End to end machine learning model deployment using flask

machine_learning_deployment_1.py

# Data frame manipulation
import pandas as pd

# Matrices operation
import numpy as np

# Data visualization with plotnine
from plotnine import *
import plotnine

# Data visualization with matplotlib
import matplotlib.pyplot as plt

# Data partitioning and cross validation
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold

# Grid-search
from sklearn.model_selection import GridSearchCV

# Evaluation metrics
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from sklearn.metrics import make_scorer

# XGBoost ML model
import xgboost as xgb

# Lightweight pipelining in Python
import joblib

# Ignore warnings
import warnings
warnings.filterwarnings('ignore', category = FutureWarning)