https://github.com/shubhammate013/ml-model_training

Exploratory Data Analysis (EDA) is an approach in data analysis that uses various techniques to maximize insight into a dataset, uncover underlying structure, analyze relationships between variables, detect outliers and anomalies, test underlying assumptions, and feature selection for training Machine Learning models.
https://github.com/shubhammate013/ml-model_training

exploratory-data-analysis machine-learning-algorithms model-selection python

Last synced: about 1 month ago
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Exploratory Data Analysis (EDA) is an approach in data analysis that uses various techniques to maximize insight into a dataset, uncover underlying structure, analyze relationships between variables, detect outliers and anomalies, test underlying assumptions, and feature selection for training Machine Learning models.

• Host: GitHub
• URL: https://github.com/shubhammate013/ml-model_training
• Owner: shubhammate013
• Created: 2024-06-26T18:15:35.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-12T12:56:59.000Z (almost 2 years ago)
• Last Synced: 2025-03-14T05:13:21.725Z (over 1 year ago)
• Topics: exploratory-data-analysis, machine-learning-algorithms, model-selection, python
• Language: Jupyter Notebook
• Homepage:
• Size: 8.92 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# ML-Model_Training