Chapter 5: Mastering Structured Data

Activity 14: Training and Predicting the Income of a Person

Solution:

1. Import the libraries and load the income dataset using pandas. First, import pandas and then read the data using read_csv.

   import pandas as pd
   import xgboost as xgb
   import numpy as np
   from sklearn.metrics import accuracy_score
   data = pd.read_csv("../data/adult-data.csv", names=['age', 'workclass', 'education-num', 'occupation', 'capital-gain', 'capital-loss', 'hours-per-week', 'income'])

   The reason we are passing the names of the columns is because the data doesn't contain them. We do this to make our lives easy.

2. Use Label Encoder from sklearn to encode strings. First, import Label Encoder. Then, encode all string categorical columns one by one.

   from sklearn.preprocessing import LabelEncoder
   data['workclass'] = LabelEncoder().fit_transform(data['workclass&apos...