Serve ML model with Flask REST API - 1

fetch_data.py

url = "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data"
column_names = [
    "MPG",
    "Cylinders",
    "Displacement",
    "Horsepower",
    "Weight",
    "Acceleration",
    "Model Year",
    "Origin",
]

raw_dataset = pd.read_csv(
    url,
    names=column_names,
    na_values="?",
    comment="\t",
    sep=" ",
    skipinitialspace=True,
)

dataset = raw_dataset.copy()
dataset = dataset.dropna().astype(np.float32)
dataset["Origin"] = dataset["Origin"].map({1: "USA", 2: "Europe", 3: "Japan"})
dataset = pd.get_dummies(dataset, columns=["Origin"], prefix="", prefix_sep="")

train_dataset = dataset.sample(frac=0.8, random_state=0)
test_dataset = dataset.drop(train_dataset.index)

train_features = train_dataset.copy()
test_features = test_dataset.copy()

train_labels = train_features.pop("MPG")
test_labels = test_features.pop("MPG")