Analysis of the Survey Results for FixMyCity Author: Tümer Tosik
├── LICENSE
├── 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 <- 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.
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to generate and load data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ summary
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ ├── visualization <- Scripts to create visualizations
│ │ └── visualize.py
│ │
│ └── exploration <- Scripts to explore dataset
└── README.md <- The top-level README for developers using this project.
- Setup environment
conda create --name fixmycity python=3.8
conda activate fixmycity
- Install requirements:
python -m pip install -U pip setuptools wheel
python -m pip install -r requirements.txt
- Move raw datafiles into data/raw and run:
python src/data/make_dataset.py
- Train respective models:
python src/models/train_model.py --experiment="<experiment name>"
You can choose between ["MS", "CP", "SE", "all"]
Notes:
- Tr_li-Breite, Tr_re-Breite, RVA-Breite were replaced by their respective meter values
Project based on the cookiecutter data science project template. #cookiecutterdatascience