kwasganguly / xgboost-income-prediction- Goto Github PK

1. Introduction This assignment will help you to consolidate the concepts learnt in the session.

2. Problem Statement

In this assignment students need to predict whether a person makes over 50K per year or not from classic adult dataset using XGBoost. The description of the dataset is as follows:

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))

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. Following is the code to load required libraries and data: import numpy as np import pandas as pd

train_set = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning- databases/adult/adult.data', header = None)

test_set = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning- databases/adult/adult.test', skiprows = 1, header = None)

col_labels = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation','relationship', 'race', 'sex', capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'wage_class'] train_set.columns = col_labels test_set.columns = col_labels NOTE: The solution shared through Github should contain the source code used and the screenshot of the output.

3. Output

N/A