<jupyter_start><jupyter_text>Data Project ObjectivesThe main objective for this project notebook is to explore any meaningful findings from the bank dataset. Let's write some powerful functions that helps explore the bank dataset!!<jupyter_code># Import dependencies for the project
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
# Read the bank.csv file into Pandas DataFrame
path = './data/bank.csv'
df = pd.read_csv(path, header = None,
                names=['age', 'job', 'marital', 'education', 'default', 'balance', 
                      'housing', 'loan', 'contact', 'day', 'month', 'duration',
                      'campaign', 'pdays', 'previous', 'poutcome', 'y'])
df.head()
df.info()
# Add a visualization to the notebook
fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(6, 8))

ax[0].boxplot(df["age"])
ax[0].set_title('Distribution of Customer Age')
ax[0].get_xaxis().set_visible(False)
ax[0].set_ylabel('Age')

ax[1].hist(df[(df['balance'] < 10000)]['balance'], bins=100)
ax[1].set_title('Distribution of Card Balance')
ax[1].set_xlabel('Card Balance')
ax[1].set_ylabel('Frequency')
# Summary for the df
df.describe()<jupyter_output><empty_output>
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