Month-to-month',\n", + " 688: 'One year',\n", + " 689: 'Month-to-month',\n", + " 690: 'Month-to-month',\n", + " 691: 'One year',\n", + " 692: 'One year',\n", + " 693: 'Month-to-month',\n", + " 694: 'One year',\n", + " 695: 'Month-to-month',\n", + " 696: 'Two year',\n", + " 697: 'Month-to-month',\n", + " 698: 'One year',\n", + " 699: 'Two year',\n", + " 700: 'Two year',\n", + " 701: 'Month-to-month',\n", + " 702: 'Month-to-month',\n", + " 703: 'Month-to-month',\n", + " 704: 'Month-to-month',\n", + " 705: 'Month-to-month',\n", + " 706: 'Two year',\n", + " 707: 'One year',\n", + " 708: 'Month-to-month',\n", + " 709: 'Month-to-month',\n", + " 710: 'Month-to-month',\n", + " 711: 'Month-to-month',\n", + " 712: 'Two year',\n", + " 713: 'Month-to-month',\n", + " 714: 'Two year',\n", + " 715: 'Month-to-month',\n", + " 716: 'Two year',\n", + " 717: 'Month-to-month',\n", + " 718: 'Month-to-month',\n", + " 719: 'Month-to-month',\n", + " 720: 'One year',\n", + " 721: 'Two year',\n", + " 722: 'Month-to-month',\n", + " 723: 'Month-to-month',\n", + " 724: 'Month-to-month',\n", + " 725: 'Month-to-month',\n", + " 726: 'Two year',\n", + " 727: 'Month-to-month',\n", + " 728: 'Month-to-month',\n", + " 729: 'Two year',\n", + " 730: 'Month-to-month',\n", + " 731: 'Month-to-month',\n", + " 732: 'Month-to-month',\n", + " 733: 'Two year',\n", + " 734: 'Month-to-month',\n", + " 735: 'Month-to-month',\n", + " 736: 'One year',\n", + " 737: 'Two year',\n", + " 738: 'Month-to-month',\n", + " 739: 'Two year',\n", + " 740: 'Two year',\n", + " 741: 'Month-to-month',\n", + " 742: 'Month-to-month',\n", + " 743: 'Two year',\n", + " 744: 'Month-to-month',\n", + " 745: 'Two year',\n", + " 746: 'Two year',\n", + " 747: 'Month-to-month',\n", + " 748: 'Two year',\n", + " 749: 'One year',\n", + " 750: 'Month-to-month',\n", + " 751: 'Two year',\n", + " 752: 'Month-to-month',\n", + " 753: 'Two year',\n", + " 754: 'Month-to-month',\n", + " 755: 'Month-to-month',\n", + " 756: 'Two year',\n", + " 757: 'Month-to-month',\n", + " 758: 'Month-to-month',\n", + " 759: 'Two year',\n", + " 760: 'Month-to-month',\n", + " 761: 'Month-to-month',\n", + " 762: 'Month-to-month',\n", + " 763: 'Two year',\n", + " 764: 'Two year',\n", + " 765: 'Month-to-month',\n", + " 766: 'Month-to-month',\n", + " 767: 'One year',\n", + " 768: 'Month-to-month',\n", + " 769: 'One year',\n", + " 770: 'Month-to-month',\n", + " 771: 'Month-to-month',\n", + " 772: 'Month-to-month',\n", + " 773: 'Month-to-month',\n", + " 774: 'One year',\n", + " 775: 'Two year',\n", + " 776: 'Month-to-month',\n", + " 777: 'Month-to-month',\n", + " 778: 'One year',\n", + " 779: 'Month-to-month',\n", + " 780: 'Two year',\n", + " 781: 'Month-to-month',\n", + " 782: 'Month-to-month',\n", + " 783: 'Month-to-month',\n", + " 784: 'One year',\n", + " 785: 'One year',\n", + " 786: 'Two year',\n", + " 787: 'Month-to-month',\n", + " 788: 'One year',\n", + " 789: 'Two year',\n", + " 790: 'Two year',\n", + " 791: 'One year',\n", + " 792: 'Two year',\n", + " 793: 'Two year',\n", + " 794: 'Month-to-month',\n", + " 795: 'Month-to-month',\n", + " 796: 'One year',\n", + " 797: 'Month-to-month',\n", + " 798: 'Month-to-month',\n", + " 799: 'One year',\n", + " 800: 'Two year',\n", + " 801: 'Month-to-month',\n", + " 802: 'Two year',\n", + " 803: 'Two year',\n", + " 804: 'Month-to-month',\n", + " 805: 'Two year',\n", + " 806: 'Month-to-month',\n", + " 807: 'Two year',\n", + " 808: 'Month-to-month',\n", + " 809: 'Month-to-month',\n", + " 810: 'One year',\n", + " 811: 'Two year',\n", + " 812: 'Two year',\n", + " 813: 'One year',\n", + " 814: 'Month-to-month',\n", + " 815: 'Month-to-month',\n", + " 816: 'Month-to-month',\n", + " 817: 'One year',\n", + " 818: 'Month-to-month',\n", + " 819: 'Two year',\n", + " 820: 'Month-to-month',\n", + " 821: 'Two year',\n", + " 822: 'One year',\n", + " 823: 'Month-to-month',\n", + " 824: 'Two year',\n", + " 825: 'Month-to-month',\n", + " 826: 'Month-to-month',\n", + " 827: 'Month-to-month',\n", + " 828: 'One year',\n", + " 829: 'Month-to-month',\n", + " 830: 'One year',\n", + " 831: 'Two year',\n", + " 832: 'One year',\n", + " 833: 'Month-to-month',\n", + " 834: 'Month-to-month',\n", + " 835: 'Month-to-month',\n", + " 836: 'One year',\n", + " 837: 'Month-to-month',\n", + " 838: 'Month-to-month',\n", + " 839: 'Two year',\n", + " 840: 'One year',\n", + " 841: 'Two year',\n", + " 842: 'One year',\n", + " 843: 'Month-to-month',\n", + " 844: 'Month-to-month',\n", + " 845: 'One year',\n", + " 846: 'Two year',\n", + " 847: 'One year',\n", + " 848: 'Two year',\n", + " 849: 'Two year',\n", + " 850: 'Two year',\n", + " 851: 'Two year',\n", + " 852: 'Month-to-month',\n", + " 853: 'Month-to-month',\n", + " 854: 'Two year',\n", + " 855: 'One year',\n", + " 856: 'Two year',\n", + " 857: 'Month-to-month',\n", + " 858: 'One year',\n", + " 859: 'Two year',\n", + " 860: 'One year',\n", + " 861: 'Two year',\n", + " 862: 'Month-to-month',\n", + " 863: 'Month-to-month',\n", + " 864: 'Month-to-month',\n", + " 865: 'Month-to-month',\n", + " 866: 'Two year',\n", + " 867: 'Month-to-month',\n", + " 868: 'One year',\n", + " 869: 'Two year',\n", + " 870: 'Two year',\n", + " 871: 'One year',\n", + " 872: 'Two year',\n", + " 873: 'Month-to-month',\n", + " 874: 'Two year',\n", + " 875: 'Month-to-month',\n", + " 876: 'Month-to-month',\n", + " 877: 'One year',\n", + " 878: 'One year',\n", + " 879: 'Month-to-month',\n", + " 880: 'Month-to-month',\n", + " 881: 'One year',\n", + " 882: 'Month-to-month',\n", + " 883: 'One year',\n", + " 884: 'One year',\n", + " 885: 'Month-to-month',\n", + " 886: 'Month-to-month',\n", + " 887: 'One year',\n", + " 888: 'One year',\n", + " 889: 'Month-to-month',\n", + " 890: 'Month-to-month',\n", + " 891: 'Month-to-month',\n", + " 892: 'Month-to-month',\n", + " 893: 'Month-to-month',\n", + " 894: 'One year',\n", + " 895: 'Month-to-month',\n", + " 896: 'Two year',\n", + " 897: 'Two year',\n", + " 898: 'Month-to-month',\n", + " 899: 'Month-to-month',\n", + " 900: 'Month-to-month',\n", + " 901: 'Month-to-month',\n", + " 902: 'Month-to-month',\n", + " 903: 'One year',\n", + " 904: 'Two year',\n", + " 905: 'Month-to-month',\n", + " 906: 'Month-to-month',\n", + " 907: 'One year',\n", + " 908: 'Month-to-month',\n", + " 909: 'Two year',\n", + " 910: 'Month-to-month',\n", + " 911: 'Month-to-month',\n", + " 912: 'One year',\n", + " 913: 'One year',\n", + " 914: 'Two year',\n", + " 915: 'Month-to-month',\n", + " 916: 'Month-to-month',\n", + " 917: 'Two year',\n", + " 918: 'Month-to-month',\n", + " 919: 'One year',\n", + " 920: 'One year',\n", + " 921: 'Month-to-month',\n", + " 922: 'Month-to-month',\n", + " 923: 'One year',\n", + " 924: 'Month-to-month',\n", + " 925: 'Month-to-month',\n", + " 926: 'Month-to-month',\n", + " 927: 'Month-to-month',\n", + " 928: 'Two year',\n", + " 929: 'One year',\n", + " 930: 'Month-to-month',\n", + " 931: 'Two year',\n", + " 932: 'Two year',\n", + " 933: 'Month-to-month',\n", + " 934: 'One year',\n", + " 935: 'Month-to-month',\n", + " 936: 'Two year',\n", + " 937: 'Month-to-month',\n", + " 938: 'One year',\n", + " 939: 'Month-to-month',\n", + " 940: 'One year',\n", + " 941: 'Month-to-month',\n", + " 942: 'Month-to-month',\n", + " 943: 'Two year',\n", + " 944: 'Month-to-month',\n", + " 945: 'Month-to-month',\n", + " 946: 'Month-to-month',\n", + " 947: 'Month-to-month',\n", + " 948: 'Month-to-month',\n", + " 949: 'Two year',\n", + " 950: 'Month-to-month',\n", + " 951: 'Month-to-month',\n", + " 952: 'Month-to-month',\n", + " 953: 'Month-to-month',\n", + " 954: 'Month-to-month',\n", + " 955: 'Month-to-month',\n", + " 956: 'Two year',\n", + " 957: 'Month-to-month',\n", + " 958: 'Month-to-month',\n", + " 959: 'Month-to-month',\n", + " 960: 'Month-to-month',\n", + " 961: 'One year',\n", + " 962: 'Month-to-month',\n", + " 963: 'Two year',\n", + " 964: 'Month-to-month',\n", + " 965: 'Month-to-month',\n", + " 966: 'Month-to-month',\n", + " 967: 'Month-to-month',\n", + " 968: 'Two year',\n", + " 969: 'Month-to-month',\n", + " 970: 'Month-to-month',\n", + " 971: 'Month-to-month',\n", + " 972: 'Two year',\n", + " 973: 'Month-to-month',\n", + " 974: 'Two year',\n", + " 975: 'Two year',\n", + " 976: 'Month-to-month',\n", + " 977: 'Month-to-month',\n", + " 978: 'One year',\n", + " 979: 'One year',\n", + " 980: 'Month-to-month',\n", + " 981: 'Two year',\n", + " 982: 'One year',\n", + " 983: 'Month-to-month',\n", + " 984: 'Two year',\n", + " 985: 'Month-to-month',\n", + " 986: 'Month-to-month',\n", + " 987: 'Month-to-month',\n", + " 988: 'Month-to-month',\n", + " 989: 'One year',\n", + " 990: 'Two year',\n", + " 991: 'Two year',\n", + " 992: 'Two year',\n", + " 993: 'Two year',\n", + " 994: 'Two year',\n", + " 995: 'Month-to-month',\n", + " 996: 'Month-to-month',\n", + " 997: 'Month-to-month',\n", + " 998: 'Two year',\n", + " 999: 'Month-to-month',\n", + " ...},\n", + " 'PaperlessBilling': {0: 'Yes',\n", + " 1: 'No',\n", + " 2: 'Yes',\n", + " 3: 'No',\n", + " 4: 'Yes',\n", + " 5: 'Yes',\n", + " 6: 'Yes',\n", + " 7: 'No',\n", + " 8: 'Yes',\n", + " 9: 'No',\n", + " 10: 'Yes',\n", + " 11: 'No',\n", + " 12: 'No',\n", + " 13: 'Yes',\n", + " 14: 'Yes',\n", + " 15: 'No',\n", + " 16: 'No',\n", + " 17: 'No',\n", + " 18: 'No',\n", + " 19: 'Yes',\n", + " 20: 'Yes',\n", + " 21: 'No',\n", + " 22: 'No',\n", + " 23: 'Yes',\n", + " 24: 'No',\n", + " 25: 'Yes',\n", + " 26: 'Yes',\n", + " 27: 'No',\n", + " 28: 'Yes',\n", + " 29: 'Yes',\n", + " 30: 'Yes',\n", + " 31: 'Yes',\n", + " 32: 'No',\n", + " 33: 'No',\n", + " 34: 'No',\n", + " 35: 'No',\n", + " 36: 'Yes',\n", + " 37: 'Yes',\n", + " 38: 'Yes',\n", + " 39: 'Yes',\n", + " 40: 'No',\n", + " 41: 'Yes',\n", + " 42: 'No',\n", + " 43: 'Yes',\n", + " 44: 'Yes',\n", 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913: 'No',\n", + " 914: 'Yes',\n", + " 915: 'Yes',\n", + " 916: 'Yes',\n", + " 917: 'Yes',\n", + " 918: 'Yes',\n", + " 919: 'Yes',\n", + " 920: 'No',\n", + " 921: 'No',\n", + " 922: 'Yes',\n", + " 923: 'Yes',\n", + " 924: 'No',\n", + " 925: 'No',\n", + " 926: 'Yes',\n", + " 927: 'Yes',\n", + " 928: 'Yes',\n", + " 929: 'No',\n", + " 930: 'No',\n", + " 931: 'No',\n", + " 932: 'No',\n", + " 933: 'Yes',\n", + " 934: 'Yes',\n", + " 935: 'Yes',\n", + " 936: 'No',\n", + " 937: 'Yes',\n", + " 938: 'Yes',\n", + " 939: 'No',\n", + " 940: 'No',\n", + " 941: 'No',\n", + " 942: 'Yes',\n", + " 943: 'No',\n", + " 944: 'Yes',\n", + " 945: 'No',\n", + " 946: 'Yes',\n", + " 947: 'Yes',\n", + " 948: 'Yes',\n", + " 949: 'No',\n", + " 950: 'No',\n", + " 951: 'No',\n", + " 952: 'No',\n", + " 953: 'No',\n", + " 954: 'Yes',\n", + " 955: 'No',\n", + " 956: 'No',\n", + " 957: 'No',\n", + " 958: 'No',\n", + " 959: 'Yes',\n", + " 960: 'No',\n", + " 961: 'Yes',\n", + " 962: 'No',\n", + " 963: 'No',\n", + " 964: 'Yes',\n", + " 965: 'No',\n", + " 966: 'No',\n", + " 967: 'No',\n", + " 968: 'No',\n", + " 969: 'Yes',\n", + " 970: 'No',\n", + " 971: 'Yes',\n", + " 972: 'No',\n", + " 973: 'Yes',\n", + " 974: 'Yes',\n", + " 975: 'Yes',\n", + " 976: 'Yes',\n", + " 977: 'No',\n", + " 978: 'Yes',\n", + " 979: 'Yes',\n", + " 980: 'Yes',\n", + " 981: 'No',\n", + " 982: 'Yes',\n", + " 983: 'Yes',\n", + " 984: 'No',\n", + " 985: 'Yes',\n", + " 986: 'No',\n", + " 987: 'No',\n", + " 988: 'No',\n", + " 989: 'No',\n", + " 990: 'No',\n", + " 991: 'No',\n", + " 992: 'No',\n", + " 993: 'No',\n", + " 994: 'No',\n", + " 995: 'No',\n", + " 996: 'Yes',\n", + " 997: 'Yes',\n", + " 998: 'No',\n", + " 999: 'No',\n", + " ...},\n", + " 'PaymentMethod': {0: 'Electronic check',\n", + " 1: 'Mailed check',\n", + " 2: 'Mailed check',\n", + " 3: 'Bank transfer (automatic)',\n", + " 4: 'Electronic check',\n", + " 5: 'Electronic check',\n", + " 6: 'Credit card (automatic)',\n", + " 7: 'Mailed check',\n", + " 8: 'Electronic check',\n", + " 9: 'Bank transfer (automatic)',\n", + " 10: 'Mailed check',\n", + " 11: 'Credit card (automatic)',\n", + " 12: 'Credit card (automatic)',\n", + " 13: 'Bank transfer (automatic)',\n", + " 14: 'Electronic check',\n", + " 15: 'Credit card (automatic)',\n", + " 16: 'Mailed check',\n", + " 17: 'Bank transfer (automatic)',\n", + " 18: 'Credit card (automatic)',\n", + " 19: 'Electronic check',\n", + " 20: 'Electronic check',\n", + " 21: 'Bank transfer (automatic)',\n", + " 22: 'Mailed check',\n", + " 23: 'Credit card (automatic)',\n", + " 24: 'Credit card (automatic)',\n", + " 25: 'Bank transfer (automatic)',\n", + " 26: 'Electronic check',\n", + " 27: 'Electronic check',\n", + " 28: 'Credit card (automatic)',\n", + " 29: 'Mailed check',\n", + " 30: 'Credit card (automatic)',\n", + " 31: 'Credit card (automatic)',\n", + " 32: 'Mailed check',\n", + " 33: 'Bank transfer (automatic)',\n", + " 34: 'Bank transfer (automatic)',\n", + " 35: 'Bank transfer (automatic)',\n", + " 36: 'Electronic check',\n", + " 37: 'Credit card (automatic)',\n", + " 38: 'Electronic check',\n", + " 39: 'Bank transfer (automatic)',\n", + " 40: 'Mailed check',\n", + " 41: 'Credit card (automatic)',\n", + " 42: 'Mailed check',\n", + " 43: 'Credit card (automatic)',\n", + " 44: 'Electronic check',\n", + " 45: 'Electronic check',\n", + " 46: 'Mailed check',\n", + " 47: 'Electronic check',\n", + " 48: 'Credit card (automatic)',\n", + " 49: 'Credit card (automatic)',\n", + " 50: 'Electronic check',\n", + " 51: 'Credit card (automatic)',\n", + " 52: 'Credit card (automatic)',\n", + " 53: 'Credit card (automatic)',\n", + " 54: 'Credit card (automatic)',\n", + " 55: 'Electronic check',\n", + " 56: 'Credit card (automatic)',\n", + " 57: 'Electronic check',\n", + " 58: 'Credit card (automatic)',\n", + " 59: 'Credit card (automatic)',\n", + " 60: 'Electronic check',\n", + " 61: 'Bank transfer (automatic)',\n", + " 62: 'Bank transfer (automatic)',\n", + " 63: 'Credit card (automatic)',\n", + " 64: 'Electronic check',\n", + " 65: 'Electronic check',\n", + " 66: 'Electronic check',\n", + " 67: 'Mailed check',\n", + " 68: 'Bank transfer (automatic)',\n", + " 69: 'Mailed check',\n", + " 70: 'Mailed check',\n", + " 71: 'Electronic check',\n", + " 72: 'Bank transfer (automatic)',\n", + " 73: 'Bank transfer (automatic)',\n", + " 74: 'Bank transfer (automatic)',\n", + " 75: 'Electronic check',\n", + " 76: 'Credit card (automatic)',\n", + " 77: 'Mailed check',\n", + " 78: 'Electronic check',\n", + " 79: 'Credit card (automatic)',\n", + " 80: 'Electronic check',\n", + " 81: 'Electronic check',\n", + " 82: 'Bank transfer (automatic)',\n", + " 83: 'Bank transfer (automatic)',\n", + " 84: 'Bank transfer (automatic)',\n", + " 85: 'Credit card (automatic)',\n", + " 86: 'Bank transfer (automatic)',\n", + " 87: 'Bank transfer (automatic)',\n", + " 88: 'Mailed check',\n", + " 89: 'Mailed check',\n", + " 90: 'Bank transfer (automatic)',\n", + " 91: 'Electronic check',\n", + " 92: 'Mailed check',\n", + " 93: 'Credit card (automatic)',\n", + " 94: 'Bank transfer (automatic)',\n", + " 95: 'Electronic check',\n", + " 96: 'Credit card (automatic)',\n", + " 97: 'Mailed check',\n", + " 98: 'Bank transfer (automatic)',\n", + " 99: 'Electronic check',\n", + " 100: 'Mailed check',\n", + " 101: 'Electronic check',\n", + " 102: 'Bank transfer (automatic)',\n", + " 103: 'Bank transfer (automatic)',\n", + " 104: 'Bank transfer (automatic)',\n", + " 105: 'Mailed check',\n", + " 106: 'Electronic check',\n", + " 107: 'Mailed check',\n", + " 108: 'Mailed check',\n", + " 109: 'Credit card (automatic)',\n", + " 110: 'Electronic check',\n", + " 111: 'Credit card (automatic)',\n", + " 112: 'Electronic check',\n", + " 113: 'Electronic check',\n", + " 114: 'Electronic check',\n", + " 115: 'Electronic check',\n", + " 116: 'Bank transfer (automatic)',\n", + " 117: 'Electronic check',\n", + " 118: 'Mailed check',\n", + " 119: 'Credit card (automatic)',\n", + " 120: 'Credit card (automatic)',\n", 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check',\n", + " 150: 'Electronic check',\n", + " 151: 'Mailed check',\n", + " 152: 'Credit card (automatic)',\n", + " 153: 'Electronic check',\n", + " 154: 'Mailed check',\n", + " 155: 'Bank transfer (automatic)',\n", + " 156: 'Bank transfer (automatic)',\n", + " 157: 'Mailed check',\n", + " 158: 'Mailed check',\n", + " 159: 'Bank transfer (automatic)',\n", + " 160: 'Electronic check',\n", + " 161: 'Electronic check',\n", + " 162: 'Electronic check',\n", + " 163: 'Bank transfer (automatic)',\n", + " 164: 'Mailed check',\n", + " 165: 'Mailed check',\n", + " 166: 'Bank transfer (automatic)',\n", + " 167: 'Bank transfer (automatic)',\n", + " 168: 'Electronic check',\n", + " 169: 'Electronic check',\n", + " 170: 'Bank transfer (automatic)',\n", + " 171: 'Electronic check',\n", + " 172: 'Mailed check',\n", + " 173: 'Bank transfer (automatic)',\n", + " 174: 'Bank transfer (automatic)',\n", + " 175: 'Electronic check',\n", + " 176: 'Mailed check',\n", + " 177: 'Bank transfer (automatic)',\n", + " 178: 'Bank transfer (automatic)',\n", + " 179: 'Credit card (automatic)',\n", + " 180: 'Bank transfer (automatic)',\n", + " 181: 'Mailed check',\n", + " 182: 'Bank transfer (automatic)',\n", + " 183: 'Credit card (automatic)',\n", + " 184: 'Electronic check',\n", + " 185: 'Electronic check',\n", + " 186: 'Mailed check',\n", + " 187: 'Electronic check',\n", + " 188: 'Electronic check',\n", + " 189: 'Credit card (automatic)',\n", + " 190: 'Credit card (automatic)',\n", + " 191: 'Electronic check',\n", + " 192: 'Bank transfer (automatic)',\n", + " 193: 'Credit card (automatic)',\n", + " 194: 'Credit card (automatic)',\n", + " 195: 'Electronic check',\n", + " 196: 'Bank transfer (automatic)',\n", + " 197: 'Electronic check',\n", + " 198: 'Bank transfer (automatic)',\n", + " 199: 'Mailed check',\n", + " 200: 'Credit card (automatic)',\n", + " 201: 'Credit card (automatic)',\n", + " 202: 'Electronic check',\n", + " 203: 'Credit card (automatic)',\n", + " 204: 'Credit card (automatic)',\n", + " 205: 'Bank transfer (automatic)',\n", + " 206: 'Credit card (automatic)',\n", + " 207: 'Electronic check',\n", + " 208: 'Electronic check',\n", + " 209: 'Credit card (automatic)',\n", + " 210: 'Credit card (automatic)',\n", + " 211: 'Electronic check',\n", + " 212: 'Mailed check',\n", + " 213: 'Electronic check',\n", + " 214: 'Electronic check',\n", + " 215: 'Bank transfer (automatic)',\n", + " 216: 'Credit card (automatic)',\n", + " 217: 'Bank transfer (automatic)',\n", + " 218: 'Bank transfer (automatic)',\n", + " 219: 'Bank transfer (automatic)',\n", + " 220: 'Electronic check',\n", + " 221: 'Bank transfer (automatic)',\n", + " 222: 'Electronic check',\n", + " 223: 'Mailed check',\n", + " 224: 'Bank transfer (automatic)',\n", + " 225: 'Mailed check',\n", + " 226: 'Mailed check',\n", + " 227: 'Mailed check',\n", + " 228: 'Credit card (automatic)',\n", + " 229: 'Credit card (automatic)',\n", + " 230: 'Credit card (automatic)',\n", + " 231: 'Credit card (automatic)',\n", + " 232: 'Mailed check',\n", + " 233: 'Credit card (automatic)',\n", + " 234: 'Electronic check',\n", + " 235: 'Mailed check',\n", + " 236: 'Mailed check',\n", + " 237: 'Electronic check',\n", + " 238: 'Electronic check',\n", + " 239: 'Mailed check',\n", + " 240: 'Bank transfer (automatic)',\n", + " 241: 'Bank transfer (automatic)',\n", + " 242: 'Credit card (automatic)',\n", + " 243: 'Electronic check',\n", + " 244: 'Electronic check',\n", + " 245: 'Electronic check',\n", + " 246: 'Mailed check',\n", + " 247: 'Electronic check',\n", + " 248: 'Mailed check',\n", + " 249: 'Electronic check',\n", + " 250: 'Electronic check',\n", + " 251: 'Electronic check',\n", + " 252: 'Electronic check',\n", + " 253: 'Credit card (automatic)',\n", + " 254: 'Mailed check',\n", + " 255: 'Electronic check',\n", + " 256: 'Credit card (automatic)',\n", + " 257: 'Credit card (automatic)',\n", + " 258: 'Credit card (automatic)',\n", + " 259: 'Electronic check',\n", + " 260: 'Electronic check',\n", + " 261: 'Electronic check',\n", + " 262: 'Electronic check',\n", + " 263: 'Bank transfer (automatic)',\n", + " 264: 'Mailed check',\n", + " 265: 'Mailed check',\n", + " 266: 'Bank transfer (automatic)',\n", + " 267: 'Mailed check',\n", + " 268: 'Mailed check',\n", + " 269: 'Credit card (automatic)',\n", + " 270: 'Electronic check',\n", + " 271: 'Electronic check',\n", + " 272: 'Bank transfer (automatic)',\n", + " 273: 'Electronic check',\n", + " 274: 'Mailed check',\n", + " 275: 'Electronic check',\n", + " 276: 'Electronic check',\n", + " 277: 'Bank transfer (automatic)',\n", + " 278: 'Electronic check',\n", + " 279: 'Bank transfer (automatic)',\n", + " 280: 'Mailed check',\n", + " 281: 'Electronic check',\n", + " 282: 'Mailed check',\n", + " 283: 'Mailed check',\n", + " 284: 'Mailed check',\n", + " 285: 'Credit card (automatic)',\n", + " 286: 'Electronic check',\n", + " 287: 'Electronic check',\n", + " 288: 'Electronic check',\n", + " 289: 'Credit card (automatic)',\n", + " 290: 'Electronic check',\n", + " 291: 'Mailed check',\n", + " 292: 'Mailed check',\n", + " 293: 'Electronic check',\n", + " 294: 'Mailed check',\n", + " 295: 'Mailed check',\n", + " 296: 'Electronic check',\n", + " 297: 'Electronic check',\n", + " 298: 'Electronic check',\n", + " 299: 'Mailed check',\n", + " 300: 'Bank transfer (automatic)',\n", + " 301: 'Electronic check',\n", + " 302: 'Electronic check',\n", + " 303: 'Credit card (automatic)',\n", + " 304: 'Mailed check',\n", + " 305: 'Mailed check',\n", + " 306: 'Electronic check',\n", + " 307: 'Mailed check',\n", + " 308: 'Electronic check',\n", + " 309: 'Credit card (automatic)',\n", + " 310: 'Bank transfer (automatic)',\n", + " 311: 'Electronic check',\n", + " 312: 'Credit card (automatic)',\n", + " 313: 'Electronic check',\n", + " 314: 'Electronic check',\n", + " 315: 'Credit card (automatic)',\n", + " 316: 'Credit card (automatic)',\n", + " 317: 'Credit card (automatic)',\n", + " 318: 'Electronic check',\n", + " 319: 'Credit card (automatic)',\n", + " 320: 'Bank transfer (automatic)',\n", + " 321: 'Electronic check',\n", + " 322: 'Electronic check',\n", + " 323: 'Mailed check',\n", + " 324: 'Bank transfer (automatic)',\n", + " 325: 'Credit card (automatic)',\n", + " 326: 'Electronic check',\n", + " 327: 'Electronic check',\n", + " 328: 'Electronic check',\n", + " 329: 'Credit card (automatic)',\n", + " 330: 'Mailed check',\n", + " 331: 'Mailed check',\n", + " 332: 'Credit card (automatic)',\n", + " 333: 'Electronic check',\n", + " 334: 'Mailed check',\n", + " 335: 'Bank transfer (automatic)',\n", + " 336: 'Electronic check',\n", + " 337: 'Electronic check',\n", + " 338: 'Credit card (automatic)',\n", + " 339: 'Electronic check',\n", + " 340: 'Bank transfer (automatic)',\n", + " 341: 'Credit card (automatic)',\n", + " 342: 'Bank transfer (automatic)',\n", + " 343: 'Mailed check',\n", + " 344: 'Credit card (automatic)',\n", + " 345: 'Electronic check',\n", + " 346: 'Electronic check',\n", + " 347: 'Bank transfer (automatic)',\n", + " 348: 'Electronic check',\n", + " 349: 'Bank transfer (automatic)',\n", + " 350: 'Credit card (automatic)',\n", + " 351: 'Mailed check',\n", + " 352: 'Electronic check',\n", + " 353: 'Bank transfer (automatic)',\n", + " 354: 'Electronic check',\n", + " 355: 'Bank transfer (automatic)',\n", + " 356: 'Electronic check',\n", + " 357: 'Electronic check',\n", + " 358: 'Credit card (automatic)',\n", + " 359: 'Electronic check',\n", + " 360: 'Mailed check',\n", + " 361: 'Bank transfer (automatic)',\n", + " 362: 'Credit card (automatic)',\n", + " 363: 'Mailed check',\n", + " 364: 'Bank transfer (automatic)',\n", + " 365: 'Bank transfer (automatic)',\n", + " 366: 'Bank transfer (automatic)',\n", + " 367: 'Mailed check',\n", + " 368: 'Credit card (automatic)',\n", + " 369: 'Credit card (automatic)',\n", + " 370: 'Mailed check',\n", + " 371: 'Electronic check',\n", + " 372: 'Mailed check',\n", + " 373: 'Credit card (automatic)',\n", + " 374: 'Credit card (automatic)',\n", + " 375: 'Credit card (automatic)',\n", + " 376: 'Mailed check',\n", + " 377: 'Bank transfer (automatic)',\n", + " 378: 'Electronic check',\n", + " 379: 'Electronic check',\n", + " 380: 'Bank transfer (automatic)',\n", + " 381: 'Mailed check',\n", + " 382: 'Bank transfer (automatic)',\n", + " 383: 'Credit card (automatic)',\n", + " 384: 'Bank transfer (automatic)',\n", + " 385: 'Mailed check',\n", + " 386: 'Electronic check',\n", + " 387: 'Electronic check',\n", + " 388: 'Mailed check',\n", + " 389: 'Electronic check',\n", + " 390: 'Bank transfer (automatic)',\n", + " 391: 'Bank transfer (automatic)',\n", + " 392: 'Electronic check',\n", + " 393: 'Mailed check',\n", + " 394: 'Electronic check',\n", + " 395: 'Credit card (automatic)',\n", + " 396: 'Bank transfer (automatic)',\n", + " 397: 'Credit card (automatic)',\n", + " 398: 'Electronic check',\n", + " 399: 'Mailed check',\n", + " 400: 'Bank transfer (automatic)',\n", + " 401: 'Credit card (automatic)',\n", + " 402: 'Credit card (automatic)',\n", + " 403: 'Bank transfer (automatic)',\n", + " 404: 'Mailed check',\n", + " 405: 'Credit card (automatic)',\n", + " 406: 'Bank transfer (automatic)',\n", + " 407: 'Mailed check',\n", + " 408: 'Mailed check',\n", + " 409: 'Mailed check',\n", + " 410: 'Electronic check',\n", + " 411: 'Bank transfer (automatic)',\n", + " 412: 'Electronic check',\n", + " 413: 'Electronic check',\n", + " 414: 'Bank transfer (automatic)',\n", + " 415: 'Bank transfer (automatic)',\n", + " 416: 'Bank transfer (automatic)',\n", + " 417: 'Mailed check',\n", + " 418: 'Electronic check',\n", + " 419: 'Bank transfer (automatic)',\n", + " 420: 'Mailed check',\n", + " 421: 'Bank transfer (automatic)',\n", + " 422: 'Credit card (automatic)',\n", + " 423: 'Mailed check',\n", + " 424: 'Bank transfer (automatic)',\n", + " 425: 'Bank transfer (automatic)',\n", + " 426: 'Bank transfer (automatic)',\n", + " 427: 'Electronic check',\n", + " 428: 'Bank transfer (automatic)',\n", + " 429: 'Bank transfer (automatic)',\n", + " 430: 'Mailed check',\n", + " 431: 'Credit card (automatic)',\n", + " 432: 'Electronic check',\n", + " 433: 'Bank transfer (automatic)',\n", + " 434: 'Electronic check',\n", + " 435: 'Credit card (automatic)',\n", + " 436: 'Bank transfer (automatic)',\n", + " 437: 'Credit card (automatic)',\n", + " 438: 'Bank transfer (automatic)',\n", + " 439: 'Mailed check',\n", + " 440: 'Electronic check',\n", + " 441: 'Credit card (automatic)',\n", + " 442: 'Mailed check',\n", + " 443: 'Electronic check',\n", + " 444: 'Electronic check',\n", + " 445: 'Electronic check',\n", + " 446: 'Mailed check',\n", + " 447: 'Bank transfer (automatic)',\n", + " 448: 'Electronic check',\n", + " 449: 'Bank transfer (automatic)',\n", + " 450: 'Mailed check',\n", + " 451: 'Mailed check',\n", + " 452: 'Bank transfer (automatic)',\n", + " 453: 'Electronic check',\n", + " 454: 'Mailed check',\n", + " 455: 'Electronic check',\n", + " 456: 'Bank transfer (automatic)',\n", + " 457: 'Bank transfer (automatic)',\n", + " 458: 'Mailed check',\n", + " 459: 'Electronic check',\n", + " 460: 'Mailed check',\n", + " 461: 'Bank transfer (automatic)',\n", + " 462: 'Electronic check',\n", + " 463: 'Electronic check',\n", + " 464: 'Bank transfer (automatic)',\n", + " 465: 'Mailed check',\n", + " 466: 'Electronic check',\n", + " 467: 'Electronic check',\n", + " 468: 'Mailed check',\n", + " 469: 'Electronic check',\n", + " 470: 'Mailed check',\n", + " 471: 'Electronic check',\n", + " 472: 'Mailed check',\n", + " 473: 'Mailed check',\n", + " 474: 'Electronic check',\n", + " 475: 'Electronic check',\n", + " 476: 'Electronic check',\n", + " 477: 'Electronic check',\n", + " 478: 'Electronic check',\n", + " 479: 'Bank transfer (automatic)',\n", + " 480: 'Mailed check',\n", + " 481: 'Bank transfer (automatic)',\n", + " 482: 'Electronic check',\n", + " 483: 'Bank transfer (automatic)',\n", + " 484: 'Electronic check',\n", + " 485: 'Credit card (automatic)',\n", + " 486: 'Electronic 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899: 'Credit card (automatic)',\n", + " 900: 'Mailed check',\n", + " 901: 'Credit card (automatic)',\n", + " 902: 'Electronic check',\n", + " 903: 'Credit card (automatic)',\n", + " 904: 'Credit card (automatic)',\n", + " 905: 'Electronic check',\n", + " 906: 'Electronic check',\n", + " 907: 'Bank transfer (automatic)',\n", + " 908: 'Mailed check',\n", + " 909: 'Electronic check',\n", + " 910: 'Electronic check',\n", + " 911: 'Credit card (automatic)',\n", + " 912: 'Bank transfer (automatic)',\n", + " 913: 'Credit card (automatic)',\n", + " 914: 'Bank transfer (automatic)',\n", + " 915: 'Electronic check',\n", + " 916: 'Bank transfer (automatic)',\n", + " 917: 'Bank transfer (automatic)',\n", + " 918: 'Electronic check',\n", + " 919: 'Mailed check',\n", + " 920: 'Bank transfer (automatic)',\n", + " 921: 'Mailed check',\n", + " 922: 'Electronic check',\n", + " 923: 'Credit card (automatic)',\n", + " 924: 'Electronic check',\n", + " 925: 'Bank transfer (automatic)',\n", + " 926: 'Electronic check',\n", + " 927: 'Electronic check',\n", + " 928: 'Credit card (automatic)',\n", + " 929: 'Electronic check',\n", + " 930: 'Bank transfer (automatic)',\n", + " 931: 'Bank transfer (automatic)',\n", + " 932: 'Credit card (automatic)',\n", + " 933: 'Electronic check',\n", + " 934: 'Mailed check',\n", + " 935: 'Electronic check',\n", + " 936: 'Mailed check',\n", + " 937: 'Electronic check',\n", + " 938: 'Credit card (automatic)',\n", + " 939: 'Mailed check',\n", + " 940: 'Bank transfer (automatic)',\n", + " 941: 'Electronic check',\n", + " 942: 'Credit card (automatic)',\n", + " 943: 'Bank transfer (automatic)',\n", + " 944: 'Bank transfer (automatic)',\n", + " 945: 'Bank transfer (automatic)',\n", + " 946: 'Mailed check',\n", + " 947: 'Mailed check',\n", + " 948: 'Electronic check',\n", + " 949: 'Bank transfer (automatic)',\n", + " 950: 'Mailed check',\n", + " 951: 'Electronic check',\n", + " 952: 'Mailed check',\n", + " 953: 'Electronic check',\n", + " 954: 'Electronic check',\n", + " 955: 'Credit card (automatic)',\n", + " 956: 'Bank transfer (automatic)',\n", + " 957: 'Electronic check',\n", + " 958: 'Bank transfer (automatic)',\n", + " 959: 'Electronic check',\n", + " 960: 'Bank transfer (automatic)',\n", + " 961: 'Mailed check',\n", + " 962: 'Bank transfer (automatic)',\n", + " 963: 'Bank transfer (automatic)',\n", + " 964: 'Mailed check',\n", + " 965: 'Credit card (automatic)',\n", + " 966: 'Electronic check',\n", + " 967: 'Mailed check',\n", + " 968: 'Credit card (automatic)',\n", + " 969: 'Electronic check',\n", + " 970: 'Electronic check',\n", + " 971: 'Bank transfer (automatic)',\n", + " 972: 'Bank transfer (automatic)',\n", + " 973: 'Electronic check',\n", + " 974: 'Credit card (automatic)',\n", + " 975: 'Electronic check',\n", + " 976: 'Electronic check',\n", + " 977: 'Electronic check',\n", + " 978: 'Electronic check',\n", + " 979: 'Bank transfer (automatic)',\n", + " 980: 'Bank transfer (automatic)',\n", + " 981: 'Mailed check',\n", + " 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19.95,\n", + " 873: 50.8,\n", + " 874: 99.7,\n", + " 875: 34.8,\n", + " 876: 105.1,\n", + " 877: 60.15,\n", + " 878: 64.75,\n", + " 879: 54.65,\n", + " 880: 110.1,\n", + " 881: 19.3,\n", + " 882: 83.9,\n", + " 883: 111.25,\n", + " 884: 35.8,\n", + " 885: 20.05,\n", + " 886: 84.35,\n", + " 887: 110.5,\n", + " 888: 91.2,\n", + " 889: 100.55,\n", + " 890: 89.3,\n", + " 891: 103.85,\n", + " 892: 81.1,\n", + " 893: 24.6,\n", + " 894: 81.2,\n", + " 895: 94.3,\n", + " 896: 116.1,\n", + " 897: 105.55,\n", + " 898: 98.9,\n", + " 899: 94.4,\n", + " 900: 19.5,\n", + " 901: 98.3,\n", + " 902: 93.85,\n", + " 903: 105.6,\n", + " 904: 81.35,\n", + " 905: 100.5,\n", + " 906: 56.4,\n", + " 907: 65.35,\n", + " 908: 19.95,\n", + " 909: 111.25,\n", + " 910: 72.85,\n", + " 911: 89.0,\n", + " 912: 106.1,\n", + " 913: 20.05,\n", + " 914: 25.2,\n", + " 915: 73.55,\n", + " 916: 75.4,\n", + " 917: 65.55,\n", + " 918: 80.7,\n", + " 919: 104.55,\n", + " 920: 24.15,\n", + " 921: 20.45,\n", + " 922: 75.4,\n", + " 923: 79.7,\n", + " 924: 81.7,\n", + " 925: 76.3,\n", + " 926: 79.4,\n", + " 927: 81.15,\n", + " 928: 103.75,\n", + " 929: 86.45,\n", + " 930: 75.1,\n", + " 931: 80.6,\n", + " 932: 19.3,\n", + " 933: 84.6,\n", + " 934: 33.6,\n", + " 935: 83.25,\n", + " 936: 80.85,\n", + " 937: 79.05,\n", + " 938: 108.05,\n", + " 939: 19.9,\n", + " 940: 21.05,\n", + " 941: 30.15,\n", + " 942: 79.85,\n", + " 943: 65.5,\n", + " 944: 104.1,\n", + " 945: 74.4,\n", + " 946: 20.5,\n", + " 947: 91.35,\n", + " 948: 99.05,\n", + " 949: 20.5,\n", + " 950: 44.95,\n", + " 951: 75.6,\n", + " 952: 55.1,\n", + " 953: 58.95,\n", + " 954: 95.1,\n", + " 955: 44.7,\n", + " 956: 25.45,\n", + " 957: 56.75,\n", + " 958: 81.75,\n", + " 959: 86.1,\n", + " 960: 29.8,\n", + " 961: 20.5,\n", + " 962: 60.9,\n", + " 963: 73.25,\n", + " 964: 45.7,\n", + " 965: 100.3,\n", + " 966: 19.25,\n", + " 967: 20.85,\n", + " 968: 77.35,\n", + " 969: 96.0,\n", + " 970: 90.55,\n", + " 971: 93.85,\n", + " 972: 70.1,\n", + " 973: 30.35,\n", + " 974: 75.95,\n", + " 975: 108.05,\n", + " 976: 69.9,\n", + " 977: 75.25,\n", + " 978: 103.75,\n", + " 979: 54.95,\n", + " 980: 19.5,\n", + " 981: 19.6,\n", + " 982: 47.85,\n", + " 983: 86.6,\n", + " 984: 23.75,\n", + " 985: 80.6,\n", + " 986: 43.8,\n", + " 987: 19.75,\n", + " 988: 19.15,\n", + " 989: 19.6,\n", + " 990: 80.3,\n", + " 991: 24.35,\n", + " 992: 25.25,\n", + " 993: 26.1,\n", + " 994: 20.0,\n", + " 995: 85.3,\n", + " 996: 70.0,\n", + " 997: 94.3,\n", + " 998: 20.7,\n", + " 999: 70.3,\n", + " ...},\n", + " 'TotalCharges': {0: 29.85,\n", + " 1: 1889.5,\n", + " 2: 108.15,\n", + " 3: 1840.75,\n", + " 4: 151.65,\n", + " 5: 820.5,\n", + " 6: 1949.4,\n", + " 7: 301.9,\n", + " 8: 3046.05,\n", + " 9: 3487.95,\n", + " 10: 587.45,\n", + " 11: 326.8,\n", + " 12: 5681.1,\n", + " 13: 5036.3,\n", + " 14: 2686.05,\n", + " 15: 7895.15,\n", + " 16: 1022.95,\n", + " 17: 7382.25,\n", + " 18: 528.35,\n", + " 19: 1862.9,\n", + " 20: 39.65,\n", + " 21: 202.25,\n", + " 22: 20.15,\n", + " 23: 3505.1,\n", + " 24: 2970.3,\n", + " 25: 1530.6,\n", + " 26: 4749.15,\n", + " 27: 30.2,\n", + " 28: 6369.45,\n", + " 29: 1093.1,\n", + " 30: 6766.95,\n", + " 31: 181.65,\n", + " 32: 1874.45,\n", + " 33: 20.2,\n", + " 34: 45.25,\n", + " 35: 7251.7,\n", + " 36: 316.9,\n", + " 37: 3548.3,\n", + " 38: 3549.25,\n", + " 39: 1105.4,\n", + " 40: 475.7,\n", + " 41: 4872.35,\n", + " 42: 418.25,\n", + " 43: 4861.45,\n", + " 44: 981.45,\n", + " 45: 3906.7,\n", + " 46: 97.0,\n", + " 47: 144.15,\n", + " 48: 4217.8,\n", + " 49: 4254.1,\n", + " 50: 3838.75,\n", + " 51: 1426.4,\n", + " 52: 1752.65,\n", + " 53: 633.3,\n", + " 54: 4456.35,\n", + " 55: 1752.55,\n", + " 56: 6311.2,\n", + " 57: 7076.35,\n", + " 58: 894.3,\n", + " 59: 7853.7,\n", + " 60: 4707.1,\n", + " 61: 5450.7,\n", + " 62: 2962.0,\n", + " 63: 957.1,\n", + " 64: 857.25,\n", + " 65: 244.1,\n", + " 66: 3650.35,\n", + " 67: 2497.2,\n", + " 68: 930.9,\n", + " 69: 887.35,\n", + " 70: 49.05,\n", + " 71: 1090.65,\n", + " 72: 7099.0,\n", + " 73: 1424.6,\n", + " 74: 177.4,\n", + " 75: 6139.5,\n", + " 76: 2688.85,\n", + " 77: 482.25,\n", + " 78: 2111.3,\n", + " 79: 1216.6,\n", + " 80: 79.35,\n", + " 81: 565.35,\n", + " 82: 496.9,\n", + " 83: 4327.5,\n", + " 84: 973.35,\n", + " 85: 918.75,\n", + " 86: 2215.45,\n", + " 87: 1057.0,\n", + " 88: 927.1,\n", + " 89: 1009.25,\n", + " 90: 2570.2,\n", + " 91: 74.7,\n", + " 92: 5714.25,\n", + " 93: 7107.0,\n", + " 94: 7459.05,\n", + " 95: 927.35,\n", + " 96: 4748.7,\n", + " 97: 113.85,\n", + " 98: 1107.2,\n", + " 99: 2514.5,\n", + " 100: 20.2,\n", + " 101: 19.45,\n", + " 102: 3605.6,\n", + " 103: 3027.25,\n", + " 104: 7611.85,\n", + " 105: 100.2,\n", + " 106: 7303.05,\n", + " 107: 927.65,\n", + " 108: 3921.3,\n", + " 109: 1363.25,\n", + " 110: 5238.9,\n", + " 111: 3042.25,\n", + " 112: 3954.1,\n", + " 113: 2868.15,\n", + " 114: 3423.5,\n", + " 115: 248.4,\n", + " 116: 1126.35,\n", + " 117: 1064.65,\n", + " 118: 835.15,\n", + " 119: 2151.6,\n", + " 120: 5515.45,\n", + " 121: 112.75,\n", + " 122: 229.55,\n", + " 123: 350.35,\n", + " 124: 62.9,\n", + " 125: 3027.65,\n", + " 126: 2135.5,\n", + " 127: 1723.95,\n", + " 128: 19.75,\n", + " 129: 3985.35,\n", + " 130: 1215.65,\n", + " 131: 1502.65,\n", + " 132: 3260.1,\n", + " 133: 35.45,\n", + " 134: 81.25,\n", + " 135: 1188.2,\n", + " 136: 1778.5,\n", + " 137: 1277.75,\n", + " 138: 1170.55,\n", + " 139: 70.45,\n", + " 140: 6425.65,\n", + " 141: 563.65,\n", + " 142: 5971.25,\n", + " 143: 5289.05,\n", + " 144: 1756.2,\n", + " 145: 6416.7,\n", + " 146: 61.35,\n", + " 147: 45.65,\n", + " 148: 1929.95,\n", + " 149: 1071.4,\n", + " 150: 564.35,\n", + " 151: 655.5,\n", + " 152: 7930.55,\n", + " 153: 5215.25,\n", + " 154: 113.5,\n", + " 155: 1152.8,\n", + " 156: 1821.95,\n", + " 157: 419.9,\n", + " 158: 1024.0,\n", + " 159: 251.6,\n", + " 160: 764.55,\n", + " 161: 1592.35,\n", + " 162: 135.2,\n", + " 163: 3958.25,\n", + " 164: 233.9,\n", + " 165: 1363.45,\n", + " 166: 273.0,\n", + " 167: 6254.45,\n", + " 168: 2651.2,\n", + " 169: 321.4,\n", + " 170: 3539.25,\n", + " 171: 242.8,\n", + " 172: 1181.75,\n", + " 173: 5000.2,\n", + " 174: 654.55,\n", + " 175: 780.2,\n", + " 176: 1145.7,\n", + " 177: 503.6,\n", + " 178: 1559.25,\n", + " 179: 1252.0,\n", + " 180: 29.95,\n", + " 181: 45.3,\n", + " 182: 662.65,\n", + " 183: 2453.3,\n", + " 184: 1111.65,\n", + " 185: 24.8,\n", + " 186: 1023.85,\n", + " 187: 82.15,\n", + " 188: 244.8,\n", + " 189: 2379.1,\n", + " 190: 3173.35,\n", + " 191: 531.0,\n", + " 192: 1375.4,\n", + " 193: 8129.3,\n", + " 194: 1192.7,\n", + " 195: 1901.65,\n", + " 196: 587.4,\n", + " 197: 6519.75,\n", + " 198: 8041.65,\n", + " 199: 20.75,\n", + " 200: 2681.15,\n", + " 201: 1112.3,\n", + " 202: 7405.5,\n", + " 203: 1033.95,\n", + " 204: 2958.95,\n", + " 205: 2684.85,\n", + " 206: 4179.2,\n", + " 207: 79.9,\n", + " 208: 1934.45,\n", + " 209: 6654.1,\n", + " 210: 84.5,\n", + " 211: 25.25,\n", + " 212: 1124.2,\n", + " 213: 540.05,\n", + " 214: 1975.85,\n", + " 215: 3437.45,\n", + " 216: 3139.8,\n", + " 217: 3789.2,\n", + " 218: 5324.5,\n", + " 219: 624.6,\n", + " 220: 268.35,\n", + " 221: 1836.9,\n", + " 222: 20.2,\n", + " 223: 179.35,\n", + " 224: 219.35,\n", + " 225: 1288.75,\n", + " 226: 2545.75,\n", + " 227: 55.2,\n", + " 228: 2723.15,\n", + " 229: 4107.25,\n", + " 230: 5760.65,\n", + " 231: 4747.5,\n", + " 232: 84.6,\n", + " 233: 1566.9,\n", + " 234: 702.0,\n", + " 235: 114.1,\n", + " 236: 299.05,\n", + " 237: 1305.95,\n", + " 238: 1120.3,\n", + " 239: 284.35,\n", + " 240: 6350.5,\n", + " 241: 7878.3,\n", + " 242: 3187.65,\n", + " 243: 6126.15,\n", + " 244: 731.3,\n", + " 245: 273.4,\n", + " 246: 2531.8,\n", + " 247: 1074.3,\n", + " 248: 48.55,\n", + " 249: 4298.45,\n", + " 250: 4619.55,\n", + " 251: 147.15,\n", + " 252: 40.2,\n", + " 253: 2633.3,\n", + " 254: 193.05,\n", + " 255: 4103.9,\n", + " 256: 7008.15,\n", + " 257: 5791.1,\n", + " 258: 80.55,\n", + " 259: 1228.65,\n", + " 260: 132.2,\n", + " 261: 1364.3,\n", + " 262: 4925.35,\n", + " 263: 1520.1,\n", + " 264: 5032.25,\n", + " 265: 5526.75,\n", + " 266: 1195.25,\n", + " 267: 2007.25,\n", + " 268: 1099.6,\n", + " 269: 1732.95,\n", + " 270: 1511.2,\n", + " 271: 3450.15,\n", + " 272: 24.8,\n", + " 273: 2172.05,\n", + " 274: 70.6,\n", + " 275: 401.1,\n", + " 276: 5624.85,\n", + " 277: 1339.8,\n", + " 278: 771.95,\n", + " 279: 244.75,\n", + " 280: 322.9,\n", + " 281: 498.25,\n", + " 282: 25.4,\n", + " 283: 3687.75,\n", + " 284: 1779.95,\n", + " 285: 1783.6,\n", + " 286: 927.15,\n", + " 287: 70.0,\n", + " 288: 606.55,\n", + " 289: 435.4,\n", + " 290: 1712.7,\n", + " 291: 2021.2,\n", + " 292: 1940.8,\n", + " 293: 567.8,\n", + " 294: 220.35,\n", + " 295: 20.25,\n", + " 296: 5436.45,\n", + " 297: 3437.5,\n", + " 298: 3015.75,\n", + " 299: 73.6,\n", + " 300: 1509.8,\n", + " 301: 396.1,\n", + " 302: 356.65,\n", + " 303: 4109.0,\n", + " 304: 3141.7,\n", + " 305: 1229.1,\n", + " 306: 2303.35,\n", + " 307: 2054.4,\n", + " 308: 3741.85,\n", + " 309: 3682.45,\n", + " 310: 19.25,\n", + " 311: 1886.25,\n", + " 312: 4895.1,\n", + " 313: 341.6,\n", + " 314: 415.55,\n", + " 315: 5686.4,\n", + " 316: 1355.1,\n", + " 317: 3058.65,\n", + " 318: 2231.05,\n", + " 319: 3236.35,\n", + " 320: 4350.1,\n", + " 321: 4264.0,\n", + " 322: 44.8,\n", + " 323: 422.3,\n", + " 324: 4176.7,\n", + " 325: 5138.1,\n", + " 326: 880.05,\n", + " 327: 139.05,\n", + " 328: 973.65,\n", + " 329: 1470.05,\n", + " 330: 739.35,\n", + " 331: 161.95,\n", + " 332: 4422.95,\n", + " 333: 511.25,\n", + " 334: 155.8,\n", + " 335: 5293.95,\n", + " 336: 4759.85,\n", + " 337: 6148.45,\n", + " 338: 3565.65,\n", + " 339: 6603.0,\n", + " 340: 1830.1,\n", + " 341: 6223.8,\n", + " 342: 4508.65,\n", + " 343: 1328.15,\n", + " 344: 865.0,\n", + " 345: 72.1,\n", + " 346: 168.2,\n", + " 347: 1303.5,\n", + " 348: 996.85,\n", + " 349: 6430.9,\n", + " 350: 2278.75,\n", + " 351: 681.4,\n", + " 352: 574.35,\n", + " 353: 371.9,\n", + " 354: 840.1,\n", + " 355: 846.0,\n", + " 356: 889.0,\n", + " 357: 6823.4,\n", + " 358: 805.1,\n", + " 359: 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False,\n", + " 424: True,\n", + " 425: False,\n", + " 426: True,\n", + " 427: True,\n", + " 428: False,\n", + " 429: True,\n", + " 430: False,\n", + " 431: True,\n", + " 432: True,\n", + " 433: False,\n", + " 434: False,\n", + " 435: True,\n", + " 436: False,\n", + " 437: True,\n", + " 438: True,\n", + " 439: True,\n", + " 440: True,\n", + " 441: True,\n", + " 442: True,\n", + " 443: True,\n", + " 444: True,\n", + " 445: True,\n", + " 446: False,\n", + " 447: False,\n", + " 448: True,\n", + " 449: True,\n", + " 450: False,\n", + " 451: True,\n", + " 452: True,\n", + " 453: True,\n", + " 454: False,\n", + " 455: True,\n", + " 456: True,\n", + " 457: True,\n", + " 458: False,\n", + " 459: False,\n", + " 460: False,\n", + " 461: True,\n", + " 462: True,\n", + " 463: True,\n", + " 464: True,\n", + " 465: True,\n", + " 466: True,\n", + " 467: True,\n", + " 468: False,\n", + " 469: True,\n", + " 470: False,\n", + " 471: True,\n", + " 472: True,\n", + " 473: True,\n", + " 474: True,\n", + " 475: True,\n", + " 476: True,\n", + " 477: True,\n", + " 478: True,\n", + " 479: False,\n", + " 480: False,\n", + " 481: True,\n", + " 482: True,\n", + " 483: True,\n", + " 484: True,\n", + " 485: True,\n", + " 486: True,\n", + " 487: True,\n", + " 488: True,\n", + " 489: False,\n", + " 490: True,\n", + " 491: True,\n", + " 492: True,\n", + " 493: True,\n", + " 494: False,\n", + " 495: True,\n", + " 496: True,\n", + " 497: False,\n", + " 498: True,\n", + " 499: True,\n", + " 500: True,\n", + " 501: True,\n", + " 502: True,\n", + " 503: True,\n", + " 504: True,\n", + " 505: True,\n", + " 506: True,\n", + " 507: True,\n", + " 508: True,\n", + " 509: False,\n", + " 510: True,\n", + " 511: True,\n", + " 512: False,\n", + " 513: True,\n", + " 514: True,\n", + " 515: True,\n", + " 516: True,\n", + " 517: False,\n", + " 518: True,\n", + " 519: False,\n", + " 520: True,\n", + " 521: False,\n", + " 522: True,\n", + " 523: False,\n", + " 524: True,\n", + " 525: True,\n", + " 526: True,\n", + " 527: True,\n", + " 528: False,\n", + " 529: True,\n", + " 530: False,\n", + " 531: False,\n", + " 532: True,\n", + " 533: True,\n", + " 534: True,\n", + " 535: True,\n", + " 536: True,\n", + " 537: True,\n", + " 538: True,\n", + " 539: True,\n", + " 540: True,\n", + " 541: True,\n", + " 542: False,\n", + " 543: True,\n", + " 544: True,\n", + " 545: True,\n", + " 546: True,\n", + " 547: True,\n", + " 548: True,\n", + " 549: False,\n", + " 550: True,\n", + " 551: True,\n", + " 552: False,\n", + " 553: False,\n", + " 554: True,\n", + " 555: True,\n", + " 556: False,\n", + " 557: True,\n", + " 558: False,\n", + " 559: True,\n", + " 560: True,\n", + " 561: False,\n", + " 562: True,\n", + " 563: True,\n", + " 564: True,\n", + " 565: True,\n", + " 566: True,\n", + " 567: True,\n", + " 568: True,\n", + " 569: True,\n", + " 570: False,\n", + " 571: True,\n", + " 572: True,\n", + " 573: True,\n", + " 574: True,\n", + " 575: True,\n", + " 576: True,\n", + " 577: False,\n", + " 578: True,\n", + " 579: False,\n", + " 580: False,\n", + " 581: True,\n", + " 582: True,\n", + " 583: False,\n", + " 584: False,\n", + " 585: True,\n", + " 586: True,\n", + " 587: False,\n", + " 588: True,\n", + " 589: True,\n", + " 590: True,\n", + " 591: False,\n", + " 592: True,\n", + " 593: True,\n", + " 594: False,\n", + " 595: True,\n", + " 596: True,\n", + " 597: True,\n", + " 598: True,\n", + " 599: False,\n", + " 600: True,\n", + " 601: False,\n", + " 602: False,\n", + " 603: True,\n", + " 604: True,\n", + " 605: True,\n", + " 606: True,\n", + " 607: True,\n", + " 608: True,\n", + " 609: True,\n", + " 610: True,\n", + " 611: True,\n", + " 612: False,\n", + " 613: True,\n", + " 614: True,\n", + " 615: False,\n", + " 616: False,\n", + " 617: True,\n", + " 618: True,\n", + " 619: True,\n", + " 620: True,\n", + " 621: True,\n", + " 622: True,\n", + " 623: False,\n", + " 624: False,\n", + " 625: True,\n", + " 626: True,\n", + " 627: True,\n", + " 628: True,\n", + " 629: True,\n", + " 630: True,\n", + " 631: True,\n", + " 632: True,\n", + " 633: False,\n", + " 634: True,\n", + " 635: True,\n", + " 636: True,\n", + " 637: True,\n", + " 638: True,\n", + " 639: True,\n", + " 640: False,\n", + " 641: True,\n", + " 642: True,\n", + " 643: True,\n", + " 644: False,\n", + " 645: True,\n", + " 646: False,\n", + " 647: True,\n", + " 648: True,\n", + " 649: False,\n", + " 650: False,\n", + " 651: True,\n", + " 652: False,\n", + " 653: False,\n", + " 654: False,\n", + " 655: True,\n", + " 656: True,\n", + " 657: True,\n", + " 658: False,\n", + " 659: False,\n", + " 660: False,\n", + " 661: False,\n", + " 662: False,\n", + " 663: True,\n", + " 664: False,\n", + " 665: True,\n", + " 666: False,\n", + " 667: True,\n", + " 668: False,\n", + " 669: True,\n", + " 670: False,\n", + " 671: False,\n", + " 672: False,\n", + " 673: False,\n", + " 674: True,\n", + " 675: True,\n", + " 676: False,\n", + " 677: True,\n", + " 678: True,\n", + " 679: True,\n", + " 680: True,\n", + " 681: True,\n", + " 682: True,\n", + " 683: True,\n", + " 684: False,\n", + " 685: True,\n", + " 686: False,\n", + " 687: True,\n", + " 688: True,\n", + " 689: True,\n", + " 690: False,\n", + " 691: True,\n", + " 692: False,\n", + " 693: False,\n", + " 694: False,\n", + " 695: False,\n", + " 696: True,\n", + " 697: True,\n", + " 698: True,\n", + " 699: True,\n", + " 700: True,\n", + " 701: True,\n", + " 702: True,\n", + " 703: False,\n", + " 704: True,\n", + " 705: True,\n", + " 706: True,\n", + " 707: True,\n", + " 708: True,\n", + " 709: True,\n", + " 710: False,\n", + " 711: True,\n", + " 712: False,\n", + " 713: True,\n", + " 714: False,\n", + " 715: True,\n", + " 716: True,\n", + " 717: True,\n", + " 718: True,\n", + " 719: True,\n", + " 720: False,\n", + " 721: False,\n", + " 722: True,\n", + " 723: False,\n", + " 724: True,\n", + " 725: False,\n", + " 726: False,\n", + " 727: True,\n", + " 728: True,\n", + " 729: True,\n", + " 730: True,\n", + " 731: False,\n", + " 732: True,\n", + " 733: True,\n", + " 734: True,\n", + " 735: True,\n", + " 736: True,\n", + " 737: True,\n", + " 738: True,\n", + " 739: True,\n", + " 740: True,\n", + " 741: True,\n", + " 742: True,\n", + " 743: False,\n", + " 744: False,\n", + " 745: True,\n", + " 746: True,\n", + " 747: True,\n", + " 748: False,\n", + " 749: True,\n", + " 750: True,\n", + " 751: True,\n", + " 752: True,\n", + " 753: False,\n", + " 754: True,\n", + " 755: True,\n", + " 756: False,\n", + " 757: False,\n", + " 758: True,\n", + " 759: True,\n", + " 760: False,\n", + " 761: True,\n", + " 762: False,\n", + " 763: True,\n", + " 764: False,\n", + " 765: True,\n", + " 766: True,\n", + " 767: False,\n", + " 768: True,\n", + " 769: True,\n", + " 770: False,\n", + " 771: True,\n", + " 772: False,\n", + " 773: True,\n", + " 774: True,\n", + " 775: True,\n", + " 776: True,\n", + " 777: True,\n", + " 778: True,\n", + " 779: False,\n", + " 780: True,\n", + " 781: False,\n", + " 782: True,\n", + " 783: True,\n", + " 784: True,\n", + " 785: True,\n", + " 786: True,\n", + " 787: True,\n", + " 788: True,\n", + " 789: True,\n", + " 790: True,\n", + " 791: True,\n", + " 792: False,\n", + " 793: True,\n", + " 794: True,\n", + " 795: True,\n", + " 796: True,\n", + " 797: False,\n", + " 798: False,\n", + " 799: True,\n", + " 800: False,\n", + " 801: True,\n", + " 802: False,\n", + " 803: True,\n", + " 804: True,\n", + " 805: True,\n", + " 806: True,\n", + " 807: True,\n", + " 808: True,\n", + " 809: False,\n", + " 810: True,\n", + " 811: True,\n", + " 812: False,\n", + " 813: True,\n", + " 814: False,\n", + " 815: True,\n", + " 816: True,\n", + " 817: True,\n", + " 818: True,\n", + " 819: True,\n", + " 820: True,\n", + " 821: True,\n", + " 822: True,\n", + " 823: True,\n", + " 824: False,\n", + " 825: True,\n", + " 826: True,\n", + " 827: False,\n", + " 828: True,\n", + " 829: True,\n", + " 830: True,\n", + " 831: False,\n", + " 832: True,\n", + " 833: True,\n", + " 834: True,\n", + " 835: True,\n", + " 836: True,\n", + " 837: False,\n", + " 838: True,\n", + " 839: False,\n", + " 840: True,\n", + " 841: True,\n", + " 842: False,\n", + " 843: True,\n", + " 844: True,\n", + " 845: False,\n", + " 846: True,\n", + " 847: True,\n", + " 848: True,\n", + " 849: True,\n", + " 850: True,\n", + " 851: True,\n", + " 852: False,\n", + " 853: True,\n", + " 854: False,\n", + " 855: True,\n", + " 856: True,\n", + " 857: True,\n", + " 858: True,\n", + " 859: True,\n", + " 860: False,\n", + " 861: True,\n", + " 862: True,\n", + " 863: True,\n", + " 864: True,\n", + " 865: True,\n", + " 866: True,\n", + " 867: False,\n", + " 868: True,\n", + " 869: False,\n", + " 870: True,\n", + " 871: True,\n", + " 872: False,\n", + " 873: True,\n", + " 874: True,\n", + " 875: True,\n", + " 876: True,\n", + " 877: True,\n", + " 878: True,\n", + " 879: True,\n", + " 880: True,\n", + " 881: False,\n", + " 882: True,\n", + " 883: True,\n", + " 884: True,\n", + " 885: False,\n", + " 886: True,\n", + " 887: True,\n", + " 888: True,\n", + " 889: True,\n", + " 890: True,\n", + " 891: True,\n", + " 892: True,\n", + " 893: False,\n", + " 894: True,\n", + " 895: True,\n", + " 896: True,\n", + " 897: True,\n", + " 898: True,\n", + " 899: True,\n", + " 900: False,\n", + " 901: True,\n", + " 902: True,\n", + " 903: True,\n", + " 904: True,\n", + " 905: True,\n", + " 906: True,\n", + " 907: True,\n", + " 908: False,\n", + " 909: True,\n", + " 910: True,\n", + " 911: True,\n", + " 912: True,\n", + " 913: False,\n", + " 914: False,\n", + " 915: False,\n", + " 916: True,\n", + " 917: True,\n", + " 918: True,\n", + " 919: True,\n", + " 920: False,\n", + " 921: False,\n", + " 922: False,\n", + " 923: True,\n", + " 924: True,\n", + " 925: True,\n", + " 926: True,\n", + " 927: True,\n", + " 928: True,\n", + " 929: True,\n", + " 930: True,\n", + " 931: True,\n", + " 932: False,\n", + " 933: True,\n", + " 934: True,\n", + " 935: True,\n", + " 936: True,\n", + " 937: True,\n", + " 938: True,\n", + " 939: False,\n", + " 940: False,\n", + " 941: True,\n", + " 942: True,\n", + " 943: True,\n", + " 944: True,\n", + " 945: False,\n", + " 946: False,\n", + " 947: True,\n", + " 948: True,\n", + " 949: False,\n", + " 950: False,\n", + " 951: True,\n", + " 952: True,\n", + " 953: True,\n", + " 954: True,\n", + " 955: False,\n", + " 956: False,\n", + " 957: True,\n", + " 958: True,\n", + " 959: True,\n", + " 960: True,\n", + " 961: False,\n", + " 962: True,\n", + " 963: True,\n", + " 964: False,\n", + " 965: True,\n", + " 966: False,\n", + " 967: False,\n", + " 968: True,\n", + " 969: True,\n", + " 970: True,\n", + " 971: True,\n", + " 972: True,\n", + " 973: True,\n", + " 974: True,\n", + " 975: True,\n", + " 976: False,\n", + " 977: False,\n", + " 978: True,\n", + " 979: True,\n", + " 980: False,\n", + " 981: False,\n", + " 982: True,\n", + " 983: True,\n", + " 984: False,\n", + " 985: True,\n", + " 986: False,\n", + " 987: False,\n", + " 988: False,\n", + " 989: False,\n", + " 990: True,\n", + " 991: False,\n", + " 992: False,\n", + " 993: False,\n", + " 994: False,\n", + " 995: True,\n", + " 996: False,\n", + " 997: True,\n", + " 998: False,\n", + " 999: True,\n", + " ...}},\n", + " 'target_variable': 'Churn',\n", + " 'all_datasets_summary': \"Dataset Name: Dataset_0\\n ----------------------------\\n Shape: 7043 rows x 21 columns\\n\\n Column Data Types:\\n customerID: object\\ngender: object\\nSeniorCitizen: int64\\nPartner: object\\nDependents: object\\ntenure: int64\\nPhoneService: object\\nMultipleLines: object\\nInternetService: object\\nOnlineSecurity: object\\nOnlineBackup: object\\nDeviceProtection: object\\nTechSupport: object\\nStreamingTV: object\\nStreamingMovies: object\\nContract: object\\nPaperlessBilling: object\\nPaymentMethod: object\\nMonthlyCharges: float64\\nTotalCharges: object\\nChurn: object\\n\\n Missing Value Percentage:\\n customerID: 0.00%\\nDeviceProtection: 0.00%\\nTotalCharges: 0.00%\\nMonthlyCharges: 0.00%\\nPaymentMethod: 0.00%\\nPaperlessBilling: 0.00%\\nContract: 0.00%\\nStreamingMovies: 0.00%\\nStreamingTV: 0.00%\\nTechSupport: 0.00%\\nOnlineBackup: 0.00%\\ngender: 0.00%\\nOnlineSecurity: 0.00%\\nInternetService: 0.00%\\nMultipleLines: 0.00%\\nPhoneService: 0.00%\\ntenure: 0.00%\\nDependents: 0.00%\\nPartner: 0.00%\\nSeniorCitizen: 0.00%\\nChurn: 0.00%\\n\\n Unique Value Counts:\\n customerID: 7043\\ngender: 2\\nSeniorCitizen: 2\\nPartner: 2\\nDependents: 2\\ntenure: 73\\nPhoneService: 2\\nMultipleLines: 3\\nInternetService: 3\\nOnlineSecurity: 3\\nOnlineBackup: 3\\nDeviceProtection: 3\\nTechSupport: 3\\nStreamingTV: 3\\nStreamingMovies: 3\\nContract: 3\\nPaperlessBilling: 2\\nPaymentMethod: 4\\nMonthlyCharges: 1585\\nTotalCharges: 6531\\nChurn: 2\\n\\n Data (first 30 rows):\\n customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn\\n0 7590-VHVEG Female 0 Yes No 1 No No phone service DSL No Yes No No No No Month-to-month Yes Electronic check 29.85 29.85 No\\n1 5575-GNVDE Male 0 No No 34 Yes No DSL Yes No Yes No No No One year No Mailed check 56.95 1889.5 No\\n2 3668-QPYBK Male 0 No No 2 Yes No DSL Yes Yes No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes\\n3 7795-CFOCW Male 0 No No 45 No No phone service DSL Yes No Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No\\n4 9237-HQITU Female 0 No No 2 Yes No Fiber optic No No No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes\\n5 9305-CDSKC Female 0 No No 8 Yes Yes Fiber optic No No Yes No Yes Yes Month-to-month Yes Electronic check 99.65 820.5 Yes\\n6 1452-KIOVK Male 0 No Yes 22 Yes Yes Fiber optic No Yes No No Yes No Month-to-month Yes Credit card (automatic) 89.10 1949.4 No\\n7 6713-OKOMC Female 0 No No 10 No No phone service DSL Yes No No No No No Month-to-month No Mailed check 29.75 301.9 No\\n8 7892-POOKP Female 0 Yes No 28 Yes Yes Fiber optic No No Yes Yes Yes Yes Month-to-month Yes Electronic check 104.80 3046.05 Yes\\n9 6388-TABGU Male 0 No Yes 62 Yes No DSL Yes Yes No No No No One year No Bank transfer (automatic) 56.15 3487.95 No\\n10 9763-GRSKD Male 0 Yes Yes 13 Yes No DSL Yes No No No No No Month-to-month Yes Mailed check 49.95 587.45 No\\n11 7469-LKBCI Male 0 No No 16 Yes No No No internet service No internet service No internet service No internet service No internet service No internet service Two year No Credit card (automatic) 18.95 326.8 No\\n12 8091-TTVAX Male 0 Yes No 58 Yes Yes Fiber optic No No Yes No Yes Yes One year No Credit card (automatic) 100.35 5681.1 No\\n13 0280-XJGEX Male 0 No No 49 Yes Yes Fiber optic No Yes Yes No Yes Yes Month-to-month Yes Bank transfer (automatic) 103.70 5036.3 Yes\\n14 5129-JLPIS Male 0 No No 25 Yes No Fiber optic Yes No Yes Yes Yes Yes Month-to-month Yes Electronic check 105.50 2686.05 No\\n15 3655-SNQYZ Female 0 Yes Yes 69 Yes Yes Fiber optic Yes Yes Yes Yes Yes Yes Two year No Credit card (automatic) 113.25 7895.15 No\\n16 8191-XWSZG Female 0 No No 52 Yes No No No internet service No internet service No internet service No internet service No internet service No internet service One year No Mailed check 20.65 1022.95 No\\n17 9959-WOFKT Male 0 No Yes 71 Yes Yes Fiber optic Yes No Yes No Yes Yes Two year No Bank transfer (automatic) 106.70 7382.25 No\\n18 4190-MFLUW Female 0 Yes Yes 10 Yes No DSL No No Yes Yes No No Month-to-month No Credit card (automatic) 55.20 528.35 Yes\\n19 4183-MYFRB Female 0 No No 21 Yes No Fiber optic No Yes Yes No No Yes Month-to-month Yes Electronic check 90.05 1862.9 No\\n20 8779-QRDMV Male 1 No No 1 No No phone service DSL No No Yes No No Yes Month-to-month Yes Electronic check 39.65 39.65 Yes\\n21 1680-VDCWW Male 0 Yes No 12 Yes No No No internet service No internet service No internet service No internet service No internet service No internet service One year No Bank transfer (automatic) 19.80 202.25 No\\n22 1066-JKSGK Male 0 No No 1 Yes No No No internet service No internet service No internet service No internet service No internet service No internet service Month-to-month No Mailed check 20.15 20.15 Yes\\n23 3638-WEABW Female 0 Yes No 58 Yes Yes DSL No Yes No Yes No No Two year Yes Credit card (automatic) 59.90 3505.1 No\\n24 6322-HRPFA Male 0 Yes Yes 49 Yes No DSL Yes Yes No Yes No No Month-to-month No Credit card (automatic) 59.60 2970.3 No\\n25 6865-JZNKO Female 0 No No 30 Yes No DSL Yes Yes No No No No Month-to-month Yes Bank transfer (automatic) 55.30 1530.6 No\\n26 6467-CHFZW Male 0 Yes Yes 47 Yes Yes Fiber optic No Yes No No Yes Yes Month-to-month Yes Electronic check 99.35 4749.15 Yes\\n27 8665-UTDHZ Male 0 Yes Yes 1 No No phone service DSL No Yes No No No No Month-to-month No Electronic check 30.20 30.2 Yes\\n28 5248-YGIJN Male 0 Yes No 72 Yes Yes DSL Yes Yes Yes Yes Yes Yes Two year Yes Credit card (automatic) 90.25 6369.45 No\\n29 8773-HHUOZ Female 0 No Yes 17 Yes No DSL No No No No Yes Yes Month-to-month Yes Mailed check 64.70 1093.1 Yes\\n\\n Data Description:\\n SeniorCitizen tenure MonthlyCharges\\ncount 7043.000000 7043.000000 7043.000000\\nmean 0.162147 32.371149 64.761692\\nstd 0.368612 24.559481 30.090047\\nmin 0.000000 0.000000 18.250000\\n25% 0.000000 9.000000 35.500000\\n50% 0.000000 29.000000 70.350000\\n75% 0.000000 55.000000 89.850000\\nmax 1.000000 72.000000 118.750000\\n\\n Data Info:\\n \\nIndex: 7043 entries, 0 to 7042\\nData columns (total 21 columns):\\n # Column Non-Null Count Dtype \\n--- ------ -------------- ----- \\n 0 customerID 7043 non-null object \\n 1 gender 7043 non-null object \\n 2 SeniorCitizen 7043 non-null int64 \\n 3 Partner 7043 non-null object \\n 4 Dependents 7043 non-null object \\n 5 tenure 7043 non-null int64 \\n 6 PhoneService 7043 non-null object \\n 7 MultipleLines 7043 non-null object \\n 8 InternetService 7043 non-null object \\n 9 OnlineSecurity 7043 non-null object \\n 10 OnlineBackup 7043 non-null object \\n 11 DeviceProtection 7043 non-null object \\n 12 TechSupport 7043 non-null object \\n 13 StreamingTV 7043 non-null object \\n 14 StreamingMovies 7043 non-null object \\n 15 Contract 7043 non-null object \\n 16 PaperlessBilling 7043 non-null object \\n 17 PaymentMethod 7043 non-null object \\n 18 MonthlyCharges 7043 non-null float64\\n 19 TotalCharges 7043 non-null object \\n 20 Churn 7043 non-null object \\ndtypes: float64(1), int64(2), object(18)\\nmemory usage: 1.2+ MB\",\n", + " 'feature_engineer_function': '# Disclaimer: This function was generated by AI. Please review before using.\\n# Agent Name: feature_engineering_agent\\n# Time Created: 2025-01-09 22:36:39\\n\\ndef feature_engineer(data_raw):\\n import pandas as pd\\n import numpy as np\\n from sklearn.preprocessing import OneHotEncoder, LabelEncoder\\n\\n\\n\\n # Step 1: Convert Data Types\\n data_raw[\\'TotalCharges\\'] = pd.to_numeric(data_raw[\\'TotalCharges\\'], errors=\\'coerce\\')\\n data_raw[\\'Churn\\'] = data_raw[\\'Churn\\'].astype(\\'category\\')\\n\\n # Step 2: Remove Unique Value Features\\n data_raw.drop(columns=[\\'customerID\\'], inplace=True)\\n\\n # Step 3: Remove Constant Features\\n constant_columns = [col for col in data_raw.columns if data_raw[col].nunique() <= 1]\\n data_raw.drop(columns=constant_columns, inplace=True)\\n\\n # Step 4: High Cardinality Categorical Encoding\\n high_cardinality_features = [\\'InternetService\\', \\'Contract\\', \\'PaymentMethod\\']\\n for col in high_cardinality_features:\\n if data_raw[col].nunique() / len(data_raw) > 0.05:\\n # Consolidate infrequent categories into \"Other\"\\n value_counts = data_raw[col].value_counts(normalize=True)\\n threshold = 0.05\\n to_replace = value_counts[value_counts < threshold].index\\n data_raw[col].replace(to_replace, \\'Other\\', inplace=True)\\n\\n # Step 5: One-Hot Encoding\\n categorical_features = [\\'gender\\', \\'Partner\\', \\'Dependents\\', \\'PhoneService\\', \\n \\'MultipleLines\\', \\'InternetService\\', \\'OnlineSecurity\\', \\n \\'OnlineBackup\\', \\'DeviceProtection\\', \\'TechSupport\\', \\n \\'StreamingTV\\', \\'StreamingMovies\\', \\'Contract\\', \\n \\'PaperlessBilling\\', \\'PaymentMethod\\']\\n \\n data_encoded = pd.get_dummies(data_raw, columns=categorical_features, drop_first=True)\\n\\n # Step 6: Convert Boolean to Integer\\n data_encoded[\\'SeniorCitizen\\'] = data_encoded[\\'SeniorCitizen\\'].astype(int)\\n data_encoded[\\'Churn\\'] = data_encoded[\\'Churn\\'].cat.codes # Encode categorical target variable\\n\\n # Step 9: Analyze for Additional Features\\n data_encoded[\\'AvgMonthlyCharge\\'] = data_encoded[\\'MonthlyCharges\\'] / data_encoded[\\'tenure\\'].replace(0, np.nan)\\n data_encoded[\\'ServiceCount\\'] = (data_encoded[\\'OnlineSecurity_Yes\\'].fillna(0) + \\n data_encoded[\\'OnlineBackup_Yes\\'].fillna(0) + \\n data_encoded[\\'DeviceProtection_Yes\\'].fillna(0) + \\n data_encoded[\\'TechSupport_Yes\\'].fillna(0) + \\n data_encoded[\\'StreamingTV_Yes\\'].fillna(0) + \\n data_encoded[\\'StreamingMovies_Yes\\'].fillna(0))\\n\\n # Step 10: Final Review\\n # Review for any additional transformations (if needed)\\n\\n return data_encoded',\n", + " 'feature_engineer_function_path': '/Users/mdancho/Desktop/course_code/ai-data-science-team/logs/feature_engineer.py',\n", + " 'feature_engineer_function_name': 'feature_engineer.py',\n", + " 'feature_engineer_error': None,\n", + " 'max_retries': 3,\n", + " 'retry_count': 0}" ] }, - "execution_count": 15, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "list(response.keys())" + "feature_engineering_agent.get_response()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### The feature engineering recipe:" + "#### Feature Engineered Data" ] }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('# Feature Engineering Agent:\\n'\n", - " '\\n'\n", - " ' The `feature_engineer` function carries out several key feature engineering '\n", - " 'steps on a raw dataset, aimed at preparing it for machine learning modeling. '\n", - " 'Here’s a clear and concise breakdown of the steps performed:\\n'\n", - " '\\n'\n", - " '1. **Data Copy**: The function creates a copy of the input dataset to '\n", - " 'prevent unintentional modifications to the original data.\\n'\n", - " '\\n'\n", - " '2. **Missing Values Handling**: It fills any missing values using forward '\n", - " 'filling (ffill), which propagates the last valid observation forward.\\n'\n", - " '\\n'\n", - " '3. **Remove Unique Features**: The function removes string or categorical '\n", - " 'features that have unique values equal to the size of the dataset, as these '\n", - " 'do not contribute useful information.\\n'\n", - " '\\n'\n", - " '4. **Remove Constant Features**: It identifies and drops features that have '\n", - " 'only one unique value, as they provide no variability.\\n'\n", - " '\\n'\n", - " '5. **Identify Categorical Features**: The function extracts the names of '\n", - " \"categorical features (i.e., those with data type 'object') for further \"\n", - " 'processing.\\n'\n", - " '\\n'\n", - " '6. **Handle High Cardinality**: For categorical features with a large number '\n", - " 'of unique values (more than 5% of the dataset), infrequent values are '\n", - " \"replaced with the label 'other' to reduce dimensionality.\\n\"\n", - " '\\n'\n", - " '7. **One-Hot Encoding**: The function applies one-hot encoding to the '\n", - " 'categorical variables to convert them into a binary format, facilitating '\n", - " 'their use in machine learning algorithms. The first category is dropped to '\n", - " 'avoid multicollinearity.\\n'\n", - " '\\n'\n", - " '8. **Convert Boolean Columns**: Boolean columns are converted to integers (0 '\n", - " 'and 1) for consistency in data representation.\\n'\n", - " '\\n'\n", - " '9. **Convert Datetime Columns**: If there are any datetime columns, it '\n", - " 'extracts the year, month, and day into separate features. This step is '\n", - " 'included for flexibility, even if not applicable to the current dataset.\\n'\n", - " '\\n'\n", - " \"10. **Target Variable Processing**: If the target variable 'Churn' exists:\\n\"\n", - " \" - It checks the data type of 'Churn'. If it's categorical, it applies \"\n", - " 'label encoding.\\n'\n", - " \" - If it's numeric, it ensures that it remains numeric.\\n\"\n", - " ' - The target variable is then separated from the features.\\n'\n", - " '\\n'\n", - " '11. **Return Processed Data**: Finally, the function returns the engineered '\n", - " 'features as a DataFrame, and if ‘Churn’ was present, it also returns the '\n", - " 'processed target variable.\\n'\n", - " '\\n'\n", - " 'By systematically addressing missing values, removing irrelevant features, '\n", - " 'and encoding categorical variables, the function prepares the dataset for '\n", - " 'effective analysis and modeling.')\n" - ] - } - ], - "source": [ - "pprint(response['messages'][0].content)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Feature Engineer Function" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('def feature_engineer(data_raw):\\n'\n", - " ' import pandas as pd\\n'\n", - " ' import numpy as np\\n'\n", - " ' from sklearn.preprocessing import OneHotEncoder, LabelEncoder\\n'\n", - " '\\n'\n", - " '\\n'\n", - " '\\n'\n", - " ' # Make a copy of the data to avoid modifying the original dataframe\\n'\n", - " ' data = data_raw.copy()\\n'\n", - " '\\n'\n", - " ' # Handle missing values (if any)\\n'\n", - " \" data.fillna(method='ffill', inplace=True)\\n\"\n", - " '\\n'\n", - " ' # Remove string or categorical features with unique values equal to the '\n", - " 'size of the dataset\\n'\n", - " ' unique_counts = data.nunique()\\n'\n", - " ' data = data.loc[:, unique_counts < len(data)]\\n'\n", - " '\\n'\n", - " ' # Remove constant features\\n'\n", - " ' constant_features = [col for col in data.columns if data[col].nunique() '\n", - " '== 1]\\n'\n", - " ' data.drop(columns=constant_features, inplace=True)\\n'\n", - " '\\n'\n", - " ' # Identify categorical features\\n'\n", - " ' categorical_features = '\n", - " \"data.select_dtypes(include=['object']).columns.tolist()\\n\"\n", - " '\\n'\n", - " ' # Handle high cardinality categorical features\\n'\n", - " ' for col in categorical_features:\\n'\n", - " ' if data[col].nunique() > len(data) * 0.05: # More than 5% unique '\n", - " 'values\\n'\n", - " \" # Replace infrequent values with 'other'\\n\"\n", - " ' value_counts = data[col].value_counts(normalize=True)\\n'\n", - " ' to_replace = value_counts[value_counts < 0.05].index\\n'\n", - " \" data[col] = data[col].replace(to_replace, 'other')\\n\"\n", - " '\\n'\n", - " ' # OneHotEncoding for categorical variables\\n'\n", - " ' data = pd.get_dummies(data, columns=categorical_features, '\n", - " 'drop_first=True)\\n'\n", - " '\\n'\n", - " ' # Convert boolean columns\\n'\n", - " \" boolean_cols = data.select_dtypes(include=['bool']).columns.tolist()\\n\"\n", - " ' data[boolean_cols] = data[boolean_cols].astype(int)\\n'\n", - " '\\n'\n", - " ' # Convert datetime columns if present (not in this dataset, but just in '\n", - " 'case)\\n'\n", - " \" for col in data.select_dtypes(include=['datetime64']).columns:\\n\"\n", - " \" data[col + '_year'] = data[col].dt.year\\n\"\n", - " \" data[col + '_month'] = data[col].dt.month\\n\"\n", - " \" data[col + '_day'] = data[col].dt.day\\n\"\n", - " ' \\n'\n", - " \" # Handle the target variable 'Churn'\\n\"\n", - " \" if 'Churn' in data.columns:\\n\"\n", - " \" target = data['Churn']\\n\"\n", - " \" if target.dtype == 'object':\\n\"\n", - " ' # Label encode the categorical target variable\\n'\n", - " ' le = LabelEncoder()\\n'\n", - " ' target = le.fit_transform(target)\\n'\n", - " ' else:\\n'\n", - " ' # Convert to numeric (if not already)\\n'\n", - " \" target = pd.to_numeric(target, errors='coerce')\\n\"\n", - " '\\n'\n", - " ' # Drop target from features\\n'\n", - " \" data.drop(columns=['Churn'], inplace=True)\\n\"\n", - " '\\n'\n", - " ' # Return the engineered features and target variable\\n'\n", - " ' return pd.DataFrame(data), target\\n'\n", - " ' \\n'\n", - " ' return pd.DataFrame(data)\\n'\n", - " '\\n'\n", - " '# Sample usage (uncomment to run):\\n'\n", - " '# data_transformed, target_transformed = feature_engineer(data_raw)')\n" - ] - } - ], - "source": [ - "pprint(response['feature_engineer_function'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Feature Engineered Data As Pandas Data Frame" - ] - }, - { - "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -830,15 +54707,14 @@ " SeniorCitizen\n", " tenure\n", " MonthlyCharges\n", + " TotalCharges\n", + " Churn\n", " gender_Male\n", " Partner_Yes\n", " Dependents_Yes\n", " PhoneService_Yes\n", " MultipleLines_No phone service\n", - " MultipleLines_Yes\n", - " InternetService_Fiber optic\n", " ...\n", - " StreamingTV_Yes\n", " StreamingMovies_No internet service\n", " StreamingMovies_Yes\n", " Contract_One year\n", @@ -847,7 +54723,8 @@ " PaymentMethod_Credit card (automatic)\n", " PaymentMethod_Electronic check\n", " PaymentMethod_Mailed check\n", - " Churn_Yes\n", + " AvgMonthlyCharge\n", + " ServiceCount\n", " \n", " \n", " \n", @@ -856,120 +54733,120 @@ " 0\n", " 1\n", " 29.85\n", + " 29.85\n", " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", + " False\n", + " True\n", + " False\n", + " False\n", + " True\n", " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " True\n", + " False\n", + " True\n", + " False\n", + " 29.850000\n", + " True\n", " \n", " \n", " 1\n", " 0\n", " 34\n", " 56.95\n", - " 1\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", + " 1889.50\n", " 0\n", + " True\n", + " False\n", + " False\n", + " True\n", + " False\n", " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", + " False\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " True\n", + " 1.675000\n", + " True\n", " \n", " \n", " 2\n", " 0\n", " 2\n", " 53.85\n", + " 108.15\n", " 1\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", + " True\n", + " False\n", + " False\n", + " True\n", + " False\n", " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 1\n", - " 1\n", + " False\n", + " False\n", + " False\n", + " False\n", + " True\n", + " False\n", + " False\n", + " True\n", + " 26.925000\n", + " True\n", " \n", " \n", " 3\n", " 0\n", " 45\n", " 42.30\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", + " 1840.75\n", " 0\n", + " True\n", + " False\n", + " False\n", + " False\n", + " True\n", " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " 0.940000\n", + " True\n", " \n", " \n", " 4\n", " 0\n", " 2\n", " 70.70\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", + " 151.65\n", " 1\n", + " False\n", + " False\n", + " False\n", + " True\n", + " False\n", " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", + " False\n", + " False\n", + " False\n", + " False\n", + " True\n", + " False\n", + " True\n", + " False\n", + " 35.350000\n", + " False\n", " \n", " \n", " ...\n", @@ -1000,228 +54877,383 @@ " 0\n", " 24\n", " 84.80\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", + " 1990.50\n", " 0\n", + " True\n", + " True\n", + " True\n", + " True\n", + " False\n", " ...\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", + " False\n", + " True\n", + " True\n", + " False\n", + " True\n", + " False\n", + " False\n", + " True\n", + " 3.533333\n", + " True\n", " \n", " \n", " 7039\n", " 0\n", " 72\n", " 103.20\n", + " 7362.90\n", " 0\n", - " 1\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", + " False\n", + " True\n", + " True\n", + " True\n", + " False\n", " ...\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " True\n", + " True\n", + " False\n", + " True\n", + " True\n", + " False\n", + " False\n", + " 1.433333\n", + " True\n", " \n", " \n", " 7040\n", " 0\n", " 11\n", " 29.60\n", + " 346.45\n", " 0\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", + " False\n", + " True\n", + " True\n", + " False\n", + " True\n", " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " True\n", + " False\n", + " True\n", + " False\n", + " 2.690909\n", + " True\n", " \n", " \n", " 7041\n", " 1\n", " 4\n", " 74.40\n", + " 306.60\n", " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", + " True\n", + " True\n", + " False\n", + " True\n", + " False\n", " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 1\n", - " 1\n", + " False\n", + " False\n", + " False\n", + " False\n", + " True\n", + " False\n", + " False\n", + " True\n", + " 18.600000\n", + " False\n", " \n", " \n", " 7042\n", " 0\n", " 66\n", " 105.65\n", - " 1\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", + " 6844.50\n", " 0\n", - " 1\n", + " True\n", + " False\n", + " False\n", + " True\n", + " False\n", " ...\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " True\n", + " False\n", + " True\n", + " True\n", + " False\n", + " False\n", + " False\n", + " 1.600758\n", + " True\n", " \n", " \n", "\n", - "

7043 rows × 30 columns

\n", + "

7043 rows × 33 columns

\n", "" ], "text/plain": [ - " SeniorCitizen tenure MonthlyCharges gender_Male Partner_Yes \\\n", - "0 0 1 29.85 0 1 \n", - "1 0 34 56.95 1 0 \n", - "2 0 2 53.85 1 0 \n", - "3 0 45 42.30 1 0 \n", - "4 0 2 70.70 0 0 \n", - "... ... ... ... ... ... \n", - "7038 0 24 84.80 1 1 \n", - "7039 0 72 103.20 0 1 \n", - "7040 0 11 29.60 0 1 \n", - "7041 1 4 74.40 1 1 \n", - "7042 0 66 105.65 1 0 \n", + " SeniorCitizen tenure MonthlyCharges TotalCharges Churn gender_Male \\\n", + "0 0 1 29.85 29.85 0 False \n", + "1 0 34 56.95 1889.50 0 True \n", + "2 0 2 53.85 108.15 1 True \n", + "3 0 45 42.30 1840.75 0 True \n", + "4 0 2 70.70 151.65 1 False \n", + "... ... ... ... ... ... ... \n", + "7038 0 24 84.80 1990.50 0 True \n", + "7039 0 72 103.20 7362.90 0 False \n", + "7040 0 11 29.60 346.45 0 False \n", + "7041 1 4 74.40 306.60 1 True \n", + "7042 0 66 105.65 6844.50 0 True \n", "\n", - " Dependents_Yes PhoneService_Yes MultipleLines_No phone service \\\n", - "0 0 0 1 \n", - "1 0 1 0 \n", - "2 0 1 0 \n", - "3 0 0 1 \n", - "4 0 1 0 \n", - "... ... ... ... \n", - "7038 1 1 0 \n", - "7039 1 1 0 \n", - "7040 1 0 1 \n", - "7041 0 1 0 \n", - "7042 0 1 0 \n", + " Partner_Yes Dependents_Yes PhoneService_Yes \\\n", + "0 True False False \n", + "1 False False True \n", + "2 False False True \n", + "3 False False False \n", + "4 False False True \n", + "... ... ... ... \n", + "7038 True True True \n", + "7039 True True True \n", + "7040 True True False \n", + "7041 True False True \n", + "7042 False False True \n", "\n", - " MultipleLines_Yes InternetService_Fiber optic ... StreamingTV_Yes \\\n", - "0 0 0 ... 0 \n", - "1 0 0 ... 0 \n", - "2 0 0 ... 0 \n", - "3 0 0 ... 0 \n", - "4 0 1 ... 0 \n", - "... ... ... ... ... \n", - "7038 1 0 ... 1 \n", - "7039 1 1 ... 1 \n", - "7040 0 0 ... 0 \n", - "7041 1 1 ... 0 \n", - "7042 0 1 ... 1 \n", + " MultipleLines_No phone service ... \\\n", + "0 True ... \n", + "1 False ... \n", + "2 False ... \n", + "3 True ... \n", + "4 False ... \n", + "... ... ... \n", + "7038 False ... \n", + "7039 False ... \n", + "7040 True ... \n", + "7041 False ... \n", + "7042 False ... \n", "\n", " StreamingMovies_No internet service StreamingMovies_Yes \\\n", - "0 0 0 \n", - "1 0 0 \n", - "2 0 0 \n", - "3 0 0 \n", - "4 0 0 \n", + "0 False False \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 False False \n", "... ... ... \n", - "7038 0 1 \n", - "7039 0 1 \n", - "7040 0 0 \n", - "7041 0 0 \n", - "7042 0 1 \n", + "7038 False True \n", + "7039 False True \n", + "7040 False False \n", + "7041 False False \n", + "7042 False True \n", "\n", " Contract_One year Contract_Two year PaperlessBilling_Yes \\\n", - "0 0 0 1 \n", - "1 1 0 0 \n", - "2 0 0 1 \n", - "3 1 0 0 \n", - "4 0 0 1 \n", + "0 False False True \n", + "1 True False False \n", + "2 False False True \n", + "3 True False False \n", + "4 False False True \n", "... ... ... ... \n", - "7038 1 0 1 \n", - "7039 1 0 1 \n", - "7040 0 0 1 \n", - "7041 0 0 1 \n", - "7042 0 1 1 \n", + "7038 True False True \n", + "7039 True False True \n", + "7040 False False True \n", + "7041 False False True \n", + "7042 False True True \n", "\n", " PaymentMethod_Credit card (automatic) PaymentMethod_Electronic check \\\n", - "0 0 1 \n", - "1 0 0 \n", - "2 0 0 \n", - "3 0 0 \n", - "4 0 1 \n", + "0 False True \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 False True \n", "... ... ... \n", - "7038 0 0 \n", - "7039 1 0 \n", - "7040 0 1 \n", - "7041 0 0 \n", - "7042 0 0 \n", + "7038 False False \n", + "7039 True False \n", + "7040 False True \n", + "7041 False False \n", + "7042 False False \n", + "\n", + " PaymentMethod_Mailed check AvgMonthlyCharge ServiceCount \n", + "0 False 29.850000 True \n", + "1 True 1.675000 True \n", + "2 True 26.925000 True \n", + "3 False 0.940000 True \n", + "4 False 35.350000 False \n", + "... ... ... ... \n", + "7038 True 3.533333 True \n", + "7039 False 1.433333 True \n", + "7040 False 2.690909 True \n", + "7041 True 18.600000 False \n", + "7042 False 1.600758 True \n", + "\n", + "[7043 rows x 33 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_engineering_agent.get_data_engineered()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Feature Engineer Function" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "```python\n", + "# Disclaimer: This function was generated by AI. Please review before using.\n", + "# Agent Name: feature_engineering_agent\n", + "# Time Created: 2025-01-09 22:36:39\n", + "\n", + "def feature_engineer(data_raw):\n", + " import pandas as pd\n", + " import numpy as np\n", + " from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n", + "\n", + "\n", + "\n", + " # Step 1: Convert Data Types\n", + " data_raw['TotalCharges'] = pd.to_numeric(data_raw['TotalCharges'], errors='coerce')\n", + " data_raw['Churn'] = data_raw['Churn'].astype('category')\n", + "\n", + " # Step 2: Remove Unique Value Features\n", + " data_raw.drop(columns=['customerID'], inplace=True)\n", + "\n", + " # Step 3: Remove Constant Features\n", + " constant_columns = [col for col in data_raw.columns if data_raw[col].nunique() <= 1]\n", + " data_raw.drop(columns=constant_columns, inplace=True)\n", + "\n", + " # Step 4: High Cardinality Categorical Encoding\n", + " high_cardinality_features = ['InternetService', 'Contract', 'PaymentMethod']\n", + " for col in high_cardinality_features:\n", + " if data_raw[col].nunique() / len(data_raw) > 0.05:\n", + " # Consolidate infrequent categories into \"Other\"\n", + " value_counts = data_raw[col].value_counts(normalize=True)\n", + " threshold = 0.05\n", + " to_replace = value_counts[value_counts < threshold].index\n", + " data_raw[col].replace(to_replace, 'Other', inplace=True)\n", + "\n", + " # Step 5: One-Hot Encoding\n", + " categorical_features = ['gender', 'Partner', 'Dependents', 'PhoneService', \n", + " 'MultipleLines', 'InternetService', 'OnlineSecurity', \n", + " 'OnlineBackup', 'DeviceProtection', 'TechSupport', \n", + " 'StreamingTV', 'StreamingMovies', 'Contract', \n", + " 'PaperlessBilling', 'PaymentMethod']\n", + " \n", + " data_encoded = pd.get_dummies(data_raw, columns=categorical_features, drop_first=True)\n", + "\n", + " # Step 6: Convert Boolean to Integer\n", + " data_encoded['SeniorCitizen'] = data_encoded['SeniorCitizen'].astype(int)\n", + " data_encoded['Churn'] = data_encoded['Churn'].cat.codes # Encode categorical target variable\n", + "\n", + " # Step 9: Analyze for Additional Features\n", + " data_encoded['AvgMonthlyCharge'] = data_encoded['MonthlyCharges'] / data_encoded['tenure'].replace(0, np.nan)\n", + " data_encoded['ServiceCount'] = (data_encoded['OnlineSecurity_Yes'].fillna(0) + \n", + " data_encoded['OnlineBackup_Yes'].fillna(0) + \n", + " data_encoded['DeviceProtection_Yes'].fillna(0) + \n", + " data_encoded['TechSupport_Yes'].fillna(0) + \n", + " data_encoded['StreamingTV_Yes'].fillna(0) + \n", + " data_encoded['StreamingMovies_Yes'].fillna(0))\n", "\n", - " PaymentMethod_Mailed check Churn_Yes \n", - "0 0 0 \n", - "1 1 0 \n", - "2 1 1 \n", - "3 0 0 \n", - "4 0 1 \n", - "... ... ... \n", - "7038 1 0 \n", - "7039 0 0 \n", - "7040 0 0 \n", - "7041 1 1 \n", - "7042 0 0 \n", + " # Step 10: Final Review\n", + " # Review for any additional transformations (if needed)\n", "\n", - "[7043 rows x 30 columns]" + " return data_encoded\n", + "```" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_engineering_agent.get_feature_engineer_function(markdown=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Feature Engineeering Recommended Steps" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "\n", + "# Recommended Feature Engineering Steps:\n", + "1. **Convert Data Types**: \n", + " - Convert `TotalCharges` from object to float, as it represents numerical values.\n", + " - Ensure `Churn` is treated as a categorical variable for encoding.\n", + "\n", + "2. **Remove Unique Value Features**: \n", + " - Remove `customerID` as it has unique values equal to the size of the dataset (7043).\n", + "\n", + "3. **Remove Constant Features**: \n", + " - Check for any columns that have the same value across all rows and remove them if found. \n", + "\n", + "4. **High Cardinality Categorical Encoding**: \n", + " - Analyze categorical features for high cardinality. For features like `InternetService`, `Contract`, and `PaymentMethod`, if they have more than 5% unique values, consolidate infrequent categories into the \"other\" category.\n", + "\n", + "5. **One-Hot Encoding**: \n", + " - Apply One-Hot Encoding to categorical features: `gender`, `Partner`, `Dependents`, `PhoneService`, `MultipleLines`, `InternetService`, `OnlineSecurity`, `OnlineBackup`, `DeviceProtection`, `TechSupport`, `StreamingTV`, `StreamingMovies`, `Contract`, `PaperlessBilling`, and `PaymentMethod`.\n", + "\n", + "6. **Convert Boolean to Integer**: \n", + " - Convert `SeniorCitizen`, `Churn`, and any categorical features that represent binary values into integers (1 for True, 0 for False) after one-hot encoding.\n", + "\n", + "7. **Create Datetime Features**: \n", + " - If there are any datetime columns in future data, create relevant features such as year, month, day, day of the week, etc. (not applicable here based on provided data).\n", + "\n", + "8. **Encode Target Variable**: \n", + " - If `Churn` is categorical, apply Label Encoding to convert it into numeric form (0 and 1).\n", + "\n", + "9. **Analyze for Additional Features**: \n", + " - Examine existing features for potential new feature creation, such as:\n", + " - Creating a feature for `MonthlyCharges` divided by `tenure` to understand the average monthly charge per period.\n", + " - Creating a feature that sums the number of services subscribed (e.g., OnlineSecurity, OnlineBackup, etc.) to gauge customer engagement.\n", + "\n", + "10. **Final Review**: \n", + " - Review the newly engineered features for any additional transformations or combinations that could enhance predictive performance before finalizing the dataset. \n", + "\n", + "No additional steps are required beyond those outlined above." + ], + "text/plain": [ + "" ] }, - "execution_count": 18, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.DataFrame(response['data_engineered'])\n" + "feature_engineering_agent.get_recommended_feature_engineering_steps(markdown=True)" ] }, {