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# ---------- 4th ITERATIONS - ADD DATA LABELS AND TITLES ----------

# Figure size
fig = plt.figure(figsize = (10, 4.8))

# Bar plot
bar_fig = plt.bar(
    x = 'PaymentMethod',
    height = 'customerID',
    data = df_group_1,

audhiaprilliant

# ---------- 3rd ITERATION - REMOVE FRAMES AND RESET THE VERTICAL TICKS ----------

# Figure size
fig = plt.figure(figsize = (10, 4.8))

# Bar plot
bar_fig = plt.bar(
    x = 'PaymentMethod',
    height = 'customerID',
    data = df_group_1,

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# ---------- 2nd ITERATION - REORDER THE CATEGORIES AND DO COLORING ----------

# Reorder the PaymentMethod based on total customer
df_group_1.sort_values(by = 'customerID', ascending = False, inplace = True)

# Figure size
fig = plt.figure(figsize = (10, 4.8))

# Bar plot
bar_fig = plt.bar(

audhiaprilliant

# ---------- 1st ITERATION - CREATE A BASIC BAR PLOT ----------

# Figure size
fig = plt.figure(figsize = (10, 4.8))

# Bar plot
plt.bar(
    x = 'PaymentMethod',
    height = 'customerID',
    data = df_group_1,

audhiaprilliant

# ---------- IMPORT PACKAGES ----------

# Dataframe manipulation
import pandas as pd

# Matrices operation
import numpy as np

# Data viz with matplotlib
import matplotlib

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# Flask
from flask import Flask, render_template, request
# Data manipulation
import pandas as pd
# Matrices manipulation
import numpy as np
# Script logging
import logging
# ML model
import joblib

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# Machine learning model development

# XGBoost model
xgb_model = xgb.XGBClassifier(
    objective = 'binary:logistic',
    use_label_encoder = False
)

# Define parameters range
params = {