Spark DF, SQL, ML Exercise (1).ipynb

{"cells":[{"cell_type":"markdown","source":["##Exercise Overview\nIn this exercise we will play with Spark [Datasets & Dataframes](https://spark.apache.org/docs/latest/sql-programming-guide.html#datasets-and-dataframes), some [Spark SQL](https://spark.apache.org/docs/latest/sql-programming-guide.html#sql), and build a couple of binary classifiaction models using [Spark ML](https://spark.apache.org/docs/latest/ml-guide.html) (with some [MLlib](https://spark.apache.org/mllib/) too). \n<br><br>\nThe set up and approach will not be too dissimilar to the standard type of approach you might do in [Sklearn](http://scikit-learn.org/stable/index.html). Spark has matured to the stage now where for 90% of what you need to do (when analysing tabular data) should be possible with Spark dataframes, SQL, and ML libraries. This is where this exercise is mainly trying to focus.  \n<br>\nFeel free to adapt this exercise to play with other datasets readily availabe in the Databricks enviornment (they are listed in a cell below). \n#####Getting Started\nTo get started you will need to create and attach a databricks spark cluster to this notebook. This notebook was developed on a cluster created with: \n- Databricks Runtime Version 4.0 (includes Apache Spark 2.3.0, Scala 2.11)\n- Python Version 3\n\n#####Links & References\n\nSome useful links and references of sources used in creating this exercise:\n\n**Note**: Right click and open as new tab!\n<br>\n1. [Latest Spark Docs](https://spark.apache.org/docs/latest/index.html)\n1. [Databricks Homepage](https://databricks.com/)\n1. [Databricks Community Edition FAQ](https://databricks.com/product/faq/community-edition)\n1. [Databricks Self Paced Training](https://databricks.com/training-overview/training-self-paced)\n1. [Databricks Notebook Guide](https://docs.databricks.com/user-guide/notebooks/index.html)\n1. [Databricks Binary Classification Tutorial](https://docs.databricks.com/spark/latest/mllib/binary-classification-mllib-pipelines.html#binary-classification)"],"metadata":{}},{"cell_type":"markdown","source":["####Get Data\n\nHere we will pull in some sample data that is already pre-loaded onto all databricks clusters.\n\nFeel free to adapt this notebook later to play around with a different dataset if you like (all available are listed in a cell below)."],"metadata":{}},{"cell_type":"code","source":["# display datasets already in databricks\ndisplay(dbutils.fs.ls(\"/databricks-datasets\"))"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>path</th><th>name</th><th>size</th></tr></thead><tbody><tr><td>dbfs:/databricks-datasets/README.md</td><td>README.md</td><td>976</td></tr><tr><td>dbfs:/databricks-datasets/Rdatasets/</td><td>Rdatasets/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/SPARK_README.md</td><td>SPARK_README.md</td><td>3359</td></tr><tr><td>dbfs:/databricks-datasets/adult/</td><td>adult/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/airlines/</td><td>airlines/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/amazon/</td><td>amazon/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/asa/</td><td>asa/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/atlas_higgs/</td><td>atlas_higgs/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/bikeSharing/</td><td>bikeSharing/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/cctvVideos/</td><td>cctvVideos/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/credit-card-fraud/</td><td>credit-card-fraud/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/cs100/</td><td>cs100/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/cs110x/</td><td>cs110x/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/cs190/</td><td>cs190/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/data.gov/</td><td>data.gov/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/definitive-guide/</td><td>definitive-guide/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/flights/</td><td>flights/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/genomics/</td><td>genomics/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/hail/</td><td>hail/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/iot/</td><td>iot/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/iot-stream/</td><td>iot-stream/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/learning-spark/</td><td>learning-spark/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/lending-club-loan-stats/</td><td>lending-club-loan-stats/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/mnist-digits/</td><td>mnist-digits/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/news20.binary/</td><td>news20.binary/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/online_retail/</td><td>online_retail/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/overlap-join/</td><td>overlap-join/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/power-plant/</td><td>power-plant/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/sample_logs/</td><td>sample_logs/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/samples/</td><td>samples/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/sfo_customer_survey/</td><td>sfo_customer_survey/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/sms_spam_collection/</td><td>sms_spam_collection/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/songs/</td><td>songs/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/structured-streaming/</td><td>structured-streaming/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/tpch/</td><td>tpch/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/weather/</td><td>weather/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/wiki/</td><td>wiki/</td><td>0</td></tr><tr><td>dbfs:/databricks-datasets/wikipedia-datasets/</td><td>wikipedia-datasets/</td><td>0</td></tr></tbody></table></div>"]}}],"execution_count":3},{"cell_type":"markdown","source":["Lets take a look at the '**adult**' dataset on the filesystem. This is the typical US Census data you often see online in tutorials. [Here](https://archive.ics.uci.edu/ml/datasets/adult) is the same data in the UCI repository.\n\n_As an aside: [here](https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/census) this same dataset is used as a quickstart example for Google CLoud ML & Tensorflow Estimator API (in case youd be interested in playing with tensorflow on the same dataset as here)._"],"metadata":{}},{"cell_type":"code","source":["%fs ls databricks-datasets/adult/adult.data"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>path</th><th>name</th><th>size</th></tr></thead><tbody><tr><td>dbfs:/databricks-datasets/adult/adult.data</td><td>adult.data</td><td>3974305</td></tr></tbody></table></div>"]}}],"execution_count":5},{"cell_type":"markdown","source":["**Note**: Above  %fs is just some file system cell magic that is specific to databricks. More info [here](https://docs.databricks.com/user-guide/notebooks/index.html#mix-languages)."],"metadata":{}},{"cell_type":"markdown","source":["####Spark SQL\nBelow we will use Spark SQL to load in the data and then register it as a Dataframe aswell. So the end result will be a Spark SQL table called _adult_ and a Spark Dataframe called _df_adult_. \n<br><br>\nThis is an example of the flexibility in Spark in that you could do lots of you ETL and data wrangling using either Spark SQL or Dataframes and pyspark. Most of the time it's a case of using whatever you are most comfortable with.\n<br><br>\nWhen you get more advanced then you might looking the pro's and con's of each and when you might favour one or the other (or operating direclty on RDD's), [here](https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-apis-rdds-dataframes-and-datasets.html) is a good article on the issues. For now, no need to overthink it!"],"metadata":{}},{"cell_type":"code","source":["%sql \n-- drop the table if it already exists\nDROP TABLE IF EXISTS adult"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr></tr></thead><tbody></tbody></table></div>"]}}],"execution_count":8},{"cell_type":"code","source":["%sql\n-- create a new table in Spark SQL from the datasets already loaded in the underlying filesystem.\n-- In the real world you might be pointing at a file on HDFS or a hive table etc. \nCREATE TABLE adult (\n  age DOUBLE,\n  workclass STRING,\n  fnlwgt DOUBLE,\n  education STRING,\n  education_num DOUBLE,\n  marital_status STRING,\n  occupation STRING,\n  relationship STRING,\n  race STRING,\n  sex STRING,\n  capital_gain DOUBLE,\n  capital_loss DOUBLE,\n  hours_per_week DOUBLE,\n  native_country STRING,\n  income STRING)\nUSING com.databricks.spark.csv\nOPTIONS (path \"/databricks-datasets/adult/adult.data\", header \"true\")"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr></tr></thead><tbody></tbody></table></div>"]}}],"execution_count":9},{"cell_type":"code","source":["# look at the data\n#spark.sql(\"SELECT * FROM adult LIMIT 5\").show() \n# this will look prettier in Databricks if you use display() instead\ndisplay(spark.sql(\"SELECT * FROM adult LIMIT 5\"))"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>age</th><th>workclass</th><th>fnlwgt</th><th>education</th><th>education_num</th><th>marital_status</th><th>occupation</th><th>relationship</th><th>race</th><th>sex</th><th>capital_gain</th><th>capital_loss</th><th>hours_per_week</th><th>native_country</th><th>income</th></tr></thead><tbody><tr><td>50.0</td><td> Self-emp-not-inc</td><td>83311.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>13.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>215646.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Handlers-cleaners</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>53.0</td><td> Private</td><td>234721.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> Handlers-cleaners</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>338409.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Wife</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Cuba</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>284582.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr></tbody></table></div>"]}}],"execution_count":10},{"cell_type":"markdown","source":["If you are more comfortable with SQL then as you can see below, its very easy to just get going with writing standard SQL type code to analyse your data, do data wrangling and create new dataframes."],"metadata":{}},{"cell_type":"code","source":["# Lets get some summary marital status rates by occupation\nresult = spark.sql(\n  \"\"\"\n  SELECT \n    occupation,\n    SUM(1) as n,\n    ROUND(AVG(if(LTRIM(marital_status) LIKE 'Married-%',1,0)),2) as married_rate,\n    ROUND(AVG(if(lower(marital_status) LIKE '%widow%',1,0)),2) as widow_rate,\n    ROUND(AVG(if(LTRIM(marital_status) = 'Divorced',1,0)),2) as divorce_rate,\n    ROUND(AVG(if(LTRIM(marital_status) = 'Separated',1,0)),2) as separated_rate,\n    ROUND(AVG(if(LTRIM(marital_status) = 'Never-married',1,0)),2) as bachelor_rate\n  FROM \n    adult \n  GROUP BY 1\n  ORDER BY n DESC\n  \"\"\")\ndisplay(result)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>occupation</th><th>n</th><th>married_rate</th><th>widow_rate</th><th>divorce_rate</th><th>separated_rate</th><th>bachelor_rate</th></tr></thead><tbody><tr><td> Prof-specialty</td><td>4140</td><td>0.53</td><td>0.02</td><td>0.13</td><td>0.02</td><td>0.3</td></tr><tr><td> Craft-repair</td><td>4099</td><td>0.64</td><td>0.01</td><td>0.11</td><td>0.03</td><td>0.21</td></tr><tr><td> Exec-managerial</td><td>4066</td><td>0.61</td><td>0.02</td><td>0.15</td><td>0.02</td><td>0.2</td></tr><tr><td> Adm-clerical</td><td>3769</td><td>0.28</td><td>0.04</td><td>0.22</td><td>0.04</td><td>0.42</td></tr><tr><td> Sales</td><td>3650</td><td>0.47</td><td>0.03</td><td>0.12</td><td>0.03</td><td>0.36</td></tr><tr><td> Other-service</td><td>3295</td><td>0.24</td><td>0.05</td><td>0.15</td><td>0.06</td><td>0.5</td></tr><tr><td> Machine-op-inspct</td><td>2002</td><td>0.51</td><td>0.03</td><td>0.14</td><td>0.04</td><td>0.29</td></tr><tr><td> ?</td><td>1843</td><td>0.36</td><td>0.08</td><td>0.1</td><td>0.04</td><td>0.42</td></tr><tr><td> Transport-moving</td><td>1597</td><td>0.63</td><td>0.02</td><td>0.11</td><td>0.02</td><td>0.21</td></tr><tr><td> Handlers-cleaners</td><td>1370</td><td>0.36</td><td>0.01</td><td>0.09</td><td>0.03</td><td>0.51</td></tr><tr><td> Farming-fishing</td><td>994</td><td>0.6</td><td>0.02</td><td>0.06</td><td>0.02</td><td>0.29</td></tr><tr><td> Tech-support</td><td>928</td><td>0.44</td><td>0.02</td><td>0.15</td><td>0.03</td><td>0.36</td></tr><tr><td> Protective-serv</td><td>649</td><td>0.6</td><td>0.01</td><td>0.12</td><td>0.02</td><td>0.24</td></tr><tr><td> Priv-house-serv</td><td>149</td><td>0.13</td><td>0.15</td><td>0.19</td><td>0.08</td><td>0.45</td></tr><tr><td> Armed-Forces</td><td>9</td><td>0.33</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.67</td></tr></tbody></table></div>"]}}],"execution_count":12},{"cell_type":"markdown","source":["You can easily register dataframes as a table for Spark SQL too. So this way you can easily move between Dataframes and Spark SQL for whatever reason."],"metadata":{}},{"cell_type":"code","source":["# register the df we just made as a table for spark sql\nsqlContext.registerDataFrameAsTable(result, \"result\")\nspark.sql(\"SELECT * FROM result\").show(5)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">+----------------+----+------------+----------+------------+--------------+-------------+\n      occupation|   n|married_rate|widow_rate|divorce_rate|separated_rate|bachelor_rate|\n+----------------+----+------------+----------+------------+--------------+-------------+\n  Prof-specialty|4140|        0.53|      0.02|        0.13|          0.02|          0.3|\n    Craft-repair|4099|        0.64|      0.01|        0.11|          0.03|         0.21|\n Exec-managerial|4066|        0.61|      0.02|        0.15|          0.02|          0.2|\n    Adm-clerical|3769|        0.28|      0.04|        0.22|          0.04|         0.42|\n           Sales|3650|        0.47|      0.03|        0.12|          0.03|         0.36|\n+----------------+----+------------+----------+------------+--------------+-------------+\nonly showing top 5 rows\n\n</div>"]}}],"execution_count":14},{"cell_type":"markdown","source":["####<span style=\"color:darkblue\">Question 1</span>\n\n1. Write some spark sql to get the top 'bachelor_rate' by 'education' group?"],"metadata":{}},{"cell_type":"code","source":["### Question 1.1 Answer ###\nresult = spark.sql(\n  \"\"\"\n  SELECT\n    education,\n    SUM(1) as n,\n    ROUND(AVG(if(LTRIM(marital_status) = 'Never-married',1,0)),2) as bachelor_rate\n  FROM \n    adult \n  GROUP BY 1\n  ORDER BY bachelor_rate DESC\n  \"\"\")\n\nresult.show()"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">+-------------+-----+-------------+\n    education|    n|bachelor_rate|\n+-------------+-----+-------------+\n         12th|  433|         0.54|\n         11th| 1175|          0.5|\n    Preschool|   51|         0.43|\n Some-college| 7291|          0.4|\n         10th|  933|         0.39|\n    Bachelors| 5354|         0.34|\n   Assoc-acdm| 1067|         0.32|\n          9th|  514|          0.3|\n      HS-grad|10501|         0.29|\n      5th-6th|  333|         0.27|\n    Assoc-voc| 1382|         0.26|\n      1st-4th|  168|         0.23|\n      Masters| 1723|         0.23|\n    Doctorate|  413|         0.18|\n      7th-8th|  646|         0.17|\n  Prof-school|  576|         0.16|\n+-------------+-----+-------------+\n\n</div>"]}}],"execution_count":16},{"cell_type":"markdown","source":["####Spark DataFrames\nBelow we will create our DataFrame from the SQL table and do some similar analysis as we did with Spark SQL but using the DataFrames API."],"metadata":{}},{"cell_type":"code","source":["# register a df from the sql df\ndf_adult = spark.table(\"adult\")\ncols = df_adult.columns # this will be used much later in the notebook, ignore for now"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":18},{"cell_type":"code","source":["# look at df schema\ndf_adult.printSchema()"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">root\n-- age: double (nullable = true)\n-- workclass: string (nullable = true)\n-- fnlwgt: double (nullable = true)\n-- education: string (nullable = true)\n-- education_num: double (nullable = true)\n-- marital_status: string (nullable = true)\n-- occupation: string (nullable = true)\n-- relationship: string (nullable = true)\n-- race: string (nullable = true)\n-- sex: string (nullable = true)\n-- capital_gain: double (nullable = true)\n-- capital_loss: double (nullable = true)\n-- hours_per_week: double (nullable = true)\n-- native_country: string (nullable = true)\n-- income: string (nullable = true)\n\n</div>"]}}],"execution_count":19},{"cell_type":"code","source":["# look at the df\ndisplay(df_adult)\n#df_adult.show(5)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>age</th><th>workclass</th><th>fnlwgt</th><th>education</th><th>education_num</th><th>marital_status</th><th>occupation</th><th>relationship</th><th>race</th><th>sex</th><th>capital_gain</th><th>capital_loss</th><th>hours_per_week</th><th>native_country</th><th>income</th></tr></thead><tbody><tr><td>50.0</td><td> Self-emp-not-inc</td><td>83311.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>13.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>215646.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Handlers-cleaners</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>53.0</td><td> Private</td><td>234721.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> Handlers-cleaners</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>338409.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Wife</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Cuba</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>284582.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Private</td><td>160187.0</td><td> 9th</td><td>5.0</td><td> Married-spouse-absent</td><td> Other-service</td><td> Not-in-family</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>16.0</td><td> Jamaica</td><td> <=50K</td></tr><tr><td>52.0</td><td> Self-emp-not-inc</td><td>209642.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>31.0</td><td> Private</td><td>45781.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>14084.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>42.0</td><td> Private</td><td>159449.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>5178.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>37.0</td><td> Private</td><td>280464.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>80.0</td><td> United-States</td><td> >50K</td></tr><tr><td>30.0</td><td> State-gov</td><td>141297.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> India</td><td> >50K</td></tr><tr><td>23.0</td><td> Private</td><td>122272.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Adm-clerical</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>205019.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> Private</td><td>121772.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> ?</td><td> >50K</td></tr><tr><td>34.0</td><td> Private</td><td>245487.0</td><td> 7th-8th</td><td>4.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> Amer-Indian-Eskimo</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>25.0</td><td> Self-emp-not-inc</td><td>176756.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Farming-fishing</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>186824.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>28887.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Self-emp-not-inc</td><td>292175.0</td><td> Masters</td><td>14.0</td><td> Divorced</td><td> Exec-managerial</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>40.0</td><td> Private</td><td>193524.0</td><td> Doctorate</td><td>16.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>54.0</td><td> Private</td><td>302146.0</td><td> HS-grad</td><td>9.0</td><td> Separated</td><td> Other-service</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Federal-gov</td><td>76845.0</td><td> 9th</td><td>5.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>117037.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>2042.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>59.0</td><td> Private</td><td>109015.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Tech-support</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>56.0</td><td> Local-gov</td><td>216851.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>19.0</td><td> Private</td><td>168294.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>54.0</td><td> ?</td><td>180211.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> South</td><td> >50K</td></tr><tr><td>39.0</td><td> Private</td><td>367260.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>80.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Private</td><td>193366.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Local-gov</td><td>190709.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> Protective-serv</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>52.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> Private</td><td>266015.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>44.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>45.0</td><td> Private</td><td>386940.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Exec-managerial</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>1408.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Federal-gov</td><td>59951.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> State-gov</td><td>311512.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>48.0</td><td> Private</td><td>242406.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Puerto-Rico</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>197200.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> Private</td><td>544091.0</td><td> HS-grad</td><td>9.0</td><td> Married-AF-spouse</td><td> Adm-clerical</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>84154.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>38.0</td><td> ?</td><td> >50K</td></tr><tr><td>48.0</td><td> Self-emp-not-inc</td><td>265477.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>507875.0</td><td> 9th</td><td>5.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>43.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>53.0</td><td> Self-emp-not-inc</td><td>88506.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>172987.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Private</td><td>94638.0</td><td> HS-grad</td><td>9.0</td><td> Separated</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>289980.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>57.0</td><td> Federal-gov</td><td>337895.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>53.0</td><td> Private</td><td>144361.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>128354.0</td><td> Masters</td><td>14.0</td><td> Divorced</td><td> Exec-managerial</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> State-gov</td><td>101603.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>271466.0</td><td> Assoc-voc</td><td>11.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>43.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>32275.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Wife</td><td> Other</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> Private</td><td>226956.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>30.0</td><td> ?</td><td> <=50K</td></tr><tr><td>47.0</td><td> Private</td><td>51835.0</td><td> Prof-school</td><td>15.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>1902.0</td><td>60.0</td><td> Honduras</td><td> >50K</td></tr><tr><td>50.0</td><td> Federal-gov</td><td>251585.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> >50K</td></tr><tr><td>47.0</td><td> Self-emp-inc</td><td>109832.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>237993.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>46.0</td><td> Private</td><td>216666.0</td><td> 5th-6th</td><td>3.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>56352.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Puerto-Rico</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>147372.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>48.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>188146.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>5013.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>59496.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>2407.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> ?</td><td>293936.0</td><td> 7th-8th</td><td>4.0</td><td> Married-spouse-absent</td><td> ?</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> ?</td><td> <=50K</td></tr><tr><td>48.0</td><td> Private</td><td>149640.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Private</td><td>116632.0</td><td> Doctorate</td><td>16.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>29.0</td><td> Private</td><td>105598.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>58.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>155537.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>183175.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>53.0</td><td> Private</td><td>169846.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>49.0</td><td> Self-emp-inc</td><td>191681.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>25.0</td><td> ?</td><td>200681.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> Private</td><td>101509.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Prof-specialty</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>32.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>309974.0</td><td> Bachelors</td><td>13.0</td><td> Separated</td><td> Sales</td><td> Own-child</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Self-emp-not-inc</td><td>162298.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>70.0</td><td> United-States</td><td> >50K</td></tr><tr><td>23.0</td><td> Private</td><td>211678.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>79.0</td><td> Private</td><td>124744.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>213921.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>40.0</td><td> Private</td><td>32214.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>67.0</td><td> ?</td><td>212759.0</td><td> 10th</td><td>6.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>2.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> Private</td><td>309634.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>22.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Local-gov</td><td>125927.0</td><td> 7th-8th</td><td>4.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> Private</td><td>446839.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>52.0</td><td> Private</td><td>276515.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Cuba</td><td> <=50K</td></tr><tr><td>46.0</td><td> Private</td><td>51618.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>59.0</td><td> Private</td><td>159937.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>48.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>343591.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Female</td><td>14344.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>53.0</td><td> Private</td><td>346253.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Sales</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Local-gov</td><td>268234.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Protective-serv</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>33.0</td><td> Private</td><td>202051.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>54334.0</td><td> 9th</td><td>5.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Federal-gov</td><td>410867.0</td><td> Doctorate</td><td>16.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>57.0</td><td> Private</td><td>249977.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>286730.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Craft-repair</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>212563.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Machine-op-inspct</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>117747.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Wife</td><td> Asian-Pac-Islander</td><td> Female</td><td>0.0</td><td>1573.0</td><td>35.0</td><td> ?</td><td> <=50K</td></tr><tr><td>34.0</td><td> Local-gov</td><td>226296.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Protective-serv</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>29.0</td><td> Local-gov</td><td>115585.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>48.0</td><td> Self-emp-not-inc</td><td>191277.0</td><td> Doctorate</td><td>16.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1902.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>37.0</td><td> Private</td><td>202683.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>48.0</td><td> United-States</td><td> >50K</td></tr><tr><td>48.0</td><td> Private</td><td>171095.0</td><td> Assoc-acdm</td><td>12.0</td><td> Divorced</td><td> Exec-managerial</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> England</td><td> <=50K</td></tr><tr><td>32.0</td><td> Federal-gov</td><td>249409.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>76.0</td><td> Private</td><td>124191.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>44.0</td><td> Private</td><td>198282.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>15024.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>47.0</td><td> Self-emp-not-inc</td><td>149116.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> Private</td><td>188300.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Tech-support</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>103432.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Self-emp-inc</td><td>317660.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>7688.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>17.0</td><td> ?</td><td>304873.0</td><td> 10th</td><td>6.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Female</td><td>34095.0</td><td>0.0</td><td>32.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>194901.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Local-gov</td><td>189265.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Private</td><td>124692.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Handlers-cleaners</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>432376.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Sales</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>65324.0</td><td> Prof-school</td><td>15.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>56.0</td><td> Self-emp-not-inc</td><td>335605.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1887.0</td><td>50.0</td><td> Canada</td><td> >50K</td></tr><tr><td>28.0</td><td> Private</td><td>377869.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Wife</td><td> White</td><td> Female</td><td>4064.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>102864.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>53.0</td><td> Private</td><td>95647.0</td><td> 9th</td><td>5.0</td><td> Married-civ-spouse</td><td> Handlers-cleaners</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>56.0</td><td> Self-emp-inc</td><td>303090.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Local-gov</td><td>197371.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>55.0</td><td> Private</td><td>247552.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>56.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> Private</td><td>102632.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>41.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>199915.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> Private</td><td>118853.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>77143.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Exec-managerial</td><td> Own-child</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Germany</td><td> <=50K</td></tr><tr><td>29.0</td><td> State-gov</td><td>267989.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>19.0</td><td> Private</td><td>301606.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>47.0</td><td> Private</td><td>287828.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>20.0</td><td> Private</td><td>111697.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>1719.0</td><td>28.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>114937.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>35.0</td><td> ?</td><td>129305.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>39.0</td><td> Private</td><td>365739.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>69621.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>43323.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>1762.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Self-emp-not-inc</td><td>120985.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>4386.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>254202.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Private</td><td>146195.0</td><td> Assoc-acdm</td><td>12.0</td><td> Divorced</td><td> Tech-support</td><td> Not-in-family</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>36.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Federal-gov</td><td>125933.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Iran</td><td> >50K</td></tr><tr><td>43.0</td><td> Self-emp-not-inc</td><td>56920.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>163127.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> Private</td><td>34310.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Private</td><td>81973.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>61.0</td><td> Self-emp-inc</td><td>66614.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>232782.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> Private</td><td>316868.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>45.0</td><td> Private</td><td>196584.0</td><td> Assoc-voc</td><td>11.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>1564.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>70.0</td><td> Private</td><td>105376.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Tech-support</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>185814.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Transport-moving</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> Private</td><td>175374.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>108293.0</td><td> HS-grad</td><td>9.0</td><td> Widowed</td><td> Other-service</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>64.0</td><td> Private</td><td>181232.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>2179.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> ?</td><td>174662.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> ?</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>47.0</td><td> Local-gov</td><td>186009.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>38.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>198183.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>163003.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Exec-managerial</td><td> Other-relative</td><td> Asian-Pac-Islander</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Philippines</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>296158.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>52.0</td><td> ?</td><td>252903.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> ?</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>48.0</td><td> Private</td><td>187715.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>46.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>214542.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>71.0</td><td> Self-emp-not-inc</td><td>494223.0</td><td> Some-college</td><td>10.0</td><td> Separated</td><td> Sales</td><td> Unmarried</td><td> Black</td><td> Male</td><td>0.0</td><td>1816.0</td><td>2.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>191535.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Private</td><td>228456.0</td><td> Bachelors</td><td>13.0</td><td> Separated</td><td> Other-service</td><td> Other-relative</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>68.0</td><td> ?</td><td>38317.0</td><td> 1st-4th</td><td>2.0</td><td> Divorced</td><td> ?</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>252752.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Self-emp-inc</td><td>78374.0</td><td> Masters</td><td>14.0</td><td> Divorced</td><td> Exec-managerial</td><td> Unmarried</td><td> Asian-Pac-Islander</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>88419.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> Asian-Pac-Islander</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> England</td><td> <=50K</td></tr><tr><td>45.0</td><td> Self-emp-not-inc</td><td>201080.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>36.0</td><td> Private</td><td>207157.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Other-service</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>39.0</td><td> Federal-gov</td><td>235485.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>42.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> State-gov</td><td>102628.0</td><td> Masters</td><td>14.0</td><td> Widowed</td><td> Protective-serv</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> Private</td><td>25828.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>16.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>66.0</td><td> Local-gov</td><td>54826.0</td><td> Assoc-voc</td><td>11.0</td><td> Widowed</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>124953.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>1980.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> State-gov</td><td>175325.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Protective-serv</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>51.0</td><td> Private</td><td>96062.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1977.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>27.0</td><td> Private</td><td>428030.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> State-gov</td><td>149624.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>27.0</td><td> Private</td><td>253814.0</td><td> HS-grad</td><td>9.0</td><td> Married-spouse-absent</td><td> Sales</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>312956.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Own-child</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>483777.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> Private</td><td>183930.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>12.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>37274.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>65.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Local-gov</td><td>181344.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> >50K</td></tr><tr><td>43.0</td><td> Private</td><td>114580.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>633742.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> Private</td><td>286370.0</td><td> 7th-8th</td><td>4.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> >50K</td></tr><tr><td>37.0</td><td> Federal-gov</td><td>29054.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>42.0</td><td> United-States</td><td> >50K</td></tr><tr><td>34.0</td><td> Private</td><td>304030.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Self-emp-not-inc</td><td>143129.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>53.0</td><td> ?</td><td>135105.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> ?</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>99928.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>58.0</td><td> State-gov</td><td>109567.0</td><td> Doctorate</td><td>16.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>1.0</td><td> United-States</td><td> >50K</td></tr><tr><td>38.0</td><td> Private</td><td>155222.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>28.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>159567.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Local-gov</td><td>523910.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>47.0</td><td> Private</td><td>120939.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Federal-gov</td><td>130760.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>197387.0</td><td> 5th-6th</td><td>3.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>99374.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> Federal-gov</td><td>56795.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>14084.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> >50K</td></tr><tr><td>35.0</td><td> Private</td><td>138992.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Other-relative</td><td> White</td><td> Male</td><td>7298.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>24.0</td><td> Self-emp-not-inc</td><td>32921.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>26.0</td><td> Private</td><td>397317.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>1876.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> ?</td><td>170653.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Italy</td><td> <=50K</td></tr><tr><td>51.0</td><td> Private</td><td>259323.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>42.0</td><td> Local-gov</td><td>254817.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>1340.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> State-gov</td><td>48211.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> Private</td><td>140164.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>128757.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> Black</td><td> Male</td><td>7298.0</td><td>0.0</td><td>36.0</td><td> United-States</td><td> >50K</td></tr><tr><td>35.0</td><td> Private</td><td>36270.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>58.0</td><td> Self-emp-inc</td><td>210563.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Wife</td><td> White</td><td> Female</td><td>15024.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> >50K</td></tr><tr><td>17.0</td><td> Private</td><td>65368.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>12.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Local-gov</td><td>160943.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>208358.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>35.0</td><td> Private</td><td>153790.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> Amer-Indian-Eskimo</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>60.0</td><td> Private</td><td>85815.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>54.0</td><td> Self-emp-inc</td><td>125417.0</td><td> 7th-8th</td><td>4.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>37.0</td><td> Private</td><td>635913.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>50.0</td><td> Private</td><td>313321.0</td><td> Assoc-acdm</td><td>12.0</td><td> Divorced</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>182609.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> Poland</td><td> <=50K</td></tr><tr><td>45.0</td><td> Private</td><td>109434.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>255004.0</td><td> 10th</td><td>6.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>197860.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Handlers-cleaners</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>64.0</td><td> ?</td><td>187656.0</td><td> 1st-4th</td><td>2.0</td><td> Divorced</td><td> ?</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>90.0</td><td> Private</td><td>51744.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>2206.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>54.0</td><td> Private</td><td>176681.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>53.0</td><td> Local-gov</td><td>140359.0</td><td> Preschool</td><td>1.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> Private</td><td>243313.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>60.0</td><td> ?</td><td>24215.0</td><td> 10th</td><td>6.0</td><td> Divorced</td><td> ?</td><td> Not-in-family</td><td> Amer-Indian-Eskimo</td><td> Female</td><td>0.0</td><td>0.0</td><td>10.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>66.0</td><td> Self-emp-not-inc</td><td>167687.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> White</td><td> Male</td><td>1409.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>75.0</td><td> Private</td><td>314209.0</td><td> Assoc-voc</td><td>11.0</td><td> Widowed</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> Columbia</td><td> <=50K</td></tr><tr><td>65.0</td><td> Private</td><td>176796.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>538583.0</td><td> 11th</td><td>7.0</td><td> Separated</td><td> Transport-moving</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>3674.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>130408.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Sales</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>159732.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>42.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>110978.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Craft-repair</td><td> Other-relative</td><td> Other</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>76714.0</td><td> Prof-school</td><td>15.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> >50K</td></tr><tr><td>59.0</td><td> State-gov</td><td>268700.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> State-gov</td><td>170525.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>180138.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> Iran</td><td> >50K</td></tr><tr><td>38.0</td><td> Local-gov</td><td>115076.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>70.0</td><td> United-States</td><td> >50K</td></tr><tr><td>23.0</td><td> Private</td><td>115458.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Transport-moving</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> Private</td><td>347890.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>41.0</td><td> Self-emp-not-inc</td><td>196001.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> State-gov</td><td>273905.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-civ-spouse</td><td> Protective-serv</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> ?</td><td>119156.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>179488.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>1741.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>56.0</td><td> Private</td><td>203580.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> ?</td><td> <=50K</td></tr><tr><td>58.0</td><td> Private</td><td>236596.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>32.0</td><td> Private</td><td>183916.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>34.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> Private</td><td>207578.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1977.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>45.0</td><td> Private</td><td>153141.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> ?</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>112763.0</td><td> Prof-school</td><td>15.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>42.0</td><td> Private</td><td>390781.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>59.0</td><td> Local-gov</td><td>171328.0</td><td> 10th</td><td>6.0</td><td> Widowed</td><td> Other-service</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> Local-gov</td><td>27382.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>58.0</td><td> Private</td><td>259014.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Transport-moving</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Self-emp-not-inc</td><td>303044.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Cambodia</td><td> >50K</td></tr><tr><td>20.0</td><td> Private</td><td>117789.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>172579.0</td><td> HS-grad</td><td>9.0</td><td> Separated</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>45.0</td><td> Private</td><td>187666.0</td><td> Assoc-voc</td><td>11.0</td><td> Widowed</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>50.0</td><td> Private</td><td>204518.0</td><td> 7th-8th</td><td>4.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>150042.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Prof-specialty</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>45.0</td><td> Private</td><td>98092.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>245918.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>12.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>59.0</td><td> Private</td><td>146013.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>4064.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>26.0</td><td> Private</td><td>378322.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Self-emp-inc</td><td>257295.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>0.0</td><td>75.0</td><td> Thailand</td><td> >50K</td></tr><tr><td>19.0</td><td> ?</td><td>218956.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>24.0</td><td> Canada</td><td> <=50K</td></tr><tr><td>64.0</td><td> Private</td><td>21174.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>33.0</td><td> Private</td><td>185480.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>222205.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>61.0</td><td> Private</td><td>69867.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>17.0</td><td> Private</td><td>191260.0</td><td> 9th</td><td>5.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Male</td><td>1055.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>50.0</td><td> Self-emp-not-inc</td><td>30653.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> White</td><td> Male</td><td>2407.0</td><td>0.0</td><td>98.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Local-gov</td><td>209109.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Prof-specialty</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>70377.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Prof-specialty</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>477983.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Handlers-cleaners</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>170924.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>7298.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>35.0</td><td> Private</td><td>190174.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>193787.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Tech-support</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>279472.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Wife</td><td> White</td><td> Female</td><td>7298.0</td><td>0.0</td><td>48.0</td><td> United-States</td><td> >50K</td></tr><tr><td>22.0</td><td> Private</td><td>34918.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>15.0</td><td> Germany</td><td> <=50K</td></tr><tr><td>42.0</td><td> Local-gov</td><td>97688.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>5178.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>34.0</td><td> Private</td><td>175413.0</td><td> Assoc-acdm</td><td>12.0</td><td> Divorced</td><td> Sales</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>60.0</td><td> Private</td><td>173960.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>42.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>205759.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>57.0</td><td> Federal-gov</td><td>425161.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>15024.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>41.0</td><td> Private</td><td>220531.0</td><td> Prof-school</td><td>15.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>50.0</td><td> Private</td><td>176609.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>371987.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>50.0</td><td> Private</td><td>193884.0</td><td> 7th-8th</td><td>4.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Ecuador</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>200352.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>127595.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Local-gov</td><td>220419.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Protective-serv</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>56.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>231931.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>248402.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Tech-support</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>65.0</td><td> Private</td><td>111095.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>16.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Self-emp-inc</td><td>57424.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>39.0</td><td> ?</td><td>157443.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> ?</td><td> Wife</td><td> Asian-Pac-Islander</td><td> Female</td><td>3464.0</td><td>0.0</td><td>40.0</td><td> ?</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>278130.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>169469.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>80.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>48.0</td><td> Private</td><td>146268.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>7688.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>21.0</td><td> Private</td><td>153718.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> Asian-Pac-Islander</td><td> Female</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>217460.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>55.0</td><td> Private</td><td>238638.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>4386.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>24.0</td><td> Private</td><td>303296.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> Asian-Pac-Islander</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Laos</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>173321.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>26.0</td><td> Private</td><td>193945.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Private</td><td>83082.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>33.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>193815.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>41.0</td><td> Self-emp-inc</td><td>34987.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>54.0</td><td> United-States</td><td> >50K</td></tr><tr><td>26.0</td><td> Private</td><td>59306.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>142897.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>7298.0</td><td>0.0</td><td>35.0</td><td> Taiwan</td><td> >50K</td></tr><tr><td>19.0</td><td> ?</td><td>860348.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Self-emp-not-inc</td><td>205607.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Prof-specialty</td><td> Not-in-family</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>22.0</td><td> Private</td><td>199698.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>191954.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>77.0</td><td> Self-emp-not-inc</td><td>138714.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> Private</td><td>399087.0</td><td> 5th-6th</td><td>3.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Other-relative</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>423158.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>62.0</td><td> Private</td><td>159841.0</td><td> HS-grad</td><td>9.0</td><td> Widowed</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>39.0</td><td> Self-emp-not-inc</td><td>174308.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>50356.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1485.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>186110.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Transport-moving</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>200381.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>76.0</td><td> Self-emp-not-inc</td><td>174309.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>10.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>63.0</td><td> Self-emp-not-inc</td><td>78383.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> ?</td><td>211601.0</td><td> Assoc-voc</td><td>11.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>187728.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>1887.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>58.0</td><td> Self-emp-not-inc</td><td>321171.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Handlers-cleaners</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>66.0</td><td> Private</td><td>127921.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Transport-moving</td><td> Not-in-family</td><td> White</td><td> Male</td><td>2050.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>206565.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>26.0</td><td> Private</td><td>224563.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>47.0</td><td> Private</td><td>178686.0</td><td> Assoc-voc</td><td>11.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>55.0</td><td> Local-gov</td><td>98545.0</td><td> 10th</td><td>6.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>53.0</td><td> Private</td><td>242606.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>270942.0</td><td> 5th-6th</td><td>3.0</td><td> Never-married</td><td> Other-service</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>48.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>94235.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Private</td><td>71195.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> Private</td><td>104112.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Sales</td><td> Unmarried</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> Haiti</td><td> <=50K</td></tr><tr><td>45.0</td><td> Private</td><td>261192.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>26.0</td><td> Private</td><td>94936.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>296478.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>7298.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>36.0</td><td> State-gov</td><td>119272.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Protective-serv</td><td> Husband</td><td> White</td><td> Male</td><td>7298.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>33.0</td><td> Private</td><td>85043.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Farming-fishing</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> State-gov</td><td>293364.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Protective-serv</td><td> Own-child</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Self-emp-not-inc</td><td>241895.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>42.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>67.0</td><td> ?</td><td>36135.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>8.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> ?</td><td>151989.0</td><td> Assoc-voc</td><td>11.0</td><td> Divorced</td><td> ?</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>56.0</td><td> Private</td><td>101128.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-spouse-absent</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>25.0</td><td> Iran</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>156464.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>117963.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>26.0</td><td> Private</td><td>192262.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>111363.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>46.0</td><td> Local-gov</td><td>329752.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>59.0</td><td> ?</td><td>372020.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>38.0</td><td> Federal-gov</td><td>95432.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>65.0</td><td> Private</td><td>161400.0</td><td> 11th</td><td>7.0</td><td> Widowed</td><td> Other-service</td><td> Unmarried</td><td> Other</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> Private</td><td>96129.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>42.0</td><td> Private</td><td>111949.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>26.0</td><td> Self-emp-not-inc</td><td>117125.0</td><td> 9th</td><td>5.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Portugal</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>348022.0</td><td> 10th</td><td>6.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>62.0</td><td> Private</td><td>270092.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>43.0</td><td> Private</td><td>180609.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>174575.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>1564.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>22.0</td><td> Private</td><td>410439.0</td><td> HS-grad</td><td>9.0</td><td> Married-spouse-absent</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>92262.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>56.0</td><td> Self-emp-not-inc</td><td>183081.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> Private</td><td>362589.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>57.0</td><td> Private</td><td>212448.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>39.0</td><td> Private</td><td>481060.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Sales</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>26.0</td><td> Federal-gov</td><td>185885.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>89821.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>10.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> State-gov</td><td>184018.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> >50K</td></tr><tr><td>45.0</td><td> Private</td><td>256649.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>160323.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> Local-gov</td><td>350845.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>10.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>267404.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>35633.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Self-emp-not-inc</td><td>80914.0</td><td> Masters</td><td>14.0</td><td> Divorced</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>172927.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>54.0</td><td> Private</td><td>174319.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Transport-moving</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Private</td><td>214955.0</td><td> 5th-6th</td><td>3.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>2339.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>344991.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Private</td><td>108699.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Local-gov</td><td>117312.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>396099.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>134152.0</td><td> HS-grad</td><td>9.0</td><td> Separated</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>162028.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>2415.0</td><td>6.0</td><td> United-States</td><td> >50K</td></tr><tr><td>19.0</td><td> Private</td><td>25429.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>16.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> Private</td><td>232392.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Other-relative</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>220098.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>27.0</td><td> Private</td><td>301302.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Self-emp-not-inc</td><td>277946.0</td><td> Assoc-acdm</td><td>12.0</td><td> Separated</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> State-gov</td><td>98101.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>7688.0</td><td>0.0</td><td>45.0</td><td> ?</td><td> >50K</td></tr><tr><td>34.0</td><td> Private</td><td>196164.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>115562.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>45.0</td><td> Private</td><td>96975.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Handlers-cleaners</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> ?</td><td>137300.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> ?</td><td> Other-relative</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>86872.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> >50K</td></tr><tr><td>52.0</td><td> Self-emp-inc</td><td>132178.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>20.0</td><td> Private</td><td>416103.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>108574.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>50.0</td><td> State-gov</td><td>288353.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Protective-serv</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>34.0</td><td> Private</td><td>227689.0</td><td> Assoc-voc</td><td>11.0</td><td> Divorced</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>64.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>166481.0</td><td> 7th-8th</td><td>4.0</td><td> Married-civ-spouse</td><td> Handlers-cleaners</td><td> Husband</td><td> Other</td><td> Male</td><td>0.0</td><td>2179.0</td><td>40.0</td><td> Puerto-Rico</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>445382.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1977.0</td><td>65.0</td><td> United-States</td><td> >50K</td></tr><tr><td>28.0</td><td> Private</td><td>110145.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Self-emp-not-inc</td><td>317253.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> ?</td><td>123147.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> ?</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>1887.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>32.0</td><td> Private</td><td>364657.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Local-gov</td><td>42346.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Other-service</td><td> Not-in-family</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>241951.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>118500.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Exec-managerial</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Private</td><td>188386.0</td><td> Doctorate</td><td>16.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>15024.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>31.0</td><td> State-gov</td><td>1033222.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>92440.0</td><td> 12th</td><td>8.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>52.0</td><td> Private</td><td>190762.0</td><td> 1st-4th</td><td>2.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>426017.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>19.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Local-gov</td><td>243867.0</td><td> 11th</td><td>7.0</td><td> Separated</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> State-gov</td><td>240283.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Transport-moving</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> Private</td><td>61777.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>175024.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>2176.0</td><td>0.0</td><td>18.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> State-gov</td><td>92003.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>29.0</td><td> Private</td><td>188401.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Farming-fishing</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>228528.0</td><td> 10th</td><td>6.0</td><td> Never-married</td><td> Craft-repair</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>133373.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Federal-gov</td><td>255191.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>1408.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>204653.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>72.0</td><td> Dominican-Republic</td><td> <=50K</td></tr><tr><td>63.0</td><td> Self-emp-inc</td><td>222289.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>47.0</td><td> Local-gov</td><td>287480.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>80.0</td><td> ?</td><td>107762.0</td><td> HS-grad</td><td>9.0</td><td> Widowed</td><td> ?</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> ?</td><td>202521.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> Self-emp-not-inc</td><td>204116.0</td><td> Bachelors</td><td>13.0</td><td> Married-spouse-absent</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>2174.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>29662.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> >50K</td></tr><tr><td>27.0</td><td> Private</td><td>116358.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Craft-repair</td><td> Own-child</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>1980.0</td><td>40.0</td><td> Philippines</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>208405.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>34.0</td><td> Local-gov</td><td>284843.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Farming-fishing</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>594.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Local-gov</td><td>117018.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Protective-serv</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>81281.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Local-gov</td><td>340148.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>363425.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>45.0</td><td> Private</td><td>45857.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>28.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Federal-gov</td><td>191073.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Armed-Forces</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>116632.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>27.0</td><td> Private</td><td>405855.0</td><td> 9th</td><td>5.0</td><td> Never-married</td><td> Craft-repair</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>20.0</td><td> Private</td><td>298227.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>290521.0</td><td> HS-grad</td><td>9.0</td><td> Widowed</td><td> Exec-managerial</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>51.0</td><td> Private</td><td>56915.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> Amer-Indian-Eskimo</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> Private</td><td>146538.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> ?</td><td>258872.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>5.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> Private</td><td>206399.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Own-child</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>45.0</td><td> Self-emp-inc</td><td>197332.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> >50K</td></tr><tr><td>60.0</td><td> Private</td><td>245062.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>42.0</td><td> Private</td><td>197583.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> ?</td><td> >50K</td></tr><tr><td>44.0</td><td> Self-emp-not-inc</td><td>234885.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>40.0</td><td> Private</td><td>72887.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>30.0</td><td> Private</td><td>180374.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>351299.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>54012.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> ?</td><td>115745.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>116632.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> Farming-fishing</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>54.0</td><td> Local-gov</td><td>288825.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>132601.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>50.0</td><td> Private</td><td>193374.0</td><td> 1st-4th</td><td>2.0</td><td> Married-spouse-absent</td><td> Craft-repair</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>170070.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>126708.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>52.0</td><td> Private</td><td>35598.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Transport-moving</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>33983.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Private</td><td>192776.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1977.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>30.0</td><td> Private</td><td>118551.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>16.0</td><td> United-States</td><td> >50K</td></tr><tr><td>60.0</td><td> Private</td><td>201965.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Prof-specialty</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>22.0</td><td> ?</td><td>139883.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>285020.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>303990.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Transport-moving</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>67.0</td><td> Private</td><td>49401.0</td><td> Assoc-voc</td><td>11.0</td><td> Divorced</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Private</td><td>279196.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>211870.0</td><td> 9th</td><td>5.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>6.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> Private</td><td>281432.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>161155.0</td><td> 10th</td><td>6.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>197904.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>111746.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> Portugal</td><td> <=50K</td></tr><tr><td>43.0</td><td> Self-emp-not-inc</td><td>170721.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> State-gov</td><td>70100.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>193626.0</td><td> HS-grad</td><td>9.0</td><td> Married-spouse-absent</td><td> Craft-repair</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>52.0</td><td> ?</td><td>271749.0</td><td> 12th</td><td>8.0</td><td> Never-married</td><td> ?</td><td> Other-relative</td><td> Black</td><td> Male</td><td>594.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>189775.0</td><td> Some-college</td><td>10.0</td><td> Married-spouse-absent</td><td> Adm-clerical</td><td> Own-child</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>63.0</td><td> ?</td><td>401531.0</td><td> 1st-4th</td><td>2.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>59.0</td><td> Local-gov</td><td>286967.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>45.0</td><td> Local-gov</td><td>164427.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Prof-specialty</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>91039.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>15024.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>40.0</td><td> Private</td><td>347934.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Federal-gov</td><td>371373.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>32220.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>187251.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Prof-specialty</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>178107.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Craft-repair</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>343121.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>36.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> Private</td><td>262749.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>403107.0</td><td> 5th-6th</td><td>3.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> El-Salvador</td><td> <=50K</td></tr><tr><td>26.0</td><td> Private</td><td>64293.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>72.0</td><td> ?</td><td>303588.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Local-gov</td><td>324960.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Farming-fishing</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Poland</td><td> <=50K</td></tr><tr><td>62.0</td><td> Local-gov</td><td>114060.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>52.0</td><td> Private</td><td>48925.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>58.0</td><td> Private</td><td>180980.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Other-service</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>42.0</td><td> France</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>181054.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>388093.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> Private</td><td>249609.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Protective-serv</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>8.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>112131.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>47.0</td><td> Local-gov</td><td>543162.0</td><td> HS-grad</td><td>9.0</td><td> Separated</td><td> Adm-clerical</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>39.0</td><td> Private</td><td>91996.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Other-service</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Private</td><td>141944.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-spouse-absent</td><td> Handlers-cleaners</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>1380.0</td><td>42.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>53.0</td><td> ?</td><td>251804.0</td><td> 5th-6th</td><td>3.0</td><td> Widowed</td><td> ?</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>37070.0</td><td> Assoc-voc</td><td>11.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>337587.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>28.0</td><td> Private</td><td>189346.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>57.0</td><td> ?</td><td>222216.0</td><td> Assoc-voc</td><td>11.0</td><td> Widowed</td><td> ?</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>267044.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Not-in-family</td><td> Amer-Indian-Eskimo</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> ?</td><td>214635.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> ?</td><td>204226.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> ?</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>108116.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>38.0</td><td> Self-emp-inc</td><td>99146.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>15024.0</td><td>0.0</td><td>80.0</td><td> United-States</td><td> >50K</td></tr><tr><td>50.0</td><td> Private</td><td>196232.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>7688.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>24.0</td><td> Local-gov</td><td>248344.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Handlers-cleaners</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Local-gov</td><td>186035.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>44.0</td><td> Private</td><td>177905.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Machine-op-inspct</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>58.0</td><td> United-States</td><td> >50K</td></tr><tr><td>28.0</td><td> Private</td><td>85812.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Private</td><td>221172.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>74.0</td><td> Private</td><td>99183.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>9.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Self-emp-not-inc</td><td>190387.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Self-emp-not-inc</td><td>202692.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>109339.0</td><td> 11th</td><td>7.0</td><td> Divorced</td><td> Machine-op-inspct</td><td> Unmarried</td><td> Other</td><td> Female</td><td>0.0</td><td>0.0</td><td>46.0</td><td> Puerto-Rico</td><td> <=50K</td></tr><tr><td>26.0</td><td> Private</td><td>108658.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>197202.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>101739.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>67.0</td><td> Private</td><td>231559.0</td><td> Prof-school</td><td>15.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>20051.0</td><td>0.0</td><td>48.0</td><td> United-States</td><td> >50K</td></tr><tr><td>39.0</td><td> Local-gov</td><td>207853.0</td><td> 12th</td><td>8.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>57.0</td><td> Private</td><td>190942.0</td><td> 1st-4th</td><td>2.0</td><td> Widowed</td><td> Priv-house-serv</td><td> Not-in-family</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>102345.0</td><td> Assoc-voc</td><td>11.0</td><td> Separated</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Self-emp-inc</td><td>41493.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Farming-fishing</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> ?</td><td>190027.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> ?</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>210525.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>133937.0</td><td> Doctorate</td><td>16.0</td><td> Never-married</td><td> Prof-specialty</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>237903.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>163862.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Transport-moving</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>201872.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>84179.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>58.0</td><td> Private</td><td>51662.0</td><td> 10th</td><td>6.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>8.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Local-gov</td><td>233327.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Protective-serv</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>259510.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>36.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>184831.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Craft-repair</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Self-emp-not-inc</td><td>245724.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Self-emp-not-inc</td><td>27053.0</td><td> HS-grad</td><td>9.0</td><td> Separated</td><td> Other-service</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>72.0</td><td> Private</td><td>205343.0</td><td> 11th</td><td>7.0</td><td> Widowed</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>229328.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Wife</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Federal-gov</td><td>319560.0</td><td> Assoc-voc</td><td>11.0</td><td> Divorced</td><td> Craft-repair</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>69.0</td><td> Private</td><td>136218.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>54576.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>323069.0</td><td> HS-grad</td><td>9.0</td><td> Separated</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> ?</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>148291.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>32.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>152453.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>114053.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Transport-moving</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>54.0</td><td> Private</td><td>212960.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> >50K</td></tr><tr><td>47.0</td><td> Private</td><td>264052.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>24.0</td><td> Private</td><td>82804.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>52.0</td><td> Self-emp-not-inc</td><td>334273.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>20.0</td><td> Private</td><td>27337.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> Amer-Indian-Eskimo</td><td> Male</td><td>0.0</td><td>0.0</td><td>48.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Self-emp-inc</td><td>188436.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>5013.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>45.0</td><td> Private</td><td>433665.0</td><td> 7th-8th</td><td>4.0</td><td> Separated</td><td> Other-service</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>29.0</td><td> Self-emp-not-inc</td><td>110663.0</td><td> HS-grad</td><td>9.0</td><td> Separated</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>47.0</td><td> Private</td><td>87490.0</td><td> Masters</td><td>14.0</td><td> Divorced</td><td> Exec-managerial</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>42.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>354351.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>51.0</td><td> Private</td><td>95469.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>242718.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>12.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>22463.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1977.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>27.0</td><td> Private</td><td>158156.0</td><td> Doctorate</td><td>16.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>70.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>350162.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Wife</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>18.0</td><td> ?</td><td>165532.0</td><td> 12th</td><td>8.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Self-emp-not-inc</td><td>28738.0</td><td> Assoc-acdm</td><td>12.0</td><td> Divorced</td><td> Sales</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>58.0</td><td> Local-gov</td><td>283635.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>26.0</td><td> Self-emp-not-inc</td><td>86646.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>65.0</td><td> ?</td><td>195733.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> >50K</td></tr><tr><td>57.0</td><td> Private</td><td>69884.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>59.0</td><td> Private</td><td>199713.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>181659.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Self-emp-not-inc</td><td>340939.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>197747.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>34292.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> Private</td><td>156764.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>52.0</td><td> Private</td><td>25826.0</td><td> 10th</td><td>6.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1887.0</td><td>47.0</td><td> United-States</td><td> >50K</td></tr><tr><td>57.0</td><td> Self-emp-inc</td><td>103948.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>80.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> ?</td><td>137390.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>55.0</td><td> ?</td><td>105138.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> ?</td><td> Wife</td><td> Asian-Pac-Islander</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>60.0</td><td> Private</td><td>39352.0</td><td> 7th-8th</td><td>4.0</td><td> Never-married</td><td> Transport-moving</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>48.0</td><td> United-States</td><td> >50K</td></tr><tr><td>31.0</td><td> Private</td><td>168387.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>7688.0</td><td>0.0</td><td>40.0</td><td> Canada</td><td> >50K</td></tr><tr><td>23.0</td><td> Private</td><td>117789.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Adm-clerical</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>267147.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> ?</td><td>99399.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> ?</td><td> Unmarried</td><td> Amer-Indian-Eskimo</td><td> Female</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Self-emp-not-inc</td><td>214242.0</td><td> Prof-school</td><td>15.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1902.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>25.0</td><td> Private</td><td>200408.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Male</td><td>2174.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Private</td><td>136455.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>239824.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> Private</td><td>217039.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>28.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>60.0</td><td> Private</td><td>51290.0</td><td> 7th-8th</td><td>4.0</td><td> Divorced</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Local-gov</td><td>175674.0</td><td> 9th</td><td>5.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Self-emp-not-inc</td><td>194404.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> Farming-fishing</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>48.0</td><td> Private</td><td>45612.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Adm-clerical</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>37.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>51.0</td><td> Private</td><td>410114.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>29.0</td><td> Private</td><td>182521.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> Amer-Indian-Eskimo</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Local-gov</td><td>339772.0</td><td> HS-grad</td><td>9.0</td><td> Separated</td><td> Exec-managerial</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>169658.0</td><td> 10th</td><td>6.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>21.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>52.0</td><td> Private</td><td>200853.0</td><td> Masters</td><td>14.0</td><td> Divorced</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>6849.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>247564.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>249909.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Handlers-cleaners</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>26.0</td><td> Local-gov</td><td>208122.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Own-child</td><td> White</td><td> Male</td><td>1055.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>109881.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>39.0</td><td> Private</td><td>207824.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>369027.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>50.0</td><td> Self-emp-not-inc</td><td>114117.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>32.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>52.0</td><td> Self-emp-inc</td><td>51048.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Private</td><td>102388.0</td><td> Prof-school</td><td>15.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>15024.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>23.0</td><td> Private</td><td>190483.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>45.0</td><td> Private</td><td>462440.0</td><td> 11th</td><td>7.0</td><td> Widowed</td><td> Other-service</td><td> Not-in-family</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>65.0</td><td> Private</td><td>109351.0</td><td> 9th</td><td>5.0</td><td> Widowed</td><td> Priv-house-serv</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>34383.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>47.0</td><td> Private</td><td>241832.0</td><td> 9th</td><td>5.0</td><td> Married-spouse-absent</td><td> Handlers-cleaners</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> El-Salvador</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>124187.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Farming-fishing</td><td> Own-child</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>153614.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>38.0</td><td> Self-emp-not-inc</td><td>267556.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>64.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>205469.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>49.0</td><td> Private</td><td>268090.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>26.0</td><td> United-States</td><td> >50K</td></tr><tr><td>47.0</td><td> Self-emp-not-inc</td><td>165039.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Local-gov</td><td>120451.0</td><td> 10th</td><td>6.0</td><td> Separated</td><td> Other-service</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>154374.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>15024.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>30.0</td><td> Private</td><td>103649.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>58.0</td><td> Self-emp-not-inc</td><td>35723.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> Private</td><td>262601.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>14.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>226181.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>175697.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>15024.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>47.0</td><td> Self-emp-inc</td><td>248145.0</td><td> 5th-6th</td><td>3.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> Cuba</td><td> <=50K</td></tr><tr><td>52.0</td><td> Self-emp-not-inc</td><td>289436.0</td><td> Doctorate</td><td>16.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>26.0</td><td> Private</td><td>75654.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>60.0</td><td> Private</td><td>199378.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>160968.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>188563.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>5178.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>31.0</td><td> Private</td><td>55849.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>50.0</td><td> Self-emp-inc</td><td>195322.0</td><td> Doctorate</td><td>16.0</td><td> Separated</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>31.0</td><td> Local-gov</td><td>402089.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>71.0</td><td> Private</td><td>78277.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>58.0</td><td> ?</td><td>158611.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> State-gov</td><td>169496.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>20.0</td><td> Private</td><td>130959.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>556660.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Exec-managerial</td><td> Other-relative</td><td> White</td><td> Male</td><td>4101.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>292472.0</td><td> Doctorate</td><td>16.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Taiwan</td><td> >50K</td></tr><tr><td>38.0</td><td> State-gov</td><td>143774.0</td><td> Some-college</td><td>10.0</td><td> Separated</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>288341.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>32.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> State-gov</td><td>71592.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Unmarried</td><td> Asian-Pac-Islander</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Philippines</td><td> <=50K</td></tr><tr><td>70.0</td><td> ?</td><td>167358.0</td><td> 9th</td><td>5.0</td><td> Widowed</td><td> ?</td><td> Unmarried</td><td> White</td><td> Female</td><td>1111.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>106742.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>219288.0</td><td> 7th-8th</td><td>4.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>174524.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>44.0</td><td> Self-emp-not-inc</td><td>335183.0</td><td> 12th</td><td>8.0</td><td> Married-civ-spouse</td><td> Handlers-cleaners</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>35.0</td><td> Private</td><td>261293.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>111900.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Local-gov</td><td>194360.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> Private</td><td>81145.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Private</td><td>341204.0</td><td> Assoc-acdm</td><td>12.0</td><td> Divorced</td><td> Prof-specialty</td><td> Unmarried</td><td> White</td><td> Female</td><td>8614.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>27.0</td><td> State-gov</td><td>249362.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>3411.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Private</td><td>247019.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>20.0</td><td> ?</td><td>114746.0</td><td> 11th</td><td>7.0</td><td> Married-spouse-absent</td><td> ?</td><td> Own-child</td><td> Asian-Pac-Islander</td><td> Female</td><td>0.0</td><td>1762.0</td><td>40.0</td><td> South</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>172146.0</td><td> 9th</td><td>5.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>1721.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>48.0</td><td> Federal-gov</td><td>110457.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> ?</td><td>80077.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Self-emp-not-inc</td><td>368700.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Farming-fishing</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>10.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>182556.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>50.0</td><td> Self-emp-inc</td><td>219420.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>22.0</td><td> Private</td><td>240817.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Female</td><td>2597.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>102726.0</td><td> 12th</td><td>8.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>16.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>226267.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>125457.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>58.0</td><td> Self-emp-not-inc</td><td>204021.0</td><td> HS-grad</td><td>9.0</td><td> Widowed</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Local-gov</td><td>92262.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Protective-serv</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>48.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>161141.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Portugal</td><td> >50K</td></tr><tr><td>34.0</td><td> Self-emp-not-inc</td><td>190290.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>23.0</td><td> Local-gov</td><td>430828.0</td><td> Some-college</td><td>10.0</td><td> Separated</td><td> Exec-managerial</td><td> Unmarried</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> State-gov</td><td>59342.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Adm-clerical</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>5.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>136721.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>66.0</td><td> ?</td><td>149422.0</td><td> 7th-8th</td><td>4.0</td><td> Never-married</td><td> ?</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>4.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>45.0</td><td> Local-gov</td><td>86644.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>195124.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> Dominican-Republic</td><td> <=50K</td></tr><tr><td>26.0</td><td> Private</td><td>167350.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>54.0</td><td> Local-gov</td><td>113000.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>140027.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Own-child</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Private</td><td>262425.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> Private</td><td>316702.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Prof-specialty</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> State-gov</td><td>335453.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> ?</td><td>202480.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> ?</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>203628.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>31.0</td><td> Private</td><td>118710.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1902.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>30.0</td><td> Private</td><td>189620.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Poland</td><td> <=50K</td></tr><tr><td>19.0</td><td> Private</td><td>475028.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Local-gov</td><td>110866.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>243605.0</td><td> Bachelors</td><td>13.0</td><td> Widowed</td><td> Sales</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>1380.0</td><td>40.0</td><td> Cuba</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>163870.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Self-emp-not-inc</td><td>80145.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Private</td><td>295566.0</td><td> Doctorate</td><td>16.0</td><td> Divorced</td><td> Prof-specialty</td><td> Unmarried</td><td> White</td><td> Female</td><td>25236.0</td><td>0.0</td><td>65.0</td><td> United-States</td><td> >50K</td></tr><tr><td>44.0</td><td> Private</td><td>63042.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Exec-managerial</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>40.0</td><td> Private</td><td>229148.0</td><td> 12th</td><td>8.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Jamaica</td><td> <=50K</td></tr><tr><td>45.0</td><td> Private</td><td>242552.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>60.0</td><td> Private</td><td>177665.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> Private</td><td>208103.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Other-service</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>296450.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>70282.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Adm-clerical</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>271767.0</td><td> Bachelors</td><td>13.0</td><td> Separated</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> ?</td><td> <=50K</td></tr><tr><td>40.0</td><td> Private</td><td>144995.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>4386.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Local-gov</td><td>382635.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> Honduras</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>295697.0</td><td> HS-grad</td><td>9.0</td><td> Separated</td><td> Other-service</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>194141.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> State-gov</td><td>378418.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Tech-support</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> Private</td><td>214399.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>217460.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>33.0</td><td> Private</td><td>182556.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>125831.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>2051.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>271328.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>4650.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>50.0</td><td> Local-gov</td><td>50459.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>42.0</td><td> United-States</td><td> >50K</td></tr><tr><td>42.0</td><td> Private</td><td>162140.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>7298.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>43.0</td><td> Private</td><td>177937.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> ?</td><td> >50K</td></tr><tr><td>44.0</td><td> Private</td><td>111502.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> Private</td><td>299047.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>223212.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>65.0</td><td> Self-emp-not-inc</td><td>118474.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>9386.0</td><td>0.0</td><td>59.0</td><td> ?</td><td> >50K</td></tr><tr><td>23.0</td><td> Private</td><td>352139.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>55.0</td><td> Private</td><td>173093.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Adm-clerical</td><td> Not-in-family</td><td> Asian-Pac-Islander</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>26.0</td><td> Private</td><td>181655.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>2377.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>332702.0</td><td> Assoc-voc</td><td>11.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>45.0</td><td> ?</td><td>51164.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> ?</td><td> Wife</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>234901.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>2407.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>131414.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>36.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> State-gov</td><td>260960.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>56.0</td><td> Private</td><td>156052.0</td><td> HS-grad</td><td>9.0</td><td> Widowed</td><td> Other-service</td><td> Unmarried</td><td> Black</td><td> Female</td><td>594.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Private</td><td>279914.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>19.0</td><td> Private</td><td>192453.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Other-relative</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>55.0</td><td> Self-emp-not-inc</td><td>200939.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>72.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Private</td><td>151408.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>14084.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>26.0</td><td> Private</td><td>112847.0</td><td> Assoc-voc</td><td>11.0</td><td> Never-married</td><td> Tech-support</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>316929.0</td><td> 12th</td><td>8.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Local-gov</td><td>126319.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>55.0</td><td> Private</td><td>197422.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>7688.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>32.0</td><td> Private</td><td>267736.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Own-child</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>267034.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Craft-repair</td><td> Own-child</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Haiti</td><td> <=50K</td></tr><tr><td>46.0</td><td> State-gov</td><td>193047.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>37.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> State-gov</td><td>356089.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>7688.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>22.0</td><td> Private</td><td>223515.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>58.0</td><td> Self-emp-not-inc</td><td>87510.0</td><td> 10th</td><td>6.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>145111.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Transport-moving</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>39.0</td><td> Private</td><td>48093.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>31757.0</td><td> Assoc-voc</td><td>11.0</td><td> Never-married</td><td> Craft-repair</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>54.0</td><td> Private</td><td>285854.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>33.0</td><td> Local-gov</td><td>120064.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Federal-gov</td><td>167381.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>37.0</td><td> Private</td><td>103408.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Farming-fishing</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>101460.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>18.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>59.0</td><td> Local-gov</td><td>420537.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> >50K</td></tr><tr><td>34.0</td><td> Local-gov</td><td>119411.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Protective-serv</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Portugal</td><td> <=50K</td></tr><tr><td>53.0</td><td> Self-emp-inc</td><td>128272.0</td><td> Doctorate</td><td>16.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>70.0</td><td> United-States</td><td> >50K</td></tr><tr><td>51.0</td><td> Private</td><td>386773.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> >50K</td></tr><tr><td>32.0</td><td> Private</td><td>283268.0</td><td> 10th</td><td>6.0</td><td> Separated</td><td> Other-service</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>42.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> State-gov</td><td>301526.0</td><td> Some-college</td><td>10.0</td><td> Married-spouse-absent</td><td> Other-service</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> Private</td><td>151790.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>30.0</td><td> Germany</td><td> <=50K</td></tr><tr><td>47.0</td><td> Self-emp-not-inc</td><td>106252.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>188557.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>26.0</td><td> Private</td><td>171114.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Farming-fishing</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>327323.0</td><td> 5th-6th</td><td>3.0</td><td> Separated</td><td> Farming-fishing</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>32.0</td><td> Guatemala</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>244147.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Craft-repair</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>280282.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> >50K</td></tr><tr><td>55.0</td><td> Private</td><td>116442.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Local-gov</td><td>282579.0</td><td> Assoc-voc</td><td>11.0</td><td> Divorced</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>56.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>51838.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>73585.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> ?</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>226902.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>54.0</td><td> Private</td><td>279129.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> State-gov</td><td>146908.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> ?</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>196690.0</td><td> Assoc-voc</td><td>11.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>1669.0</td><td>42.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> Private</td><td>130760.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>41.0</td><td> Self-emp-not-inc</td><td>49572.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> Private</td><td>237601.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> Other</td><td> Female</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> >50K</td></tr><tr><td>42.0</td><td> Private</td><td>169628.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Adm-clerical</td><td> Not-in-family</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>61.0</td><td> Self-emp-not-inc</td><td>36671.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>2352.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> Private</td><td>231193.0</td><td> 12th</td><td>8.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>59.0</td><td> ?</td><td>192130.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>16.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> ?</td><td>149704.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> ?</td><td> Not-in-family</td><td> White</td><td> Female</td><td>1055.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>48.0</td><td> Private</td><td>102102.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>41.0</td><td> Self-emp-inc</td><td>32185.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> ?</td><td>196061.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>33.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>211046.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Female</td><td>2463.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>60.0</td><td> Private</td><td>31577.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>22.0</td><td> Private</td><td>162343.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Other-relative</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>61.0</td><td> Private</td><td>128831.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>316688.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Private</td><td>90758.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> >50K</td></tr><tr><td>43.0</td><td> Private</td><td>274363.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1902.0</td><td>40.0</td><td> England</td><td> >50K</td></tr><tr><td>43.0</td><td> Private</td><td>154538.0</td><td> Assoc-acdm</td><td>12.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>106085.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> Black</td><td> Male</td><td>0.0</td><td>1721.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>68.0</td><td> Self-emp-not-inc</td><td>315859.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Farming-fishing</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>51471.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>193830.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>231043.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>5178.0</td><td>0.0</td><td>48.0</td><td> United-States</td><td> >50K</td></tr><tr><td>50.0</td><td> ?</td><td>23780.0</td><td> Masters</td><td>14.0</td><td> Married-spouse-absent</td><td> ?</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>169879.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>3103.0</td><td>0.0</td><td>47.0</td><td> United-States</td><td> >50K</td></tr><tr><td>64.0</td><td> Private</td><td>270333.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>20.0</td><td> Private</td><td>138768.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Transport-moving</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>191571.0</td><td> HS-grad</td><td>9.0</td><td> Separated</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>36.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> ?</td><td>219941.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>94113.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> Private</td><td>137510.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>32607.0</td><td> 10th</td><td>6.0</td><td> Never-married</td><td> Farming-fishing</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>47.0</td><td> Self-emp-not-inc</td><td>93208.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>75.0</td><td> Italy</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>254440.0</td><td> Assoc-voc</td><td>11.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>56.0</td><td> Private</td><td>186556.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>64.0</td><td> Private</td><td>169871.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>47.0</td><td> Private</td><td>191277.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>48.0</td><td> Private</td><td>167159.0</td><td> Assoc-voc</td><td>11.0</td><td> Never-married</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>171871.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>46.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>154411.0</td><td> Assoc-voc</td><td>11.0</td><td> Never-married</td><td> Tech-support</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>129227.0</td><td> HS-grad</td><td>9.0</td><td> Widowed</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>110331.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>1672.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>57.0</td><td> Private</td><td>34269.0</td><td> HS-grad</td><td>9.0</td><td> Widowed</td><td> Transport-moving</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>653.0</td><td>42.0</td><td> United-States</td><td> >50K</td></tr><tr><td>62.0</td><td> Private</td><td>174355.0</td><td> HS-grad</td><td>9.0</td><td> Widowed</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>39.0</td><td> Private</td><td>680390.0</td><td> HS-grad</td><td>9.0</td><td> Separated</td><td> Machine-op-inspct</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>233130.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Self-emp-inc</td><td>165474.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> ?</td><td>257780.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>53.0</td><td> Private</td><td>194259.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> White</td><td> Female</td><td>4386.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>26.0</td><td> Private</td><td>280093.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>73.0</td><td> Self-emp-not-inc</td><td>177387.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>72.0</td><td> ?</td><td>28929.0</td><td> 11th</td><td>7.0</td><td> Widowed</td><td> ?</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>24.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>55.0</td><td> Private</td><td>105304.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>499233.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>180572.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>24.0</td><td> Private</td><td>321435.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>63.0</td><td> Private</td><td>86108.0</td><td> HS-grad</td><td>9.0</td><td> Widowed</td><td> Farming-fishing</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>6.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>198124.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>135162.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>51.0</td><td> Private</td><td>146813.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>62.0</td><td> Local-gov</td><td>291175.0</td><td> Bachelors</td><td>13.0</td><td> Widowed</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>48.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>55.0</td><td> Private</td><td>387569.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>4386.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>43.0</td><td> Private</td><td>102895.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> Local-gov</td><td>33274.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>86551.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>39.0</td><td> Private</td><td>138192.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>118966.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>18.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>61.0</td><td> Private</td><td>99784.0</td><td> Masters</td><td>14.0</td><td> Widowed</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>26.0</td><td> Private</td><td>90980.0</td><td> Assoc-voc</td><td>11.0</td><td> Divorced</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Self-emp-not-inc</td><td>177407.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>26.0</td><td> Private</td><td>96467.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>48.0</td><td> State-gov</td><td>327886.0</td><td> Doctorate</td><td>16.0</td><td> Divorced</td><td> Prof-specialty</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>34.0</td><td> Private</td><td>111567.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Transport-moving</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Local-gov</td><td>166545.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Adm-clerical</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>59.0</td><td> Private</td><td>142182.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>34.0</td><td> Private</td><td>188798.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Private</td><td>38563.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>56.0</td><td> United-States</td><td> >50K</td></tr><tr><td>18.0</td><td> Private</td><td>216284.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Adm-clerical</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>43.0</td><td> Private</td><td>191547.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>48.0</td><td> Private</td><td>285335.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Self-emp-inc</td><td>142712.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>70.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>80945.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>309055.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>62339.0</td><td> 10th</td><td>6.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>17.0</td><td> Private</td><td>368700.0</td><td> 11th</td><td>7.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>28.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>39.0</td><td> Private</td><td>176186.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>29.0</td><td> Self-emp-not-inc</td><td>266855.0</td><td> Bachelors</td><td>13.0</td><td> Separated</td><td> Prof-specialty</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>48087.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>24.0</td><td> Private</td><td>121313.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Transport-moving</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>71.0</td><td> Self-emp-not-inc</td><td>143437.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>10605.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>51.0</td><td> Self-emp-not-inc</td><td>160724.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>2415.0</td><td>40.0</td><td> China</td><td> >50K</td></tr><tr><td>55.0</td><td> Private</td><td>282753.0</td><td> 5th-6th</td><td>3.0</td><td> Divorced</td><td> Other-service</td><td> Unmarried</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>194636.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>23.0</td><td> Private</td><td>153044.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>7.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>411797.0</td><td> Assoc-voc</td><td>11.0</td><td> Divorced</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>39.0</td><td> Private</td><td>117683.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>19.0</td><td> Private</td><td>376540.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Private</td><td>72393.0</td><td> 9th</td><td>5.0</td><td> Divorced</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>270335.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Other-relative</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Philippines</td><td> >50K</td></tr><tr><td>27.0</td><td> Private</td><td>96226.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>70.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>95336.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>33.0</td><td> Private</td><td>258498.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>63.0</td><td> ?</td><td>149698.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>205865.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Exec-managerial</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>28.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Self-emp-inc</td><td>155781.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> ?</td><td> <=50K</td></tr><tr><td>54.0</td><td> Self-emp-not-inc</td><td>406468.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>29.0</td><td> Private</td><td>177119.0</td><td> Assoc-voc</td><td>11.0</td><td> Divorced</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Female</td><td>2174.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>48.0</td><td> ?</td><td>144397.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> ?</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Self-emp-not-inc</td><td>372525.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>164170.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> Asian-Pac-Islander</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> India</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>183800.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>7688.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>42.0</td><td> Self-emp-not-inc</td><td>177307.0</td><td> Prof-school</td><td>15.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>65.0</td><td> United-States</td><td> >50K</td></tr><tr><td>40.0</td><td> Private</td><td>170108.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>47.0</td><td> Private</td><td>341995.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Sales</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> Private</td><td>226508.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Exec-managerial</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>30.0</td><td> Private</td><td>87418.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>28.0</td><td> Private</td><td>109165.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>63.0</td><td> Local-gov</td><td>28856.0</td><td> 7th-8th</td><td>4.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>51.0</td><td> Self-emp-not-inc</td><td>175897.0</td><td> 9th</td><td>5.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> Private</td><td>99697.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> ?</td><td>90270.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-civ-spouse</td><td> ?</td><td> Own-child</td><td> Amer-Indian-Eskimo</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>152375.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Private</td><td>171550.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>211154.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>52.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>24.0</td><td> Private</td><td>202570.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Prof-specialty</td><td> Own-child</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>15.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Self-emp-not-inc</td><td>168496.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>10.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>53.0</td><td> Private</td><td>68898.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Tech-support</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>27.0</td><td> Private</td><td>93235.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>278924.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>44.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>53.0</td><td> Self-emp-not-inc</td><td>311020.0</td><td> 10th</td><td>6.0</td><td> Married-civ-spouse</td><td> Farming-fishing</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>175878.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Craft-repair</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>543028.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Sales</td><td> Own-child</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>39.0</td><td> Private</td><td>202027.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>15024.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>43.0</td><td> Private</td><td>158926.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Wife</td><td> Asian-Pac-Islander</td><td> Female</td><td>0.0</td><td>0.0</td><td>50.0</td><td> South</td><td> <=50K</td></tr><tr><td>67.0</td><td> Self-emp-inc</td><td>76860.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>81.0</td><td> Self-emp-not-inc</td><td>136063.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>186648.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>257509.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>98155.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Transport-moving</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>42.0</td><td> Private</td><td>274198.0</td><td> 5th-6th</td><td>3.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>38.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>38.0</td><td> Private</td><td>97083.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>64.0</td><td> ?</td><td>29825.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> ?</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>5.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>262153.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>214738.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>51.0</td><td> Private</td><td>138022.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>22.0</td><td> Private</td><td>91842.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>42.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Private</td><td>373662.0</td><td> 1st-4th</td><td>2.0</td><td> Married-spouse-absent</td><td> Priv-house-serv</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Guatemala</td><td> <=50K</td></tr><tr><td>42.0</td><td> Private</td><td>162003.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>19.0</td><td> ?</td><td>52114.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> ?</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>10.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>51.0</td><td> Local-gov</td><td>241843.0</td><td> Preschool</td><td>1.0</td><td> Married-civ-spouse</td><td> Other-service</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> Private</td><td>375871.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>37.0</td><td> Private</td><td>186934.0</td><td> 11th</td><td>7.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>3103.0</td><td>0.0</td><td>44.0</td><td> United-States</td><td> >50K</td></tr><tr><td>37.0</td><td> Private</td><td>176900.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>99.0</td><td> United-States</td><td> >50K</td></tr><tr><td>47.0</td><td> Private</td><td>21906.0</td><td> Masters</td><td>14.0</td><td> Never-married</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>25.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>132222.0</td><td> Prof-school</td><td>15.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>2415.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>33.0</td><td> Private</td><td>143653.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>31.0</td><td> Private</td><td>111567.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>31.0</td><td> Private</td><td>78602.0</td><td> Assoc-acdm</td><td>12.0</td><td> Divorced</td><td> Other-service</td><td> Unmarried</td><td> Amer-Indian-Eskimo</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Private</td><td>465507.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> Black</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>38.0</td><td> Self-emp-inc</td><td>196373.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>18.0</td><td> Private</td><td>293227.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> Private</td><td>241752.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>54.0</td><td> Local-gov</td><td>166398.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Exec-managerial</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> Private</td><td>184682.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>36.0</td><td> Self-emp-inc</td><td>108293.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>1977.0</td><td>45.0</td><td> United-States</td><td> >50K</td></tr><tr><td>43.0</td><td> Private</td><td>250802.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Craft-repair</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Self-emp-not-inc</td><td>325159.0</td><td> Some-college</td><td>10.0</td><td> Divorced</td><td> Farming-fishing</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> State-gov</td><td>174675.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Wife</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>43.0</td><td> Private</td><td>227065.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>43.0</td><td> United-States</td><td> >50K</td></tr><tr><td>51.0</td><td> Private</td><td>269080.0</td><td> 7th-8th</td><td>4.0</td><td> Widowed</td><td> Other-service</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>18.0</td><td> Private</td><td>177722.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>51.0</td><td> Private</td><td>133461.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>239683.0</td><td> 10th</td><td>6.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> ?</td><td> <=50K</td></tr><tr><td>44.0</td><td> Self-emp-inc</td><td>398473.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>70.0</td><td> United-States</td><td> >50K</td></tr><tr><td>33.0</td><td> Local-gov</td><td>298785.0</td><td> 10th</td><td>6.0</td><td> Divorced</td><td> Transport-moving</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>33.0</td><td> Self-emp-not-inc</td><td>123424.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>42.0</td><td> Private</td><td>176286.0</td><td> Assoc-acdm</td><td>12.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>25.0</td><td> Private</td><td>150062.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>169240.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Machine-op-inspct</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>38.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>32.0</td><td> Private</td><td>288273.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>70.0</td><td> Mexico</td><td> <=50K</td></tr><tr><td>36.0</td><td> Private</td><td>526968.0</td><td> 10th</td><td>6.0</td><td> Divorced</td><td> Exec-managerial</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>28.0</td><td> Private</td><td>57066.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Transport-moving</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>20.0</td><td> Private</td><td>323573.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>35.0</td><td> Self-emp-inc</td><td>368825.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Sales</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>60.0</td><td> United-States</td><td> >50K</td></tr><tr><td>55.0</td><td> Self-emp-not-inc</td><td>189721.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>48.0</td><td> Private</td><td>164966.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> Asian-Pac-Islander</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> India</td><td> >50K</td></tr><tr><td>36.0</td><td> ?</td><td>94954.0</td><td> Assoc-voc</td><td>11.0</td><td> Widowed</td><td> ?</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>202046.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> >50K</td></tr><tr><td>28.0</td><td> Private</td><td>161538.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Tech-support</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>67.0</td><td> Private</td><td>105252.0</td><td> Bachelors</td><td>13.0</td><td> Widowed</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>2392.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>37.0</td><td> Private</td><td>200153.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>32185.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Transport-moving</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>70.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>25.0</td><td> Private</td><td>178326.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>21.0</td><td> Private</td><td>255957.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Female</td><td>4101.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>40.0</td><td> State-gov</td><td>188693.0</td><td> Masters</td><td>14.0</td><td> Married-civ-spouse</td><td> Prof-specialty</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> >50K</td></tr><tr><td>78.0</td><td> Private</td><td>182977.0</td><td> HS-grad</td><td>9.0</td><td> Widowed</td><td> Other-service</td><td> Not-in-family</td><td> Black</td><td> Female</td><td>2964.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>34.0</td><td> Private</td><td>159929.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Handlers-cleaners</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>49.0</td><td> Private</td><td>123207.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>44.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>22.0</td><td> Private</td><td>284317.0</td><td> Assoc-acdm</td><td>12.0</td><td> Never-married</td><td> Adm-clerical</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>23.0</td><td> ?</td><td>184699.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> ?</td><td> Unmarried</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>60.0</td><td> Self-emp-not-inc</td><td>154474.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Farming-fishing</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>42.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>45.0</td><td> Local-gov</td><td>318280.0</td><td> HS-grad</td><td>9.0</td><td> Widowed</td><td> Protective-serv</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> >50K</td></tr><tr><td>63.0</td><td> Private</td><td>254907.0</td><td> Assoc-voc</td><td>11.0</td><td> Divorced</td><td> Other-service</td><td> Not-in-family</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>20.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>349221.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Craft-repair</td><td> Own-child</td><td> Black</td><td> Female</td><td>0.0</td><td>0.0</td><td>35.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>47.0</td><td> Private</td><td>335973.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Adm-clerical</td><td> Unmarried</td><td> White</td><td> Female</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>44.0</td><td> Private</td><td>126701.0</td><td> HS-grad</td><td>9.0</td><td> Divorced</td><td> Craft-repair</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>51.0</td><td> Private</td><td>122159.0</td><td> Some-college</td><td>10.0</td><td> Widowed</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Female</td><td>3325.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>46.0</td><td> Private</td><td>187370.0</td><td> Bachelors</td><td>13.0</td><td> Never-married</td><td> Sales</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>1504.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>41.0</td><td> Private</td><td>194636.0</td><td> Assoc-voc</td><td>11.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>50.0</td><td> Self-emp-not-inc</td><td>124793.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Craft-repair</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>30.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>47.0</td><td> Private</td><td>192835.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Adm-clerical</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>35.0</td><td> Private</td><td>290226.0</td><td> HS-grad</td><td>9.0</td><td> Never-married</td><td> Exec-managerial</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>56.0</td><td> Private</td><td>112840.0</td><td> HS-grad</td><td>9.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>55.0</td><td> United-States</td><td> >50K</td></tr><tr><td>45.0</td><td> Private</td><td>89325.0</td><td> Masters</td><td>14.0</td><td> Divorced</td><td> Prof-specialty</td><td> Not-in-family</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>45.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>48.0</td><td> Federal-gov</td><td>33109.0</td><td> Bachelors</td><td>13.0</td><td> Divorced</td><td> Exec-managerial</td><td> Unmarried</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>58.0</td><td> United-States</td><td> >50K</td></tr><tr><td>40.0</td><td> Private</td><td>82465.0</td><td> Some-college</td><td>10.0</td><td> Married-civ-spouse</td><td> Machine-op-inspct</td><td> Husband</td><td> White</td><td> Male</td><td>2580.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr><tr><td>39.0</td><td> Self-emp-inc</td><td>329980.0</td><td> Bachelors</td><td>13.0</td><td> Married-civ-spouse</td><td> Exec-managerial</td><td> Husband</td><td> White</td><td> Male</td><td>15024.0</td><td>0.0</td><td>50.0</td><td> United-States</td><td> >50K</td></tr><tr><td>20.0</td><td> Private</td><td>148294.0</td><td> Some-college</td><td>10.0</td><td> Never-married</td><td> Other-service</td><td> Own-child</td><td> White</td><td> Male</td><td>0.0</td><td>0.0</td><td>40.0</td><td> United-States</td><td> <=50K</td></tr></tbody></table></div>"]}}],"execution_count":20},{"cell_type":"markdown","source":["Below we will do a similar calculation to what we did above but using the DataFrames API"],"metadata":{}},{"cell_type":"code","source":["# import what we will need\nfrom pyspark.sql.functions import when, col, mean, desc, round\n\n# wrangle the data a bit\ndf_result = df_adult.select(\n  df_adult['occupation'], \n  # create a 1/0 type col on the fly\n  when( col('marital_status') == ' Divorced' , 1 ).otherwise(0).alias('is_divorced')\n)\n# do grouping (and a round)\ndf_result = df_result.groupBy('occupation').agg(round(mean('is_divorced'),2).alias('divorced_rate'))\n# do ordering\ndf_result = df_result.orderBy(desc('divorced_rate'))\n# show results\ndf_result.show(5)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">+----------------+-------------+\n      occupation|divorced_rate|\n+----------------+-------------+\n    Adm-clerical|         0.22|\n Priv-house-serv|         0.19|\n    Tech-support|         0.15|\n   Other-service|         0.15|\n Exec-managerial|         0.15|\n+----------------+-------------+\nonly showing top 5 rows\n\n</div>"]}}],"execution_count":22},{"cell_type":"markdown","source":["As you can see the dataframes api is a bit more verbose then just expressing what you want to do in standard SQL.<br><br>But some prefer it and might be more used to it, and there could be cases where expressing what you need to do might just be better using the DataFrame API if it is too complicated for a simple SQL expression for example of maybe involves recursion of some type."],"metadata":{}},{"cell_type":"markdown","source":["####<span style=\"color:darkblue\">Question 2</span>\n1. Write some pyspark to get the top 'bachelor_rate' by 'education' group using DataFrame operations?"],"metadata":{}},{"cell_type":"code","source":["from pyspark.sql.functions import when, col, mean, desc, round\n\ndf_result = df_adult.select(\n  df_adult['education'], \n  when( col('marital_status') == ' Never-married' , 1 ).otherwise(0).alias('is_bachelor')\n)\ndf_result = df_result.groupBy('education').agg(round(mean('is_bachelor'),2).alias('bachelor_rate'))\ndf_result = df_result.orderBy(desc('bachelor_rate'))\n\n#show results\ndf_result.show(5)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">+-------------+-------------+\n    education|bachelor_rate|\n+-------------+-------------+\n         12th|         0.54|\n         11th|          0.5|\n    Preschool|         0.43|\n Some-college|          0.4|\n         10th|         0.39|\n+-------------+-------------+\nonly showing top 5 rows\n\n</div>"]}}],"execution_count":25},{"cell_type":"markdown","source":["####Explore & Visualize Data\nIt's very easy to [collect()](https://spark.apache.org/docs/latest/rdd-programming-guide.html#printing-elements-of-an-rdd) your Spark DataFrame data into a Pandas df and then continue to analyse or plot as you might normally.\n<br><br>\nObviously if you try to collect() a huge DataFrame then you will run into issues, so usually you would only collect aggregated or sampled data into a Pandas df."],"metadata":{}},{"cell_type":"code","source":["import pandas as pd\n\n# do some analysis\nresult = spark.sql(\n  \"\"\"\n  SELECT \n    occupation,\n    AVG(IF(income = ' >50K',1,0)) as plus_50k\n  FROM \n    adult \n  GROUP BY 1\n  ORDER BY 2 DESC\n  \"\"\")\n\n# collect results into a pandas df\ndf_pandas = pd.DataFrame(\n  result.collect(),\n  columns=result.schema.names\n)\n\n# look at df\nprint(df_pandas.head())"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">         occupation  plus_50k\n0   Exec-managerial  0.484014\n1    Prof-specialty  0.449034\n2   Protective-serv  0.325116\n3      Tech-support  0.304957\n4             Sales  0.269315\n</div>"]}}],"execution_count":27},{"cell_type":"code","source":["print(df_pandas.describe())"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">        plus_50k\ncount  15.000000\nmean    0.197357\nstd     0.143993\nmin     0.006711\n25%     0.107373\n50%     0.134518\n75%     0.287136\nmax     0.484014\n</div>"]}}],"execution_count":28},{"cell_type":"code","source":["print(df_pandas.info())"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">&lt;class &apos;pandas.core.frame.DataFrame&apos;&gt;\nRangeIndex: 15 entries, 0 to 14\nData columns (total 2 columns):\noccupation    15 non-null object\nplus_50k      15 non-null float64\ndtypes: float64(1), object(1)\nmemory usage: 312.0+ bytes\nNone\n</div>"]}}],"execution_count":29},{"cell_type":"markdown","source":["Here we will just do some very basic plotting to show how you might collect what you are interested in into a Pandas DF and then just plot any way you normally would.\n\nFor simplicity we are going to use the plotting functionality built into pandas (you could make this a pretty as you want)."],"metadata":{}},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n\n# i like ggplot style\nplt.style.use('ggplot')\n\n# get simple plot on the pandas data\nmyplot = df_pandas.plot(kind='barh', x='occupation', y='plus_50k')\n\n# display the plot (note - display() is a databricks function - \n# more info on plotting in Databricks is here: https://docs.databricks.com/user-guide/visualizations/matplotlib-and-ggplot.html)\ndisplay(myplot.figure)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"image/png":["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"]}}],"execution_count":31},{"cell_type":"markdown","source":["You can also easily get summary stats on a Spark DataFrame like below. [Here](https://databricks.com/blog/2015/06/02/statistical-and-mathematical-functions-with-dataframes-in-spark.html) is a nice blog post that has more examples.<br><br>So this is an example of why you might want to move from Spark SQL into DataFrames API as being able to just call describe() on the Spark DF is easier then trying to do the equivilant in Spark SQL."],"metadata":{}},{"cell_type":"code","source":["# describe df\ndf_adult.select(df_adult['age'],df_adult['education_num']).describe().show()"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">+-------+------------------+------------------+\nsummary|               age|     education_num|\n+-------+------------------+------------------+\n  count|             32560|             32560|\n   mean|38.581633906633904| 10.08058968058968|\n stddev|13.640641827464002|2.5727089681052058|\n    min|              17.0|               1.0|\n    max|              90.0|              16.0|\n+-------+------------------+------------------+\n\n</div>"]}}],"execution_count":33},{"cell_type":"markdown","source":["### ML Pipeline - Logistic Regression vs Random Forest\n\nBelow we will create two [Spark ML Pipelines](https://spark.apache.org/docs/latest/ml-pipeline.html) - one that fits a logistic regression and one that fits a random forest. We will then compare the performance of each.\n\n**Note**: A lot of the code below is adapted from [this example](https://docs.databricks.com/spark/latest/mllib/binary-classification-mllib-pipelines.html)."],"metadata":{}},{"cell_type":"code","source":["from pyspark.ml import Pipeline\nfrom pyspark.ml.feature import OneHotEncoderEstimator, StringIndexer, VectorAssembler\n\ncategoricalColumns = [\"workclass\", \"education\", \"marital_status\", \"occupation\", \"relationship\", \"race\", \"sex\", \"native_country\"]\nstages = [] # stages in our Pipeline\n\nfor categoricalCol in categoricalColumns:\n    # Category Indexing with StringIndexer\n    stringIndexer = StringIndexer(inputCol=categoricalCol, outputCol=categoricalCol + \"Index\")\n    # Use OneHotEncoder to convert categorical variables into binary SparseVectors\n    # encoder = OneHotEncoderEstimator(inputCol=categoricalCol + \"Index\", outputCol=categoricalCol + \"classVec\")\n    encoder = OneHotEncoderEstimator(inputCols=[stringIndexer.getOutputCol()], outputCols=[categoricalCol + \"classVec\"])\n    # Add stages.  These are not run here, but will run all at once later on.\n    stages += [stringIndexer, encoder]"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":35},{"cell_type":"code","source":["# Convert label into label indices using the StringIndexer\nlabel_stringIdx = StringIndexer(inputCol=\"income\", outputCol=\"label\")\nstages += [label_stringIdx]"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":36},{"cell_type":"code","source":["# Transform all features into a vector using VectorAssembler\nnumericCols = [\"age\", \"fnlwgt\", \"education_num\", \"capital_gain\", \"capital_loss\", \"hours_per_week\"]\nassemblerInputs = [c + \"classVec\" for c in categoricalColumns] + numericCols\nassembler = VectorAssembler(inputCols=assemblerInputs, outputCol=\"features\")\nstages += [assembler]"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":37},{"cell_type":"code","source":["# Create a Pipeline.\npipeline = Pipeline(stages=stages)\n# Run the feature transformations.\n#  - fit() computes feature statistics as needed.\n#  - transform() actually transforms the features.\npipelineModel = pipeline.fit(df_adult)\ndataset = pipelineModel.transform(df_adult)\n# Keep relevant columns\nselectedcols = [\"label\", \"features\"] + cols\ndataset = dataset.select(selectedcols)\ndisplay(dataset)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":38},{"cell_type":"code","source":["### Randomly split data into training and test sets. set seed for reproducibility\n(trainingData, testData) = dataset.randomSplit([0.7, 0.3], seed=100)\nprint(trainingData.count())\nprint(testData.count())"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansired\">---------------------------------------------------------------------------</span>\n<span class=\"ansired\">NameError</span>                                 Traceback (most recent call last)\n<span class=\"ansigreen\">&lt;command-415717336807221&gt;</span> in <span class=\"ansicyan\">&lt;module&gt;</span><span class=\"ansiblue\">()</span>\n<span class=\"ansigreen\">      1</span> <span class=\"ansired\">### Randomly split data into training and test sets. set seed for reproducibility</span><span class=\"ansiyellow\"></span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">----&gt; 2</span><span class=\"ansiyellow\"> </span><span class=\"ansiyellow\">(</span>trainingData<span class=\"ansiyellow\">,</span> testData<span class=\"ansiyellow\">)</span> <span class=\"ansiyellow\">=</span> dataset<span class=\"ansiyellow\">.</span>randomSplit<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">[</span><span class=\"ansicyan\">0.7</span><span class=\"ansiyellow\">,</span> <span class=\"ansicyan\">0.3</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">,</span> seed<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">100</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">      3</span> print<span class=\"ansiyellow\">(</span>trainingData<span class=\"ansiyellow\">.</span>count<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">      4</span> print<span class=\"ansiyellow\">(</span>testData<span class=\"ansiyellow\">.</span>count<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansired\">NameError</span>: name &apos;dataset&apos; is not defined</div>"]}}],"execution_count":39},{"cell_type":"code","source":["from pyspark.sql.functions import avg\n\n# get the rate of the positive outcome from the training data to use as a threshold in the model\ntraining_data_positive_rate = trainingData.select(avg(trainingData['label'])).collect()[0][0] \n\nprint(\"Positive rate in the training data is {}\".format(training_data_positive_rate))"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansired\">---------------------------------------------------------------------------</span>\n<span class=\"ansired\">IndexError</span>                                Traceback (most recent call last)\n<span class=\"ansigreen\">&lt;command-415717336807249&gt;</span> in <span class=\"ansicyan\">&lt;module&gt;</span><span class=\"ansiblue\">()</span>\n<span class=\"ansigreen\">      1</span> <span class=\"ansigreen\">from</span> pyspark<span class=\"ansiyellow\">.</span>sql<span class=\"ansiyellow\">.</span>functions <span class=\"ansigreen\">import</span> avg<span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">      2</span> <span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">----&gt; 3</span><span class=\"ansiyellow\"> </span>trainingData<span class=\"ansiyellow\">.</span>select<span class=\"ansiyellow\">(</span>avg<span class=\"ansiyellow\">(</span>trainingData<span class=\"ansiyellow\">[</span><span class=\"ansiblue\">&apos;label&apos;</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">.</span>take<span class=\"ansiyellow\">(</span><span class=\"ansicyan\">1</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">[</span><span class=\"ansicyan\">1</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">.</span>cast<span class=\"ansiyellow\">(</span><span class=\"ansiblue\">&apos;float&apos;</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansired\">IndexError</span>: list index out of range</div>"]}}],"execution_count":40},{"cell_type":"markdown","source":["####Logistic Regression - Train"],"metadata":{}},{"cell_type":"code","source":["from pyspark.ml.classification import LogisticRegression\n\n# Create initial LogisticRegression model\nlr = LogisticRegression(labelCol=\"label\", featuresCol=\"features\", maxIter=10)\n\n# set threshold for the probability above which to predict a 1\nlr.setThreshold(training_data_positive_rate)\n# lr.setThreshold(0.5) # could use this if knew you had balanced data\n\n# Train model with Training Data\nlrModel = lr.fit(trainingData)\n\n# get training summary used for eval metrics and other params\nlrTrainingSummary = lrModel.summary\n\n# Find the best model threshold if you would like to use that instead of the empirical positve rate\nfMeasure = lrTrainingSummary.fMeasureByThreshold\nmaxFMeasure = fMeasure.groupBy().max('F-Measure').select('max(F-Measure)').head()\nlrBestThreshold = fMeasure.where(fMeasure['F-Measure'] == maxFMeasure['max(F-Measure)']) \\\n    .select('threshold').head()['threshold']\n  \nprint(\"Best threshold based on model performance on training data is {}\".format(lrBestThreshold))"],"metadata":{},"outputs":[],"execution_count":42},{"cell_type":"markdown","source":["####GBM - Train"],"metadata":{}},{"cell_type":"markdown","source":["####<span style=\"color:darkblue\">Question 3</span>\n1. Train a GBTClassifier on the training data, call the trained model 'gbModel'"],"metadata":{}},{"cell_type":"code","source":["from pyspark.ml.classification import GBTClassifier\ngb = GBTClassifier(labelCol=\"label\", featuresCol=\"features\", maxIter=10)\ngbModel = gb.fit(trainingData)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansired\">---------------------------------------------------------------------------</span>\n<span class=\"ansired\">NameError</span>                                 Traceback (most recent call last)\n<span class=\"ansigreen\">&lt;command-415717336807262&gt;</span> in <span class=\"ansicyan\">&lt;module&gt;</span><span class=\"ansiblue\">()</span>\n<span class=\"ansigreen\">      7</span> <span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">      8</span> <span class=\"ansired\"># Train model with Training Data</span><span class=\"ansiyellow\"></span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">----&gt; 9</span><span class=\"ansiyellow\"> </span>gbModel <span class=\"ansiyellow\">=</span> gb<span class=\"ansiyellow\">.</span>fit<span class=\"ansiyellow\">(</span>trainingData<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansired\">NameError</span>: name &apos;trainingData&apos; is not defined</div>"]}}],"execution_count":45},{"cell_type":"markdown","source":["####Logistic Regression - Predict"],"metadata":{}},{"cell_type":"code","source":["# make predictions on test data\nlrPredictions = lrModel.transform(testData)\n\n# display predictions\ndisplay(lrPredictions.select(\"label\", \"prediction\", \"probability\"))\n#display(lrPredictions)"],"metadata":{},"outputs":[],"execution_count":47},{"cell_type":"markdown","source":["###GBM - Predict"],"metadata":{}},{"cell_type":"markdown","source":["####<span style=\"color:darkblue\">Question 4</span>\n1. Get predictions on the test data for your GBTClassifier. Call the predictions df 'gbPredictions'."],"metadata":{}},{"cell_type":"code","source":["### Question 4.1 Answer ###\n\n# make predictions on test data\ngbPredictions = gbModel.transform(testData)\n\ndisplay(gbPredictions)\n"],"metadata":{},"outputs":[],"execution_count":50},{"cell_type":"markdown","source":["####Logistic Regression - Evaluate"],"metadata":{}},{"cell_type":"markdown","source":["####<span style=\"color:darkblue\">Question 5</span>\n1. Complete the print_performance_metrics() function below to also include measures of F1, Precision, Recall, False Positive Rate and True Positive Rate."],"metadata":{}},{"cell_type":"code","source":["from pyspark.ml.evaluation import BinaryClassificationEvaluator\nfrom pyspark.mllib.evaluation import BinaryClassificationMetrics, MulticlassMetrics\n\ndef print_performance_metrics(predictions):\n  # Evaluate model\n  evaluator = BinaryClassificationEvaluator(rawPredictionCol=\"rawPrediction\")\n  auc = evaluator.evaluate(predictions, {evaluator.metricName: \"areaUnderROC\"})\n  aupr = evaluator.evaluate(predictions, {evaluator.metricName: \"areaUnderPR\"})\n  print(\"auc = {}\".format(auc))\n  print(\"aupr = {}\".format(aupr))\n\n  # get rdd of predictions and labels for mllib eval metrics\n  predictionAndLabels = predictions.select(\"prediction\",\"label\").rdd\n\n  # Instantiate metrics objects\n  binary_metrics = BinaryClassificationMetrics(predictionAndLabels)\n  multi_metrics = MulticlassMetrics(predictionAndLabels)\n\n  # Area under precision-recall curve\n  print(\"Area under PR = {}\".format(binary_metrics.areaUnderPR))\n  # Area under ROC curve\n  print(\"Area under ROC = {}\".format(binary_metrics.areaUnderROC))\n  # Accuracy\n  print(\"Accuracy = {}\".format(multi_metrics.accuracy))\n  # Confusion Matrix\n  print(multi_metrics.confusionMatrix())\n  \n  ### Question 5.1 Answer ###\n  \n  # F1\n  print(\"F1 = {}\".format(multi_metrics.fMeasure()))\n  # Precision\n  print(\"Precision = {}\".format(multi_metrics.precision()))\n  # Recall\n  print(\"Recall = {}\".format(multi_metrics.recall()))\n  # FPR\n  print(\"FPR = {}\".format(multi_metrics.falsePositiveRate(0.0)))\n  # TPR\n  print(\"TPR = {}\".format(multi_metrics.truePositiveRate(1.0)))\n  \n  \nprint_performance_metrics(lrPredictions)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansicyan\">  File </span><span class=\"ansigreen\">&quot;&lt;command-383991067828018&gt;&quot;</span><span class=\"ansicyan\">, line </span><span class=\"ansigreen\">33</span>\n<span class=\"ansiyellow\">    print(&quot;Precision = {}&quot;.format(# fill in here))</span>\n<span class=\"ansigrey\">        ^</span>\n<span class=\"ansired\">SyntaxError</span><span class=\"ansired\">:</span> invalid syntax\n</div>"]}}],"execution_count":53},{"cell_type":"markdown","source":["####GBM - Evaluate"],"metadata":{}},{"cell_type":"code","source":["print_performance_metrics(gbPredictions)"],"metadata":{},"outputs":[],"execution_count":55},{"cell_type":"markdown","source":["## Cross Validation"],"metadata":{}},{"cell_type":"markdown","source":["For each model you can run the below comand to see its params and a brief explanation of each."],"metadata":{}},{"cell_type":"code","source":["print(lr.explainParams())"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansicyan\">  File </span><span class=\"ansigreen\">&quot;&lt;command-415717336807228&gt;&quot;</span><span class=\"ansicyan\">, line </span><span class=\"ansigreen\">1</span>\n<span class=\"ansiyellow\">    (lr.explainParams()</span>\n<span class=\"ansigrey\">                       ^</span>\n<span class=\"ansired\">SyntaxError</span><span class=\"ansired\">:</span> unexpected EOF while parsing\n</div>"]}}],"execution_count":58},{"cell_type":"code","source":["print(gb.explainParams())"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansired\">---------------------------------------------------------------------------</span>\n<span class=\"ansired\">NameError</span>                                 Traceback (most recent call last)\n<span class=\"ansigreen\">&lt;command-415717336807260&gt;</span> in <span class=\"ansicyan\">&lt;module&gt;</span><span class=\"ansiblue\">()</span>\n<span class=\"ansigreen\">----&gt; 1</span><span class=\"ansiyellow\"> </span>print<span class=\"ansiyellow\">(</span>rf<span class=\"ansiyellow\">.</span>explainParams<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansired\">NameError</span>: name &apos;rf&apos; is not defined</div>"]}}],"execution_count":59},{"cell_type":"markdown","source":["####Logisitic Regression - Param Grid"],"metadata":{}},{"cell_type":"code","source":["from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n\n# Create ParamGrid for Cross Validation\nlrParamGrid = (ParamGridBuilder()\n             .addGrid(lr.regParam, [0.01, 0.5, 2.0])\n             .addGrid(lr.elasticNetParam, [0.0, 0.5, 1.0])\n             .addGrid(lr.maxIter, [2, 5])\n             .build())"],"metadata":{},"outputs":[],"execution_count":61},{"cell_type":"markdown","source":["####GBM - Param Grid"],"metadata":{}},{"cell_type":"markdown","source":["####<span style=\"color:darkblue\">Question 6</span>\n1. Build out a param grid for the gb model, call it 'gbParamGrid'."],"metadata":{}},{"cell_type":"code","source":["### Question 6.1 Answer ###\ngbParamGrid = (ParamGridBuilder()\n               .addGrid(gb.maxDepth, [2,5])\n               .addGrid(gb.maxIter, [50,100])\n               .build())\n"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansired\">---------------------------------------------------------------------------</span>\n<span class=\"ansired\">NameError</span>                                 Traceback (most recent call last)\n<span class=\"ansigreen\">&lt;command-383991067828029&gt;</span> in <span class=\"ansicyan\">&lt;module&gt;</span><span class=\"ansiblue\">()</span>\n<span class=\"ansigreen\">      1</span> <span class=\"ansired\">### Question 6.1 Answer ###</span><span class=\"ansiyellow\"></span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">----&gt; 2</span><span class=\"ansiyellow\"> gbParamGrid = (ParamGridBuilder()\n</span><span class=\"ansigreen\">      3</span>                <span class=\"ansiyellow\">.</span>addGrid<span class=\"ansiyellow\">(</span>gb<span class=\"ansiyellow\">.</span>maxDepth<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">[</span><span class=\"ansicyan\">2</span><span class=\"ansiyellow\">,</span><span class=\"ansicyan\">5</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">      4</span>                <span class=\"ansiyellow\">.</span>addGrid<span class=\"ansiyellow\">(</span>gb<span class=\"ansiyellow\">.</span>maxIter<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">[</span><span class=\"ansicyan\">50</span><span class=\"ansiyellow\">,</span><span class=\"ansicyan\">100</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">      5</span>                .build())\n\n<span class=\"ansired\">NameError</span>: name &apos;ParamGridBuilder&apos; is not defined</div>"]}}],"execution_count":64},{"cell_type":"markdown","source":["####Logistic Regression - Perform Cross Validation"],"metadata":{}},{"cell_type":"code","source":["# set up an evaluator\nevaluator = BinaryClassificationEvaluator(rawPredictionCol=\"rawPrediction\")\n\n# Create CrossValidator\nlrCv = CrossValidator(estimator=lr, estimatorParamMaps=lrParamGrid, evaluator=evaluator, numFolds=2)\n\n# Run cross validations\nlrCvModel = lrCv.fit(trainingData)\n# this will likely take a fair amount of time because of the amount of models that we're creating and testing"],"metadata":{},"outputs":[],"execution_count":66},{"cell_type":"code","source":["# below approach to getting at the best params from the best cv model taken from:\n# https://stackoverflow.com/a/46353730/1919374\n\n# look at best params from the CV\nprint(lrCvModel.bestModel._java_obj.getRegParam())\nprint(lrCvModel.bestModel._java_obj.getElasticNetParam())\nprint(lrCvModel.bestModel._java_obj.getMaxIter())"],"metadata":{},"outputs":[],"execution_count":67},{"cell_type":"markdown","source":["####GBM - Perform Cross Validation"],"metadata":{}},{"cell_type":"markdown","source":["####<span style=\"color:darkblue\">Question 7</span>\n1. Perform cross validation of params on your 'gb' model.\n1. Print out the best params you found."],"metadata":{}},{"cell_type":"code","source":["### Question 7.1 Answer ###\n\n# Create CrossValidator\ngbCv = CrossValidator(estimator=gb, estimatorParamMaps=gbParamGrid, evaluator=evaluator, numFolds=2)\n\n# Run cross validations\ngbCvModel = gbCv.fit(trainingData)"],"metadata":{},"outputs":[],"execution_count":70},{"cell_type":"code","source":["### Question 7.2 Answer ###\n\n# look at best params from the CV\nprint(gbCvModel.bestModel._java_obj.getMaxDepth())\nprint(gbCvModel.bestModel._java_obj.getMaxIter())\n"],"metadata":{},"outputs":[],"execution_count":71},{"cell_type":"markdown","source":["####Logistic Regression - CV Model Predict"],"metadata":{}},{"cell_type":"code","source":["# Use test set to measure the accuracy of our model on new data\nlrCvPredictions = lrCvModel.transform(testData)\n\ndisplay(lrCvPredictions)"],"metadata":{},"outputs":[],"execution_count":73},{"cell_type":"markdown","source":["####GBM - CV Model Predict"],"metadata":{}},{"cell_type":"code","source":["gbCvPredictions = gbCvModel.transform(testData)\n\ndisplay(gbCvPredictions)"],"metadata":{},"outputs":[],"execution_count":75},{"cell_type":"markdown","source":["####Logistic Regression - CV Model Evaluate"],"metadata":{}},{"cell_type":"code","source":["print_performance_metrics(lrCvPredictions)"],"metadata":{},"outputs":[],"execution_count":77},{"cell_type":"markdown","source":["####GBM - CV Model Evaluate"],"metadata":{}},{"cell_type":"code","source":["print_performance_metrics(gbCvPredictions)"],"metadata":{},"outputs":[],"execution_count":79},{"cell_type":"markdown","source":["####Logistic Regression - Model Explore"],"metadata":{}},{"cell_type":"code","source":["print('Model Intercept: ', lrCvModel.bestModel.intercept)"],"metadata":{},"outputs":[],"execution_count":81},{"cell_type":"code","source":["lrWeights = lrCvModel.bestModel.coefficients\nlrWeights = [(float(w),) for w in lrWeights]  # convert numpy type to float, and to tuple\nlrWeightsDF = sqlContext.createDataFrame(lrWeights, [\"Feature Weight\"])\ndisplay(lrWeightsDF)"],"metadata":{},"outputs":[],"execution_count":82},{"cell_type":"markdown","source":["### Feature Importance"],"metadata":{}},{"cell_type":"markdown","source":["####<span style=\"color:darkblue\">Question 8</span>\n1. Print out a table of feature_name and feature_coefficient from the Logistic Regression model.\n<br><br>\nHint: Adapt the code from here: https://stackoverflow.com/questions/42935914/how-to-map-features-from-the-output-of-a-vectorassembler-back-to-the-column-name"],"metadata":{}},{"cell_type":"code","source":["### Question 8.1 Answer ###\n\nrom itertools import chain\n# from: https://stackoverflow.com/questions/42935914/how-to-map-features-from-the-output-of-a-vectorassembler-back-to-the-column-name\n#.summary.featuresCol\ntransformed =  lrCvModel.bestModel.transform(dataset)\n\nattrs = sorted(\n    (attr[\"idx\"], attr[\"name\"]) for attr in (chain(*transformed\n        .schema[lrCvModel.bestModel.summary.featuresCol]\n        .metadata[\"ml_attr\"][\"attrs\"].values())))\n\ncoefSummary = pd.DataFrame([(name, lrCvModel.bestModel.coefficients[idx]) for idx, name in attrs], columns = ['feature', 'coef'])\nprint(coefSummary.sort_values(['coef'], ascending = False))"],"metadata":{},"outputs":[],"execution_count":85},{"cell_type":"code","source":["gbCvFeatureImportance = pd.DataFrame([(name, gbCvModel.bestModel.featureImportances[idx]) for idx, name in attrs],columns=['feature_name','feature_importance'])\n\nprint(gbCvFeatureImportance.sort_values(by=['feature_importance'],ascending =False))"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansired\">---------------------------------------------------------------------------</span>\n<span class=\"ansired\">AttributeError</span>                            Traceback (most recent call last)\n<span class=\"ansigreen\">&lt;command-415717336807283&gt;</span> in <span class=\"ansicyan\">&lt;module&gt;</span><span class=\"ansiblue\">()</span>\n<span class=\"ansigreen\">----&gt; 1</span><span class=\"ansiyellow\"> </span>gbCvFeatureImportance <span class=\"ansiyellow\">=</span> pd<span class=\"ansiyellow\">.</span>DataFrame<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">[</span><span class=\"ansiyellow\">(</span>name<span class=\"ansiyellow\">,</span> gbCvModel<span class=\"ansiyellow\">.</span>bestmodel<span class=\"ansiyellow\">.</span>featureImportances<span class=\"ansiyellow\">[</span>idx<span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">)</span> <span class=\"ansigreen\">for</span> idx<span class=\"ansiyellow\">,</span> name <span class=\"ansigreen\">in</span> attrs<span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">,</span>columns<span class=\"ansiyellow\">=</span><span class=\"ansiyellow\">[</span><span class=\"ansiblue\">&apos;feature_name&apos;</span><span class=\"ansiyellow\">,</span><span class=\"ansiblue\">&apos;feature_importance&apos;</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">      2</span> print<span class=\"ansiyellow\">(</span>gbCvFeatureImportance<span class=\"ansiyellow\">.</span>sort_values<span class=\"ansiyellow\">(</span>by<span class=\"ansiyellow\">=</span><span class=\"ansiyellow\">[</span><span class=\"ansiblue\">&apos;feature_importance&apos;</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">,</span>ascending <span class=\"ansiyellow\">=</span><span class=\"ansigreen\">False</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansigreen\">&lt;command-415717336807283&gt;</span> in <span class=\"ansicyan\">&lt;listcomp&gt;</span><span class=\"ansiblue\">(.0)</span>\n<span class=\"ansigreen\">----&gt; 1</span><span class=\"ansiyellow\"> </span>gbCvFeatureImportance <span class=\"ansiyellow\">=</span> pd<span class=\"ansiyellow\">.</span>DataFrame<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">[</span><span class=\"ansiyellow\">(</span>name<span class=\"ansiyellow\">,</span> gbCvModel<span class=\"ansiyellow\">.</span>bestmodel<span class=\"ansiyellow\">.</span>featureImportances<span class=\"ansiyellow\">[</span>idx<span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">)</span> <span class=\"ansigreen\">for</span> idx<span class=\"ansiyellow\">,</span> name <span class=\"ansigreen\">in</span> attrs<span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">,</span>columns<span class=\"ansiyellow\">=</span><span class=\"ansiyellow\">[</span><span class=\"ansiblue\">&apos;feature_name&apos;</span><span class=\"ansiyellow\">,</span><span class=\"ansiblue\">&apos;feature_importance&apos;</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">      2</span> print<span class=\"ansiyellow\">(</span>gbCvFeatureImportance<span class=\"ansiyellow\">.</span>sort_values<span class=\"ansiyellow\">(</span>by<span class=\"ansiyellow\">=</span><span class=\"ansiyellow\">[</span><span class=\"ansiblue\">&apos;feature_importance&apos;</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">,</span>ascending <span class=\"ansiyellow\">=</span><span class=\"ansigreen\">False</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansired\">AttributeError</span>: &apos;CrossValidatorModel&apos; object has no attribute &apos;bestmodel&apos;</div>"]}}],"execution_count":86},{"cell_type":"markdown","source":["####<span style=\"color:darkblue\">Question 9</span>\n1. Build and train a RandomForestClassifier and print out a table of feature importances from it."],"metadata":{}},{"cell_type":"code","source":["### Question 9.1 Answer ###\n\nfrom pyspark.ml.classification import RandomForestClassifier\n\nrf = RandomForestClassifier(numTrees=100, maxDepth=2, labelCol=\"label\", seed=42)\n\nrfModel = rf.fit(trainingData)\n\nrfFeatureImportance = pd.DataFrame([(name, rfModel.featureImportances[idx]) for idx, name in attrs],columns=['feature_name','feature_importance'])\n\nprint(rfFeatureImportance.sort_values(by = ['feature_importance'], ascending = False))"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansired\">---------------------------------------------------------------------------</span>\n<span class=\"ansired\">AttributeError</span>                            Traceback (most recent call last)\n<span class=\"ansigreen\">&lt;command-415717336807284&gt;</span> in <span class=\"ansicyan\">&lt;module&gt;</span><span class=\"ansiblue\">()</span>\n<span class=\"ansigreen\">      2</span> rf <span class=\"ansiyellow\">=</span> RandomForestClassifier<span class=\"ansiyellow\">(</span>labelCol<span class=\"ansiyellow\">=</span><span class=\"ansiblue\">&quot;label&quot;</span><span class=\"ansiyellow\">,</span> featuresCol<span class=\"ansiyellow\">=</span><span class=\"ansiblue\">&quot;features&quot;</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">      3</span> rfModel <span class=\"ansiyellow\">=</span> rf<span class=\"ansiyellow\">.</span>fit<span class=\"ansiyellow\">(</span>trainingData<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">----&gt; 4</span><span class=\"ansiyellow\"> </span>rfFeatureImportance <span class=\"ansiyellow\">=</span> pd<span class=\"ansiyellow\">.</span>DataFrame<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">[</span><span class=\"ansiyellow\">(</span>name<span class=\"ansiyellow\">,</span> rfModel<span class=\"ansiyellow\">.</span>bestModel<span class=\"ansiyellow\">.</span>featureImportances<span class=\"ansiyellow\">[</span>idx<span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">)</span> <span class=\"ansigreen\">for</span> idx<span class=\"ansiyellow\">,</span> name <span class=\"ansigreen\">in</span> attrs<span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">,</span>columns<span class=\"ansiyellow\">=</span><span class=\"ansiyellow\">[</span><span class=\"ansiblue\">&apos;feature_name&apos;</span><span class=\"ansiyellow\">,</span><span class=\"ansiblue\">&apos;feature_importance&apos;</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">      5</span> print<span class=\"ansiyellow\">(</span>rfFeatureImportance<span class=\"ansiyellow\">.</span>sort_values<span class=\"ansiyellow\">(</span>by<span class=\"ansiyellow\">=</span><span class=\"ansiyellow\">[</span><span class=\"ansiblue\">&apos;feature_importance&apos;</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">,</span>ascending<span class=\"ansiyellow\">=</span><span class=\"ansigreen\">False</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansigreen\">&lt;command-415717336807284&gt;</span> in <span class=\"ansicyan\">&lt;listcomp&gt;</span><span class=\"ansiblue\">(.0)</span>\n<span class=\"ansigreen\">      2</span> rf <span class=\"ansiyellow\">=</span> RandomForestClassifier<span class=\"ansiyellow\">(</span>labelCol<span class=\"ansiyellow\">=</span><span class=\"ansiblue\">&quot;label&quot;</span><span class=\"ansiyellow\">,</span> featuresCol<span class=\"ansiyellow\">=</span><span class=\"ansiblue\">&quot;features&quot;</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">      3</span> rfModel <span class=\"ansiyellow\">=</span> rf<span class=\"ansiyellow\">.</span>fit<span class=\"ansiyellow\">(</span>trainingData<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">----&gt; 4</span><span class=\"ansiyellow\"> </span>rfFeatureImportance <span class=\"ansiyellow\">=</span> pd<span class=\"ansiyellow\">.</span>DataFrame<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">[</span><span class=\"ansiyellow\">(</span>name<span class=\"ansiyellow\">,</span> rfModel<span class=\"ansiyellow\">.</span>bestModel<span class=\"ansiyellow\">.</span>featureImportances<span class=\"ansiyellow\">[</span>idx<span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">)</span> <span class=\"ansigreen\">for</span> idx<span class=\"ansiyellow\">,</span> name <span class=\"ansigreen\">in</span> attrs<span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">,</span>columns<span class=\"ansiyellow\">=</span><span class=\"ansiyellow\">[</span><span class=\"ansiblue\">&apos;feature_name&apos;</span><span class=\"ansiyellow\">,</span><span class=\"ansiblue\">&apos;feature_importance&apos;</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">      5</span> print<span class=\"ansiyellow\">(</span>rfFeatureImportance<span class=\"ansiyellow\">.</span>sort_values<span class=\"ansiyellow\">(</span>by<span class=\"ansiyellow\">=</span><span class=\"ansiyellow\">[</span><span class=\"ansiblue\">&apos;feature_importance&apos;</span><span class=\"ansiyellow\">]</span><span class=\"ansiyellow\">,</span>ascending<span class=\"ansiyellow\">=</span><span class=\"ansigreen\">False</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansired\">AttributeError</span>: &apos;RandomForestClassificationModel&apos; object has no attribute &apos;bestModel&apos;</div>"]}}],"execution_count":88}],"metadata":{"name":"Spark DF, SQL, ML Exercise","notebookId":383991067827965},"nbformat":4,"nbformat_minor":0}