Machine Learning for Email Marketing Campaigns
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Updated
Jul 30, 2019 - Jupyter Notebook
Machine Learning for Email Marketing Campaigns
Celestial Type Classification hosted by Dacon
A collection of papers produced on the theory of boosting as applied to binary classification. Further extensions to the multi-class classification problem and necessary and sufficient conditions to ensure boostability i.e. weak learning conditions. Finally, an overview over boosting algorithms and models employed in industry.
Write a code to implement AdaBoost algorithm using decision stump to learn strong classifier
Forward stagewise sparse regression estimation implemented for gretl.
HackerEarth Machine Learning Problem solved with ❤️ by Nakshatra Singh.
This project explores the working of various Boosting algorithms and analyzes the results across different algorithms. Algorithms Used are: Random Forest, Ada Boost, Gradient Boost and XG Boost
With the rise in the variety of cars with differentiated capabilities and features such as model, production year, category, brand, fuel type, engine volume, mileage, cylinders, colour, airbags and many more, we are bringing a car price prediction challenge for all. We all aspire to own a car within budget with the best features available. To so…
Identified the drivers of the risk of coronary heart disease and cardiovascular disease using the Sleep Heart Health Study dataset
Cost-sensitive loss in the component-wise boosting framework.
Implementation of BAdaCost - multi-class cost-sensitive boosting algorithm
Machine Learning Concepts and Algorithms.
A CLI Tuner of NGBoost
Repository containing introduction to scikit-learn to provide hands-on problem solving experience for all the methods and models learnt in MLT.
Prediction of Employee Burnout Rates at Work
This repository contains various classification, clustering and data analysis code.
House price prediction using ensemble methods including AdaBoost, Gradient Boost and XGBoost.
Add a description, image, and links to the boosting-algorithms topic page so that developers can more easily learn about it.
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