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Today I attended a very informative presentation organized by Sonia Singh, PMP® at the Technical Meetup Group hosted by IBM and presented by Mark Freeman on prescriptive analytics, showcasing how efficient it can be for improving business outcomes. In his presentation 'Applications of Machine Learning to Optimization', Mark shared some insights from real-world applications, like health treatment plans. I liked the described procedure for creating predictive models for different customer segments, and optimizing the models for each segment separately to plan for better price strategies. I appreciated the comprehensive list of all the resources provided to help me dive deeper into the matter. Thanks for the great presentation. A must-attend for those interested in the practical impact of prescriptive analytics (or optimization) on organizational decision-making. Catch the repeat session tomorrow! (https://lnkd.in/dtP9-y4R). #Optimization #PrescriptiveAnalytics

🚀 Unlock the power of AI-driven insights! Get the story on how Beiersdorf transformed their business operations with Azure Cognitive Search. 📈 Discover how they conquered the challenge of efficient data management and boosted productivity. How can IAYF Consulting, LLC help your company conquer your data management challenges?

Analytics & Observability tools today are slow, expensive, and don’t give you the insight you need to run your business. That’s why Conviva’s Operational Data Platform goes beyond basic business intelligence and static dashboards, enabling teams to solve real-time QoE issues with an AI-driven platform that spots problems in real time, sends alerts, and tells disparate teams exactly what’s going wrong, why it matters, and how to fix it. 

Have you utilized Netdata's Anomaly Rate feature yet? Built right into the menu, this tool calculates an anomaly score for each raw metric value, allowing you to quickly identify unusual activity in your infrastructure. It's our way of integrating Machine Learning into User Experience, making your monitoring tasks more intuitive and effective. Explore this feature and learn how it can augment your workflow in our blog post: https://loom.ly/zfZ6xAQ #Netdata #AnomalyDetection #MachineLearning

Have you utilized Netdata's Anomaly Rate feature yet? Built right into the menu, this tool calculates an anomaly score for each raw metric value, allowing you to quickly identify unusual activity in your infrastructure. It's our way of integrating Machine Learning into User Experience, making your monitoring tasks more intuitive and effective. Explore this feature and learn how it can augment your workflow in our blog post: https://loom.ly/zfZ6xAQ #Netdata #AnomalyDetection #MachineLearning

🚀 Unlock the power of AI-driven insights! Get the story on how Beiersdorf transformed their business operations with Azure Cognitive Search. 📈 Discover how they conquered the challenge of efficient data management and boosted productivity. How can Cutec help your company conquer your data management challenges?

🚀 Deep Dive into Machine Learning Model Performance: Logistic Regression vs. XGBoost🚀 In the realm of predictive analytics for fitness class attendance, I embarked on an exploration comparing two powerhouse models: Logistic Regression and XGBoost. My goal? To identify which model most accurately predicts class attendance. Here's what I found: Accuracy and Precision: - XGBoost outperformed with a higher accuracy rate, demonstrating its prowess in handling complex patterns within the data. - The precision of XGBoost, particularly in identifying true positives, was notably superior, making it the preferred model for predicting class attendance accurately. F1 Score and Balance: - Considering the F1 score, which balances precision and recall, **XGBoost** again led the pack. This score is crucial, especially given the unbalanced nature of class attendance data. - XGBoost’s ability to achieve high F1 scores across both classes (attending and not attending) signifies its effectiveness in dealing with imbalanced datasets. Log Loss – Confidence in Prediction: - Evaluating models on log loss provided insights into their confidence. XGBoost demonstrated a lower log loss, indicating not just higher accuracy but also greater certainty in its predictions. Why XGBoost Stands Out: - Efficiency and Efficacy: XGBoost's superior handling of complex data relationships and its regularization features help prevent overfitting, ensuring robust predictions. - **Speed and Scalability**: Its ability to manage large datasets efficiently, coupled with parallel processing capabilities, makes XGBoost a versatile tool for data scientists. Reflections: This comparative analysis not only highlighted XGBoost's strengths in predictive accuracy and model confidence but also underscored the importance of comprehensive evaluation metrics. By leveraging XGBoost, data scientists can navigate complex datasets with nuanced insights, leading to more informed decision-making processes. https://lnkd.in/dWX9ccBC

🚀 Unlock the power of AI-driven insights! Get the story on how Beiersdorf transformed their business operations with Azure Cognitive Search. 📈 Discover how they conquered the challenge of efficient data management and boosted productivity. How can NCI help your company conquer your data management challenges?