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

Build a High-Performing Movie Recommender System using Collaborative Filtering in Python

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The digital age presents us with an unmanageable number of decisions and even more options. Which series to watch today? What song to listen to next? Nowadays, the internet and its vast content offer too many choices. But there is hope – recommender systems are here to solve this problem and support our decision-making. They … Read more

Automate Crypto Trading with a Python-Powered Twitter Bot and Signals

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This tutorial develops a Twitter bot in Python that will generate automated trading signals. The bot will pull real-time price data on various cryptocurrencies (Bitcoin, Ethereum, Doge, etc.) from the crypto exchange and analyze it using predefined rules. Whenever the bot detects a relevant price change, it automatically posts a tweet via Twitter. Simple … 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

Forecasting Beer Sales with ARIMA in Python

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Time series analysis and forecasting is a tough nut to crack, but the ARIMA model has been cracking it for decades. ARIMA, short for “Auto-Regressive Integrated Moving Average,” is a powerful statistical modeling technique for time series analysis. It’s particularly effective when the time series you’re analyzing follows a clear pattern, like seasonal changes in … 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

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

Mastering Multivariate Stock Market Prediction with Python: A Guide to Effective Feature Engineering Techniques

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Are you interested in learning how multivariate forecasting models can enhance the accuracy of stock market predictions? Look no further! While traditional time series data provides valuable insights into historical trends, multivariate forecasting models utilize additional features to identify patterns and predict future price movements. This process, known as “feature engineering,” is a crucial step … Read more

Rolling Time Series Forecasting: Creating a Multi-Step Prediction for a Rising Sine Curve using Neural Networks in Python

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Many time forecasting problems can be solved by predicting just one step into the future. However, some problems require a forecast for an extended period of time, which calls for a multi-step time series forecasting approach. This approach involves modeling the distribution of future values of a signal over a prediction horizon. In this article, … Read more