🧹 Data Cleaning and Preprocessing for Machine Learning & Deep Learning

This repository contains a well-documented Jupyter Notebook that walks through essential and commonly used techniques for cleaning and preprocessing data before feeding it into machine learning and deep learning models.

The notebook includes:

• Handling missing values
• Removing duplicates
• Encoding categorical variables (including Entity Embeddings)
• Normalization & Standardization
• Outlier detection (IQR, Z-score, Isolation Forest)
• Text preprocessing (Stemming, Lemmatization)
• Image cleaning via augmentations (Rotation, Affine & Perspective transforms)
• Feature scaling
• Data visualization for sanity checks

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📁 Contents

• data_cleaning_and_preprocessing.ipynb: Main notebook with code examples and explanations.
• images/: Folder (if any) with sample images for transformations.
• requirements.txt: List of packages needed to run the notebook (optional, if provided).

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/yourusername/data-cleaning-preprocessing-ml.git
cd data-cleaning-preprocessing-ml

2. Install the required packages

pip install -r requirements.txt

🤝 Contributing

We welcome contributions! Here's how you can help:

1. Fork the repository

2. Create a new branch (git checkout -b feature-name)

3. Make your changes — Improve examples, fix bugs, add new cleaning methods, etc.

4. Commit your changes (git commit -m 'Add new cleaning method')

5. Push to your fork (git push origin feature-name)

6. Submit a pull request

Contribution Guidelines: Keep explanations simple and beginner-friendly.

Follow PEP8 style guidelines for Python code.

Provide comments in the notebook for new sections.

If adding new data types (e.g., audio, time-series), include minimal sample data.