Скачать или смотреть Complete ML or DataScience Project | Comparing ML Models | Decision Tree vs Random Forest vs XGBoost

Machine Learning Model Comparison on the Diamonds Dataset

In this project, I trained and compared three powerful ML algorithms —
🔹 Decision Tree
🔹 Random Forest
🔹 XGBoost

Each model was tested on the Diamonds dataset to evaluate performance using Accuracy, Precision, Recall, and F1 Score.
See how these algorithms perform side by side — and find out which one gives the best results!

📊 Project Highlights:

End-to-end data preprocessing and model training
Performance comparison of three classification models
Visual and metric-based evaluation

📂 Project Links:
🔗 GitHub Repository: https://github.com/khazarShahid/Diamo...

🔗 Kaggle Notebook: https://www.kaggle.com/code/khazarsha...

🎥 Watch till the end for the final comparison results!
💬 Don’t forget to like, comment, and subscribe for more ML projects and tutorials.

Tags:-
machine learning, machine learning projects, ml model comparison, xgboost vs random forest, decision tree classifier, diamonds dataset analysis, data science projects, python machine learning, kaggle projects, github machine learning, ai short video, xgboost tutorial, random forest tutorial, data science short, ml comparison, classification models, python data analysis

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GitHub:- https://github.com/khazarShahid

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