基于区块链的毕业设计Ethereum Data Analisys – 以太坊数据分析

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Ethereum Data Analisys

— Project Status: [Active]

Project Intro/Objective

O objetivo desse projeto é analisar informações sobre a rede Ethereum.

Collaborators

Name Github Page
Júlia Valadares juliaavaladares

Project Organization

├── LICENSE ├── Makefile           <- Makefile with commands like `make data` or `make train` ├── README.md          <- The top-level README for developers using this project. ├── 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. │ ├── docs               <- A default Sphinx project; see sphinx-doc.org for details │ ├── 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` │ ├── 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 │   │ │   ├── 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 │   │   │                 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.readthedocs.io 

Methods Used

  • Aprendizado de Máquina
  • Modelos de predição

Technologies

  • Python
  • Pandas, Jupyter

Getting Started

  1. Clone o repositório (for help see this tutorial).
  2. Raw Data is being kept [here](Repo folder containing raw data) within this repo.
  3. Data processing/transformation scripts are being kept [here](Repo folder containing data processing scripts/notebooks)
  4. etc…
  5. Follow setup [instructions](Link to file)

Featured Notebooks/Analysis/Deliverables

  • Notebook/Markdown/Slide Deck Title
  • Notebook/Markdown/Slide DeckTitle
  • Blog Post


项目简介/目标

合作者

使用的方法

O以太坊eth信息分析项目的目标

技术

Name Github Page
Júlia Valadares juliaavaladares

特色笔记本/分析/可交付成果,Jupyter
  • 克隆o repositoório(有关帮助信息,请参阅本教程)
  • 原始数据保存在此回购中[此处](包含原始数据的回购文件夹)
  • 正在保存数据处理/转换脚本[此处](包含数据处理脚本/笔记本的Repo文件夹)
  • 按照设置[说明](链接到文件)
  • 笔记本/标记/幻灯片组标题
  • 笔记本/标记/幻灯片组标题
  • 博客帖子 Júlia Valadares

    Juliavaladares

    ├── LICENSE ├── Makefile           <- Makefile with commands like `make data` or `make train` ├── README.md          <- The top-level README for developers using this project. ├── 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. │ ├── docs               <- A default Sphinx project; see sphinx-doc.org for details │ ├── 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` │ ├── 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 │   │ │   ├── 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 │   │   │                 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.readthedocs.io 

    Methods Used

    • 克隆o repositoório(有关帮助信息,请参阅本教程)
    • 原始数据保存在此回购中[此处](包含原始数据的回购文件夹)

    Technologies

    • 正在保存数据处理/转换脚本[此处](包含数据处理脚本/笔记本的Repo文件夹)

    Getting Started

    1. 按照设置[说明](链接到文件)
    2. 笔记本/标记/幻灯片组标题
    3. 笔记本/标记/幻灯片组标题
    4. etc…
    5. Follow setup [instructions](Link to file)

    Featured Notebooks/Analysis/Deliverables

    • Notebook/Markdown/Slide Deck Title
    • Notebook/Markdown/Slide DeckTitle
    • Blog Post

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