Loy Adult

June 24, 2019
How much is this person income? Prediction with Two-Class Boosted Decision Tree
Predicting whether a person’s income exceeds $50,000 per year based on his demographics or census data <br><br> • Import census income dataset • Create a new Azure Machine Learning experiment • Train and evaluate a prediction model <br><br> Split up the dataset <br><br> • Training data This grouping is used for creating our new predictive model based on the inherent patterns found in the historical data via the ML algorithm we use for the solution. • Validation data This grouping is used for testing the new predictive model against known outcomes to determine accuracy and probabilities. <br><br> **Data Set Information:** <br><br> Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0)) <br><br> Prediction task is to determine whether a person makes over 50K a year. <br><br> **Attribute Information:** <br><br> Listing of attributes: >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. <br><br> fnlwgt: continuous. <br><br> education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. <br><br> education-num: continuous. <br><br> marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. <br><br> occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. <br><br> relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. <br><br> race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. <br><br> sex: Female, Male. <br><br> capital-gain: continuous. <br><br> capital-loss: continuous. <br><br> hours-per-week: continuous. <br><br> native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.<br><br> <br><br> ![enter image description here][1] <br><br> ![enter image description here][2] <br><br> [1]: https://raw.githubusercontent.com/laploy/ML.NET/master/Adult/adult-azureML-model.JPG [2]: https://raw.githubusercontent.com/laploy/ML.NET/master/Adult/adult-azureML-score.JPG