Build a Full Python Machine Learning Project With XGBoost: Airline Flight Delay Prediction скачать в хорошем качестве

Build a Full Python Machine Learning Project With XGBoost: Airline Flight Delay Prediction

This tutorial shows you how to build an end-to-end machine learning project using Python and the XGBoost algorithm. It covers the main steps, including wrangling and feature engineering, train/test split data preparation, exploratory data exploration (EDA), model training of XGBoost, model evaluation, and hyperparameter tuning. Whether you're a beginner or looking to enhance your data science and machine learning skills, this video covers essential skills and considerations that are needed to create robust prediction models. Like, Comment, and Subscribe to the Deep Charts channel for more Machine Learning and Quantitative Finance Tutorials! Code: https://github.com/deepcharts/project... Chapters 00:00 Introduction 00:10 Steps of End-To-End Machine learning analysis 00:32 Importing Python Libraries 01:17 Importing Project Dataset 02:04 Data Pre-processing and Preliminary Feature Engineering 04:26 Preparing Data for Analysis (e.g. Train/Test Split) 05:00 Exploratory Data Analysis (EDA) 06:26 Identifying and Handling Class Imbalances 08:25 XGBoost Model Training and Evaluation 10:34 Model Improvement: Hyperparameter Tuning *Resources* Kaggle Dataset: https://www.kaggle.com/datasets/under...