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eda-analysis

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We leverage machine learning and data analysis to address real-world challenges in the copper industry. Our documentation encompasses data preprocessing, feature engineering, classification, regression, and model selection. Explore how we've enhanced predictive capabilities to optimize manufacturing solutions.

  • Updated Jun 11, 2024
  • Python

Culled from the UCI Machine Learning Repository, the Dry Bean Dataset (licensed under CC BY 4.0) provides valuable insights into bean classification and is a valuable resource for machine learning enthusiasts.

  • Updated Jul 2, 2024
  • Jupyter Notebook

We use machine learning and data analysis to predict resale prices of Singapore flats. Our documentation covers data preprocessing, feature engineering, regression, and model selection. Discover how we improved predictions to optimize solutions.

  • Updated Jun 19, 2024
  • Python

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