4. Supervised Learning Algorithms: Predicting Annual Income

Activity 4.01: Training a Naïve Bayes Model for Our Census Income Dataset

Solution:

1. In a Jupyter Notebook, import all the required elements to load and split the dataset, as well as to train a Naïve Bayes algorithm:

   import pandas as pd
   from sklearn.model_selection import train_test_split
   from sklearn.naive_bayes import GaussianNB

2. Load the pre-processed Census Income dataset. Next, separate the features from the target by creating two variables, X and Y:

   data = pd.read_csv("census_income_dataset_preprocessed.csv")
   X = data.drop("target", axis=1)
   Y = data["target"]

   Note that there are several ways to achieve the separation of X and Y. Use the one that you feel most comfortable with. However, take into account that X should contain the features of all instances, while Y should contain the class labels of all instances.

3. Divide the dataset into training, validation, and...