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

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

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

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

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