https://github.com/priyanshul28/ml_regression_eda_waiterstip

An EDA and Machine Learning Regression exercise on the Waiter's Tip dataset demonstrating the use of Linear Regression, Neural Network Regressors, Decision Trees, Random Forests, Linear SVR, XGBoost, etc. The models are optimized using hyperparameter tuning through GridSearchCV.
https://github.com/priyanshul28/ml_regression_eda_waiterstip

eda machine-learning regression scikit-learn seaborn

Last synced: 13 days ago
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An EDA and Machine Learning Regression exercise on the Waiter's Tip dataset demonstrating the use of Linear Regression, Neural Network Regressors, Decision Trees, Random Forests, Linear SVR, XGBoost, etc. The models are optimized using hyperparameter tuning through GridSearchCV.

• Host: GitHub
• URL: https://github.com/priyanshul28/ml_regression_eda_waiterstip
• Owner: PriyanshuL28
• Created: 2025-01-18T04:35:31.000Z (20 days ago)
• Default Branch: main
• Last Pushed: 2025-01-18T05:02:19.000Z (20 days ago)
• Last Synced: 2025-01-25T14:13:31.869Z (13 days ago)
• Topics: eda, machine-learning, regression, scikit-learn, seaborn
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/jsphyg/tipping
• Size: 1.94 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# ML_Regression_WaitersTip

An EDA and Machine Learning Regression exercise on the Waiter's Tip dataset demonstrating the use of Linear Regression, Neural Network Regressors, Decision Trees, Random Forests, Linear SVR, XGBoost, etc. The models are optimized using hyperparameter tuning through GridSearchCV.