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20 data prep techniques-Credit Scoring or Loan Approval Case Study
A comprehensive tutorial on data preprocessing techniques covering everything from dimensionality reduction to text processing, with practical Python code examples using scikit-learn and related libra
ries. The guide includes detailed explanations and implementations of 20 essential preprocessing methods for preparing data for machine learning models.

Reasons to Read -- Learn:

  • how to implement 20 essential data preprocessing techniques with practical Python code examples using popular libraries like scikit-learn, making it invaluable for hands-on machine learning projects.
  • how to handle common data challenges such as missing values, outliers, and imbalanced datasets through detailed explanations and step-by-step implementations of solutions like SMOTE, IQR method, and MICE.
  • advanced feature engineering techniques that can improve model performance, including polynomial features, interaction terms, and specialized text processing methods with concrete examples showing input and output transformations.
  • publisher: @anixlynch
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