02-BankMarketing.ipynb

Kernel: Python 3

Bank Marketing Lab

In this lab, our goal is to predict the purchase of a bank product marketed over the phone. This is represented in our y column in the data. (Here is the data dictionary ) In examining this binary classification problem, we want to explore how different models perform, specifically in terms of:

• Accuracy

• Precision

• Recall

import pandas as pd

bank = pd.read_csv('data/bank_marketing.csv')

bank.head()

bank.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4119 entries, 0 to 4118
Data columns (total 22 columns):
Unnamed: 0        4119 non-null int64
age               4119 non-null int64
job               4119 non-null object
marital           4119 non-null object
education         4119 non-null object
default           4119 non-null object
housing           4119 non-null object
loan              4119 non-null object
contact           4119 non-null object
month             4119 non-null object
day_of_week       4119 non-null object
duration          4119 non-null int64
campaign          4119 non-null int64
pdays             4119 non-null int64
previous          4119 non-null int64
poutcome          4119 non-null object
emp.var.rate      4119 non-null float64
cons.price.idx    4119 non-null float64
cons.conf.idx     4119 non-null float64
euribor3m         4119 non-null float64
nr.employed       4119 non-null float64
y                 4119 non-null int64
dtypes: float64(5), int64(7), object(10)
memory usage: 708.0+ KB

bank.y.value_counts()

0    3668
1     451
Name: y, dtype: int64

Exploration

Are there other characteristics that need to be handled within the data? How can we encode something like the missing values that are labeled 'unknown'?

Model Building

Examine the results of three classification models including a LogisticRegression, and a DummyClassifier. Produce and interpret the classification report for each of these models. Which would you care about more in this scenario, a model that has higher precision or recall?