Titanic Data Cleaning Project 🚢

A complete data cleaning and preprocessing project using the Titanic dataset from Kaggle.

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🔍 Project Goals

This project focuses on preparing the Titanic dataset for machine learning.
We aim to clean, transform, and engineer features using best practices.

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✅ Steps Completed

• Missing value handling (Age, Cabin, Embarked)
• Outlier detection and treatment
• Feature extraction (e.g. extracting titles from names)
• Encoding categorical variables (Sex, Embarked)
• Feature scaling (Standardization and Normalization)

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📊 Libraries Used

• pandas
• numpy
• matplotlib
• seaborn
• sklearn

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📁 Project Structure

titanic-data-cleaning/
├── data/
│   ├── train.csv
│   ├── test.csv
├── notebooks/
│   └── 01_data_cleaning.ipynb
├── README.md
└── requirements.txt

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📌 Notebook Preview

You can follow the full data cleaning process in the notebook:
01_data_cleaning.ipynb

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🧠 What You Will Learn

• Exploratory Data Analysis (EDA)
• Handling null and duplicate values
• Visualizing distributions and outliers
• Preparing a dataset for machine learning

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📦 Coming Next:

• Classification models on cleaned data
• Model evaluation and tuning
• Submission to Kaggle!

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👤 Author

• Emre İlhan

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📜 License

This project is licensed under the MIT License.