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https://github.com/ronvoluted/kaggle-nba
Team repository for the NBA Career Prediction Kaggle Competition from UTS Advanced Data Science for Innovation
https://github.com/ronvoluted/kaggle-nba
kaggle machine-learning python
Last synced: 12 days ago
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Team repository for the NBA Career Prediction Kaggle Competition from UTS Advanced Data Science for Innovation
- Host: GitHub
- URL: https://github.com/ronvoluted/kaggle-nba
- Owner: ronvoluted
- License: mit
- Created: 2021-02-02T16:49:03.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2021-03-03T01:57:03.000Z (over 3 years ago)
- Last Synced: 2023-03-09T08:21:04.969Z (over 1 year ago)
- Topics: kaggle, machine-learning, python
- Language: Jupyter Notebook
- Homepage: https://kaggle.com/c/uts-advdsi-nba-career-prediction
- Size: 10.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Kaggle NBA - Group 6 | All-Star
Team repository for the [NBA Career Prediction Kaggle Competition](https://www.kaggle.com/c/uts-advdsi-nba-career-prediction/overview) from UTS Advanced Data Science for Innovation.
- Kai-Ping Wang
- Sampath Pitchandi
- Ron Au
## Option A) Run with pipenv
### Install dependencies
```bash
pipenv install
```
### Run Jupyter Lab
```bash
pipenv run jupyter lab
```
## Option B) Run with Docker
### Build image:
```bash
docker build -t kaggle-nba .
```
### Run image container
#### Windows (cmd):
```cmd
docker run -itp 8888:8888 -v %cd%:/home/kaggle --name all-star kaggle-nba
```
#### Windows (Powershell):
```cmd
docker run -itp 8888:8888 -v ${PWD}:/home/kaggle --name all-star kaggle-nba
```
#### Mac/Linux:
```bash
docker run -itp 8888:8888 -v "$PWD":/home/kaggle --name all-star kaggle-nba
```
## Project Organization
├── Dockerfile <- Document containing build instructions for Docker image
├── LICENSE <- MIT License
├── Makefile <- Makefile with commands like `make data` or `make train`
├── Pipfile <- The requirements file for managing dependency installations
├── Pipfile.lock <- Locks package versions for dependency installations
├── 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:
│ _-week_ e.g.
│ wang_kai-ping-week1_1.0-train-data-exploration.ipynb
│
├── 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
│
├── 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
---
Project based on the [cookiecutter data science project template](https://drivendata.github.io/cookiecutter-data-science)
#cookiecutterdatascience