Скачать или смотреть 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.

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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...