Air BNB Price Prediction Case Study | Boosting Algorithms | Adaptive, Gradient, and XGBoost

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 - www.kaggle.com...
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[@dineshv1806]

May I know y the test r2 of adaboost is -ve?

[@prosmartanalytics]

Sure, it used to be a very popular question in IBM's interviews. So, a regression model overall tries to be significantly better than just the average value of the target column, but when it's even worse than the average value of the target column, the Rsq is negative; indicating this is not a good model at all.