Chapter 4: Supervised Learning Algorithms: Predict Annual Income

Activity 11: Training a Naïve Bayes Model for our Census Income Dataset

Before working on step 1, make sure that the data has been preprocessed, as follows:

import pandas as pd
data = pd.read_csv("datasets/census_income_dataset.csv")
data = data.drop(["fnlwgt","education","relationship","sex", "race"], axis=1)

After reading the dataset, the three variables considered irrelevant for the study are removed.

Next, the remaining qualitative variables are converted into their numerical form via the following code:

from sklearn.preprocessing import LabelEncoder
enc = LabelEncoder()
features_to_convert = ["workclass","marital-status","occupation","native-country","target"]
for i in features_to_convert:
  data[i] = enc.fit_transform(data[i].astype('str'))

Once this is complete...