uberwach/titanic

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

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
│