Using Pandas DataReader to Access Online Data Sources in Python

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Pandas DataReader is a library that allows data scientists to easily read data from a variety of sources into a Pandas DataFrame. This is especially useful for accessing data that resides outside of their local development environment and needs to be accessed via APIs. The Pandas DataReader provides functions for loading data from various online … Read more

Feature Engineering and Selection for Regression Models with Python and Scikit-learn

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Training a machine learning model is like baking a cake: the quality of the end result depends on the ingredients you put in. If your input data is poor, your predictions will be too. But with the right ingredients – in this case, carefully selected input features – you can create a model that’s both … Read more

Create a Personalized Movie Recommendation Engine using Content-based Filtering in Python

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Content-based recommender systems are a popular type of machine learning algorithm that recommends relevant articles based on what a user has previously consumed or liked. This approach aims to identify items with certain keywords, understand what the customer likes, and then identify other items that are similar to items the user has previously consumed or … Read more

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

Getting Started with Big Data Analytics – Apache Spark Concepts and Architecture

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Apache Spark is an absolute powerhouse when it comes to open-source Big Data processing and analytics. It’s used all over the place for everything from data processing to machine learning to real-time stream processing. Thanks to its distributed architecture, it can parallelize workloads like nobody’s business, making it a lean, mean data processing machine when … 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

Stock Market Forecasting Neural Networks for Multi-Output Regression in Python

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Multi-output time series regression can forecast several steps of a time series at once. The number of neurons in the final output layer determines how many steps the model can predict. Models with one output return single-step forecasts. Models with various outputs can return entire series of time steps and thus deliver a more detailed … 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

Multivariate Anomaly Detection on Time-Series Data in Python: Using Isolation Forests to Detect Credit Card Fraud

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Credit card fraud has become one of the most common use cases for anomaly detection systems. The number of fraud attempts has risen sharply, resulting in billions of dollars in losses. Early detection of fraud attempts with machine learning is therefore becoming increasingly important. In this article, we take on the fight against international credit … Read more