Скачать или смотреть Is Random Forest the SECRET to ACCURATELY Predicting Flight Delays

Random Forest

This project uses the Random Forest algorithm to predict flight delays based on factors like airline, departure time, route, and historical data. It demonstrates a real-world machine learning classification problem with data preprocessing, model training, and performance evaluation using Python.
Understand the Random Forest algorithm and how it works in machine learning (ML) and data science using decision trees
and ensemble learning. This explanation covers Random Forest regression, core statistics, and practical implementation in Python,
while highlighting its importance in AI and artificial intelligence applications.
Random Forest is a powerful ensemble learning algorithm in machine learning that builds multiple decision trees and combines their predictions to improve accuracy, stability, and generalization. Instead of relying on a single model, Random Forest uses the concept of bagging (Bootstrap Aggregating),
where multiple subsets of the training data are sampled and used to train independent trees. For classification, Random Forest predicts the class based on majority voting from all trees, while for regression, it returns the average prediction of individual trees. By introducing randomness in both data sampling and feature selection, the algorithm reduces overfitting, handles high-dimensional data, and performs well even with noisy or missing values.
Random Forest is widely used in data science, artificial intelligence, and real-world applications such as fraud detection, customer churn prediction, medical diagnosis, recommendation systems, and financial forecasting. It supports feature importance analysis, works well with non-linear relationships, and requires minimal preprocessing compared to many other ML algorithms.
Due to its robustness, scalability, and strong performance, Random Forest remains one of the most popular and reliable algorithms in machine learning using Python.
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Github Link: https://github.com/edumentordeepti/Fo...
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Chapter/ Timestamp:
00:00 - intro
00:22 - how to ovreview dataset features?
01:32 - how to understand head() of dataset?
02:00 - how to understand info() of dataset?
02:26 - how to undetrstand data.describe()?
02:50 - how to explore various features?
03:53 - how to preprocess categorical data to numerical?
04:19 - how to plot heatmap for features?
05:30 - how to understand sklearn libraries?
06:21 - how to get featue segregation?
07:41 - how to get train test split?
08:17 - how to use gridsearchcv methods on dataset?
14:09 - how to get best parameter?
14:30 - how to train model with best parameter?
14:52 - hoe to predict output with test data?
15:03 - how to understand evaluation metrices?
15:55 - how to understand feature importance?
17:22 - outro
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DISCLAIMER:
This video is created for educational purposes only. We do not own any copyrights, all code, resources shared are for learning only and all rights go to their respective owners. Please respect licenses and terms of use when implementing in your projects. The usage is non-commercial and we do not make any profit from it. The sole purpose of this vides is to " Learn & Grow... " together in the field of Artificial Intelligence and Machine Learning..