DS-vacancies-analysis

数据科学空缺:分析和推荐

指令

1. 克隆回购。
2. 运行使dirs来创建下面描述的目录结构中缺失的部分。
3. 可选:运行使virtualenv创建一个python虚拟环境。如果使用conda或其他环境管理器，则跳过。
   1. 运行源env / bin /激活命令，激活virtualenv。
4. 运行使需求安装所需的python包。
5. 把原始数据放进去数据/生．
6. 要将原始数据保存到DVC缓存中，运行提交raw_data.dvc
7. 按照您的意愿编辑代码文件。
8. 处理数据，训练和评估你的模型使用DVC repro . DVC或使繁殖
9. 当你对结果满意时，将文件(包括。dvc文件)提交到git。

项目组织

├──LICENSE├──Makefile <-用“make dirs”或“make clean”这样的命令制作├──README. sound效果器md <-使用此项目的开发人员的顶级README。├──data│├──经整理的<-最终的、规范的建模数据集。│├──raw <-原始的、不可变的数据转储。│├──eval。"<- The end of the data pipeline - evaluates the trained model on the test dataset. │ ├── 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`. │ ├── process_data.dvc <- Process the raw data and prepare it for training. ├── raw_data.dvc <- Keeps the raw data versioned. │ ├── 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 │ └── metrics.txt <- Relevant metrics after evaluating the model. │ └── training_metrics.txt <- Relevant metrics from training the model. │ ├── 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 download or generate data │ │ └── make_dataset.py │ │ │ ├── models <- Scripts to train models and then use trained models to make │ │ │ predictions │ │ ├── predict_model.py │ │ └── train_model.py │ │ │ └── visualization <- Scripts to create exploratory and results oriented visualizations │ └── visualize.py │ ├── tox.ini <- tox file with settings for running tox; see tox.testrun.org └── train.dvc <- Traing a model on the processed data.

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项目基于Cookiecutter数据科学项目模板．# cookiecutterdatascience