While building batch learning as mentioned above it has to be trained with available data to do so here is the below code

import pandas as pd
load_data = pd.read_csv("xyz.csv")

As we have to work on available data so no such automation is required and it is a one time process, however, if you want to train the model progressively for example in the stock price sector you need to update the data in each instance that is a type of online learning as mentioned above:

To do so we can create a python function:

import os
import tarfile
import urllib
DOWNLOAD_ROOT = "https://yourrepo.....//..../"
XYZ_PATH = os.path.join("datasets", "xyz")
XYZ_URL = DOWNLOAD_ROOT + "datasets/xyz/xyz.tgz"
def fetch_housing_data(xyz_url=XYZ_URL, xyz_path=XYZ_PATH):
   os.makedirs(xyz_path, exist_ok=True)
   tgz_path = os.path.join(xyz_path, "xyz.tgz")
   urllib.request.urlretrieve(xyz_url, tgz_path)
   xyz_tgz = tarfile.open(tgz_path)
   xyz_tgz.extractall(path=xyz_path)
   xyz_tgz.close()

It will create a directory in your workspace, now let’s create a function to load data using pandas:

import pandas as pd

# this function will return the data
def load_data(xyz_path = XYZ_PATH):
   csv_path = os.path.join(xyz_path, "xyz.csv")
  
   return pd.read_csv(csv_path)