6. Building Your Own Program

Activity 6.01: Performing the Preparation and Creation Stages for the Bank Marketing Dataset

Solution:

Note

To ensure the reproducibility of the results available at https://packt.live/2RpIhn9, make sure that you use a random_state of 0 when splitting the datasets and a random_state of 2 when training the models.

1. Open a Jupyter Notebook and import all the required elements:

   import pandas as pd
   from sklearn.preprocessing import LabelEncoder
   from sklearn.model_selection import train_test_split
   from sklearn.tree import DecisionTreeClassifier
   from sklearn.neural_network import MLPClassifier
   from sklearn.metrics import precision_score

2. Load the dataset into the notebook. Make sure that you load the one that was edited previously, named bank-full-dataset.csv, which is also available at https://packt.live/2wnJyny:

   data = pd.read_csv("bank-full-dataset.csv")
   data.head(10)

   The output is as follows:

   Figure 6.8: A screenshot showing...