Air BNB Price Prediction Case Study | Boosting Algorithms | Adaptive, Gradient, and XGBoost скачать в хорошем качестве

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 - https://www.kaggle.com/datasets/dgomo... 👉 Don’t forget to like 👍, comment 💬, and subscribe 🔔 for more data science and machine learning tutorials!