Processing Large Datasets with Dask and SkateLearn

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# Introduction

Disk is a set of packages that take advantage of the capabilities of parallel computing—extremely useful when handling large datasets or building efficient, data-driven applications such as advanced analytics and machine learning systems. Among its most prominent advantages is Disk’s seamless integration with existing Python frameworks, including support for processing large datasets as well as large datasets. Learn to skate modules through parallel workflows. This article explores how to use disk for scalable data processing, even under limited hardware constraints.

# Step-by-step walkthrough

It’s not particularly massive though California Housing Dataset is reasonably large, which makes it an excellent choice for a soft, reflective coding example that demonstrates how to jointly leverage disk and skate learning for data processing at scale.

Disk provides a dataframe Module that simulates many aspects of Pandas DataFrame objects to handle large datasets that may not fit completely in memory. We will use this disk DataFrame structure to load our data from a CSV into a GitHub repository, as follows:

import dask.dataframe as dd

url = "
df = dd.read_csv(url)

df.head()

An important note here. If you want to see the “shape” of the dataset – the number of rows and columns – it’s more difficult to use this method. df.shape. Instead, you should do something like:

num_rows = df.shape(0).compute()
num_cols = df.shape(1)
print(f"Number of rows: {num_rows}")
print(f"Number of columns: {num_cols}")

Output:

Number of rows: 20640
Number of columns: 10

Note that we used disk compute() Slowly calculating the number of rows, but not the number of columns. A dataset’s metadata allows us to immediately obtain the number of columns (attributes), whereas determining the number of rows in a dataset can (hypothetically) be larger than memory – and thus requires distributed computation: something like: something that is compute() Transparently handles it for us.

Data preprocessing Machine learning is often the last step to building a model or estimator. Before we get into that part, and since the main focus of this hands-on article is to show how disk can be used for data processing, let’s clean up and get it ready.

is a common step in data preparation Dealing with missing values. With Disk, the process is as smooth as if we were just using Pandas. For example, the code below removes rows for instances that have missing values ​​in any of their attributes.

df = df.dropna()

num_rows = df.shape(0).compute()
num_cols = df.shape(1)
print(f"Number of rows: {num_rows}")
print(f"Number of columns: {num_cols}")

The dataset is now reduced to over 200 instances, with a total of 20433 rows.

Next, we can measure some numerical properties in the dataset by adding scatter learning StandardScaler or any other suitable Scaling method:

from sklearn.preprocessing import StandardScaler

numeric_df = df.select_dtypes(include=("number"))
X_pd = numeric_df.drop("median_house_value", axis=1).compute()

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X_pd)

Importantly, note that for a series of dataset-related operations we perform in disk, such as dropping rows containing missing values, the target column is then dropped. "median_house_value"we should add compute() At the end of a sequence of chained operations. This is the reason Dataset changes to disk are performed slowly. once compute() The result of permutation of chains on the dataset is called pandas DataFrame (Disk depends on pandas, so you don’t need to explicitly import the pandas library in your code unless you’re calling special functions directly from pandas).

What if we want to? Train a machine learning model? Then we should extract the target variable "median_house_value" And apply the same principle to convert to a Pandas object:

y = df("median_house_value")
y_pd = y.compute()

From now on, train the regression model by dividing the dataset into training and test sets. RandomForestRegressorand estimate its error on test data in an orchestrated manner that fully resembles traditional approaches using pandas and skate learning. Since tree-based models are insensitive to feature scaling, you can use unexpected features (eitherX_pd) or scaled (X_scaled) below we proceed with the above enumerated features:

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import numpy as np

# Use the scaled feature matrix produced earlier
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_pd, test_size=0.2, random_state=42)

model = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
print(f"RMSE: {rmse:.2f}")

Output:

# wrap up

Learn to Dask and Skate In order to efficiently represent large datasets to build machine learning models, scalable, parallel data processing workflows can be used together. This article demonstrates how to load, clean, prepare, and transform data using DASC, then apply standard SkateLearn tools to machine learning modeling—all while optimizing memory usage and speeding up the pipeline when dealing with large-scale datasets.

Ivan Palomares Carrascosa Is a leader, author, speaker, and consultant in AI, Machine Learning, Deep Learning, and LLMS. He trains and guides others in real-world applications of AI.