Unveiling Hidden Patterns in the Cryptocurrency Market with Affinity Propagation and Python

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Affinity propagation is a powerful unsupervised clustering technique that can identify hidden patterns in large datasets. In the cryptocurrency world, where new coins are constantly emerging and prices can be highly volatile, affinity propagation can help investors simplify the chaos. By analyzing historical price data, affinity propagation groups coins into clusters based on their past … Read more

Using Random Search to Tune the Hyperparameters of a Random Decision Forest with Python

Perfecting your machine learning model’s hyperparameters can often feel like hunting for a proverbial needle in a haystack. But with the Random Search algorithm, this intricate process of hyperparameter tuning can be efficiently automated, saving you valuable time and effort. Hyperparameters are properties intrinsic to your model, like the number of estimators in an ensemble … Read more

Leveraging Distributed Computing for Weather Analytics with PySpark

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Apache Spark is a popular distributed computing framework for Big Data processing and analytics. In this tutorial, we will work hands-on with PySpark, Spark’s Python-specific interface. We built on the conceptual knowledge gained in a previous tutorial: Introduction to BigData Analytics with Apache Spark, in which we learned about the essential concepts behind Apache Spark … Read more

How to Measure the Performance of a Machine Learning Classifier with Python and Scikit-Learn?

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Have you ever received a spam email and wondered how your email provider was able to identify it as spam? Well, the answer is likely machine learning! One common type of machine learning problem is called classification. The goal is to predict the correct class labels for a given set of observations. For example, we … Read more

Cluster Analysis with k-Means in Python

Embark on a journey into the world of unsupervised machine learning with this beginner-friendly Python tutorial focusing on K-Means clustering, a powerful technique used to group similar data points into distinct clusters. This invaluable tool helps us make sense of complex datasets, finding hidden patterns and associations without the need for a predetermined target variable. … Read more

Requesting Crypto Prices from the Coinmarketcap API using Python

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You can do various things with cryptocurrency price data, such as creating forecasting models, illustrating historical prices, or performing chart analysis. But first, you need to get hold of the data. Nowadays, several APIs provide access to cryptocurrency price data. One of the most important and trusted sources is Coinmarketcap.com. The website offers multiple API … Read more

Predictive Policing: Preventing Crime in San Francisco using XGBoost and Python

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In this tutorial, we’ll be using machine learning to predict and map out crime in San Francisco. We’ll be working with a dataset from Kaggle that contains information on 39 different types of crimes, including everything from vehicle theft to drug offenses. Using Python and the powerful Scikit-Learn library, we’ll train a classification model using … Read more

Color-Coded Cryptocurrency Price Charts in Python

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Are you intrigued by the fascinating world of cryptocurrency and looking to visually decipher its price trends? Welcome aboard! In this comprehensive tutorial, we will explore creating color-coded line charts using Python and Matplotlib, a powerful tool for effective analysis of changes along a third dimension. The past few years have witnessed a meteoric rise … Read more

Streaming Tweets and Images via the Twitter API in Python

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Twitter is a rich source of data that can be used to understand current and future trends. Because tweets often include hashtags, they can be easily linked to specific contexts such as political discussions or financial instruments. This makes Twitter a valuable tool for collecting and analyzing data. In this article, we’ll demonstrate how to … Read more

Customer Churn Prediction – Understanding Models with Feature Permutation Importance using Python

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Customer retention is a prime objective for service companies, and understanding the patterns that lead to customer churn can be the key to maintaining long-lasting client relationships. Businesses incur significant costs when customers discontinue their services, hence it’s vital to identify potential churn risks and take preemptive actions to retain these customers. Machine Learning models … Read more

Tuning Model Hyperparameters with Grid Search at the Example of Training a Random Forest Classifier in Python

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Are you struggling to find the best hyperparameters for your machine learning model? With Python’s Scikit-learn library, you can use grid search to fine-tune your model and improve its performance. In this article, we’ll guide you through the process of hyperparameter tuning for a classification model, using a random decision forest that predicts the survival … Read more