How to Use Hierarchical Clustering For Customer Segmentation in Python

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Have you ever found yourself wondering how you can better understand your customer base and target your marketing efforts more effectively? One solution is to use hierarchical clustering, a method of grouping customers into clusters based on their characteristics and behaviors. By dividing your customers into distinct groups, you can tailor your marketing campaigns and … Read more

Unlocking the Potential of Machine Learning in the Insurance Industry: Five Use Cases with High Business Value

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The insurance industry has long harnessed technology’s transformative power. From online policy applications to modernized claims processing systems, the tech revolution in insurance has been in motion for years. However, machine learning promises to be one of the most influential and disruptive advancements in the sector. Machine learning empowers insurers to analyze vast data volumes, … 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

Training a Sentiment Classifier with Naive Bayes and Logistic Regression in Python

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Are you ready to learn about the exciting world of social media sentiment analysis using Python? In this article, we’ll dive into how companies are leveraging machine learning to extract insights from Twitter comments, and how you can do the same. By comparing two popular classification models – Naive Bayes and Logistic Regression – we’ll … Read more