Project Description ๐
โ๏ธ Built a xgboost model for the famous Income Census dataset to predict whether a person will have salary <=50K or >50K.
Deployed Project Link: https://predict-income-api.herokuapp.com/
Jupyter Notebook: (https://github.com/shiv0112/income-prediction-xgboost/blob/master/notebooks/income_prediction.ipynb)
Data name: Income Census
## Data Set Information:
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))
Prediction task is to determine whether a person makes over 50K a year.
## Attribute Information:
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.
=> fnlwgt: continuous.
=> 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.
=> education-num: continuous.
=> marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
=> 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.
=> relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
=> race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
=> sex: Female, Male.
=> capital-gain: continuous.
=> capital-loss: continuous.
=> hours-per-week: continuous.
=> 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.
I trained this model using XGBoost Classifier:
-->Classification using XGBoost Model
-->How we can use GridSearch CV to select best parameters for model
-->Hardcode Preprocessing pipline