End to end machine learning model deployment using flask

machine_learning_deployment_5.py

# Number of customers by loan status

# Data aggregation between default and not default customers
df_viz_1 = df_train.groupby(['Loan_Status'])['Loan_ID'].count().reset_index(name = 'Total')
# Map the loan status
df_viz_1['Loan_Status'] = df_viz_1['Loan_Status'].map(
    {
        0: 'Not default',
        1: 'Default'
    }
)

# Figure size
plt.figure(figsize = (6.4,4.8))
# Customize colors and other settings
colors = ['#80797c','#981220']
# Explode 1st slice
explode = (0.1, 0)
# Create a pie chart
plt.pie(
    x = 'Total',
    labels = 'Loan_Status',
    data = df_viz_1,
    explode = explode,
    colors = colors,
    autopct = '%1.1f%%',
    shadow = False,
    startangle = 140
)
# Title and axis
plt.title('Number of customers by loan status', fontsize = 18)
plt.axis('equal')
plt.show()