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Titanic tutorial we did at the Kaggle Berlin group together on 10th September

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titanic

Data Science project to solve the Titanic challenge on Kaggle. We find the survivors of the Titanic disaster.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands that perform parts of the processing pipeline
├── README.md          <- The top-level README
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
├── Dockerfile         <- Dockerfile, alternative approach to manage environment
│                         more interesting if using non-Unix
├── submissions        <- Directory to keep submissions
│ 
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   │── make_dataset.py <- creates quickly hacked data files
│   │   └── make_dataset_v2.py <- prepares features properly instead of Ticket and Cabin.
│   │
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions for submissions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│

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