This repository demonstrates end-to-end data preprocessing techniques using Python, including handling missing values, feature encoding, scaling, and outlier detection.

• Missing value analysis (missingno, seaborn)
• Filling missing values (mean, median, mode)
• Data leakage prevention using SimpleImputer
• Preprocessing with Pipeline and ColumnTransformer
• Feature scaling and one-hot encoding
• Outlier detection using:
  • Boxplot & Scatterplot
  • IQR method
  • Z-score
  • Isolation Forest

• pandas, numpy
• seaborn, matplotlib, missingno
• scikit-learn
• scipy

• preprocessing.ipynb – Jupyter Notebook containing the full workflow

Useful for beginners and practitioners learning data cleaning, preprocessing pipelines, and outlier handling in machine learning.

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