End to end machine learning model deployment using flask

machine_learning_deployment_8.py

# The distribution of applicant incomes by loan status

# Slice the columns
df_viz_4 = df_train[['ApplicantIncome', 'Loan_Status']].reset_index(drop = True)
# Map the loan status
df_viz_4['Loan_Status'] = df_viz_4['Loan_Status'].map(
    {
        0: 'Not default',
        1: 'Default'
    }
)

# Data viz
plotnine.options.figure_size = (8, 4.8)
(
    ggplot(
        data = df_viz_4
    )+
    geom_density(
        aes(
            x = 'ApplicantIncome',
            fill = 'Loan_Status'
        ),
        color = 'white',
        alpha = 0.85
    )+
    labs(
        title = 'The distribution of applicant incomes by loan status'
    )+
    scale_fill_manual(
        name = 'Loan Status',
        values = ['#981220','#80797c'],
        labels = ['Default', 'Not Default']
    )+
    xlab(
        'Applicant income'
    )+
    ylab(
        'Density'
    )+
    theme_minimal()
)