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This data is still available for access. In the previous step, we downloaded the data from Kaggle and then uploaded it to Spark as a Dataset. In this step, we will talk about how to download data from Kaggle Notebooks as well as how to upload Spark data into Kaggle Notebooks. Figure 1: Image of Kaggle Notebook Note: There are two ways to do this. For example, I do not include the full path. Thus, I would like to provide just a few notes in this case. Add the IP address to the saved password list Save and close the Kaggle website Figure 2: How to save the IP address as a password Save the Kaggle notebook Figure 3: How to add the IP address to the saved password list Practical Data Preprocessing and Modeling Let's start with the coding implementation. The first thing we should do is load the Jupyter kernel and run it with a local Python interpreter. Note that this procedure will need your system-wide administrative privileges. Figure 4: How to run the Jupyter kernel on your system We first download the mnist dataset from UCI for experimenting. Then we load the data into Spark and convert it into a Dataset as follows. The following is a complete code snippet: from __future__ import print_function from datetime import datetime import sys from typing import Optional, Tuple, List from pyspark.sql import SparkSession from pyspark.sql.functions import DataFrame, * from pyspark.sql.types import * from pyspark.sql import SparkSession from pyspark.sql import SQLContext import json # getting the notebook from the IP address notebook = notebook_url # specify the notebook, here this is Gail, there is an example notebook link




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