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Includes data cleaning, visualizations, correlations, and key business insights.\n# Titanic EDA\n\nExploratory Data Analysis (EDA) of the classic Titanic dataset. The goal is to clean the data, explore patterns, and present clear visualizations and business-style insights.\n\n## Setup\n\n```bash\n# (Recommended) create a virtual environment\npython -m venv .venv\nsource .venv/bin/activate  # Windows: .venv\\Scripts\\activate\n\n# install dependencies\npip install -r requirements.txt\n```\n\n## Data\n\nPlace the dataset as `data/titanic.csv`. You can download a compatible version from Kaggle (\"Titanic - Machine Learning from Disaster\") and save the combined train/test or just `train.csv` as `data/titanic.csv`.\n\nRequired columns (common in public versions): `Survived`, `Pclass`, `Sex`, `Age`, `SibSp`, `Parch`, `Fare`, `Embarked`.\n\n\u003e Tip: If your file is named `train.csv`, update the notebook path or simply rename it to `titanic.csv`.\n\n## Analysis\n\nOpen the notebook and run all cells:\n\n```bash\njupyter notebook notebooks/01_titanic_eda.ipynb\n```\n\nWhat you'll find inside:\n- Data loading \u0026 basic sanity checks\n- Cleaning: missing values, types, outliers (simple)\n- 6-8 visualizations (class distribution, survival by sex/class/embarked, age/fare distributions, correlations)\n- Compact takeaways and \"What I found\" section\n\n## Results\n\nExported figures (PNG) will be saved to the `results/` folder automatically when you run the notebook. You can attach the best 2-3 charts in your README for recruiters.\n\n## Next Steps\n\n- Add a simple baseline model (e.g., logistic regression) in a new notebook `02_baseline_model.ipynb`\n- Compare metrics and add feature importance\n- Create a short executive summary in the README (top 3-5 insights)\n## Business Insights\n\nSurvival differed significantly by gender and class – female passengers and those in higher classes had higher survival rates.\n\nAge and fare distributions are skewed; for modeling they may require transformation or winsorization.\n\nMissing values (e.g., Age, occasionally Embarked) require simple imputation to avoid bias.\n\nPassenger class (Pclass) partly reflects socio-economic status and is a strong predictor of survival.\n\nFor decision-makers: segmentation by gender, class, and embarkation port clearly highlights groups at higher risk.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnkamilla%2Ftitanic-eda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnkamilla%2Ftitanic-eda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnkamilla%2Ftitanic-eda/lists"}