Welcome to our deep dive into the world of boosting algorithms! In this video, we’ll take you through a hands-on case study using the Airbnb New York City dataset. 🌆
This tutorial is packed with valuable insights. Here’s what we’ll cover:
🔥 Data Preparation:
📊 Handling missing values
🛠️ Detecting and treating outliers
🧠 Selecting the best features to ensure our data is model-ready
✨ Boosting Algorithms Overview:
AdaBoost (Adaptive Boosting): 🌟 An ensemble method that focuses on correcting the errors of previous models, boosting the performance of weak learners.
Gradient Boosting: 📈 An advanced technique that builds models sequentially, each new model correcting the errors of its predecessor.
Extreme Gradient Boosting (XGBoost): 🚀 A highly efficient and scalable implementation of gradient boosting that’s widely used in machine learning competitions for its speed and accuracy.
🛠️ Hyperparameter Tuning with Randomized Search CV:
Randomized Search CV is a powerful tool that helps us find the best hyperparameters for our models by randomly sampling a wide range of possible parameters. 🎯 It’s faster than a grid search and can often find better results!
📊 Model Comparison:
After fine-tuning, we’ll compare the performance of AdaBoost, Gradient Boosting, and XGBoost to see which one reigns supreme on our dataset. 🏆
Dataset Link - https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data
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