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https://github.com/shortthirdman/datascience-jupyter-notebooks

Data Science Jupyter Notebooks
https://github.com/shortthirdman/datascience-jupyter-notebooks

data-science data-visualization jupyterlab-notebooks jyputer-notebook notebook pycryptobot

Last synced: 1 day ago
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Data Science Jupyter Notebooks

Host: GitHub
URL: https://github.com/shortthirdman/datascience-jupyter-notebooks
Owner: shortthirdman
License: mit
Created: 2024-01-27T08:02:28.000Z (10 months ago)
Default Branch: main
Last Pushed: 2024-11-04T12:48:19.000Z (16 days ago)
Last Synced: 2024-11-04T13:39:16.386Z (16 days ago)
Topics: data-science, data-visualization, jupyterlab-notebooks, jyputer-notebook, notebook, pycryptobot
Language: Jupyter Notebook
Homepage:
Size: 5.13 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # Big Data MLOps Platform

Data Science and Machine Learning Jupyter Notebooks

[![Made withJupyter](https://img.shields.io/badge/Made%20with-Jupyter-orange?style=for-the-badge&logo=Jupyter)](https://jupyter.org/try)	[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Naereen/badges)	[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/shortthirdman/DataScience-Jupyter-Notebooks/main)

![GitHub commit activity](https://img.shields.io/github/commit-activity/m/shortthirdman/DataScience-Jupyter-Notebooks)	![GitHub commit activity](https://img.shields.io/github/commit-activity/t/shortthirdman/DataScience-Jupyter-Notebooks)	![GitHub Created At](https://img.shields.io/github/created-at/shortthirdman/DataScience-Jupyter-Notebooks)	![GitHub last commit](https://img.shields.io/github/last-commit/shortthirdman/DataScience-Jupyter-Notebooks)	![GitHub repo size](https://img.shields.io/github/repo-size/shortthirdman/DataScience-Jupyter-Notebooks)	[![Docker Image CI](https://github.com/shortthirdman/DataScience-Jupyter-Notebooks/actions/workflows/docker.yaml/badge.svg?branch=main&event=workflow_run)](https://github.com/shortthirdman/DataScience-Jupyter-Notebooks/actions/workflows/docker.yaml)

## Tech Stack

shortthirdman/DataScience-Jupyter-Notebooks is built on the following main stack:

-  [Python](https://www.python.org) – Languages

-  [Docker](https://www.docker.com/) – Virtual Machine Platforms & Containers

-  [GitHub Actions](https://github.com/features/actions) – Continuous Integration

-  [Jupyter](http://jupyter.org) – Data Science Notebooks

Full tech stack [here](/techstack.md)

## Setup References

- [Ready-to-run Docker images containing Jupyter applications - jupyter/docker-stacks](https://github.com/jupyter/docker-stacks)

- [Kaggle/docker-python - Kaggle Python Docker image](https://github.com/Kaggle/docker-python)

## Dataset References

- [Apple Stock Price](https://www.kaggle.com/datasets/rafsunahmad/apple-stock-price)

- [Spotify Dataset 2023](https://www.kaggle.com/datasets/tonygordonjr/spotify-dataset-2023)

- [Online Retail Transactions](https://www.kaggle.com/datasets/thedevastator/online-retail-transaction-data)

- [Stock Market](https://www.kaggle.com/datasets/jacksoncrow/stock-market-dataset)

- [American Airlines Group Stock](https://www.kaggle.com/datasets/varpit94/american-airlines-group-stock-data)

- [Amazon (AMZN) Historical Stock Price](https://www.kaggle.com/datasets/specter7/amazon-amzn-historical-stock-price-data)

## Docker commands

```shell

 docker system prune --all --volumes --force

```

```shell

 docker build --no-cache -f Dockerfile --progress=auto --compress --rm -t shortthirdman-org/bigdata-mlops-platform:latest .

```

```shell

docker buildx build --progress=auto --compress --rm -t shortthirdman-org/bigdata-mlops-platform:latest .

```

```shell

 docker run -d -n mlops -p 8888:8888 --restart unless-stopped shortthirdman-org/bigdata-mlops-platform:latest

```

## Local Setup

  - Create a Python virtual environment and activate

	

	```shell

	python -m venv dev

	```

	

	```shell

	.\dev\Scripts\activate

	```

  - Install the packages and dependencies as listed in requirements file

	

	```shell

	pip install -r requirements.txt --no-cache-dir --disable-pip-version-check

	```

  - Start your development `Jupyter Notebook` or `Jupyter Lab` server

	

	```shell

	jupyter lab --notebook-dir=.\notebooks --no-browser

	```

	

	```shell

	jupyter notebook

	```

	

	```

	jupyter_nbextensions_configurator

	```

## References

- [TimeGPT: The First Foundation Model for Time Series Forecasting](https://towardsdatascience.com/timegpt-the-first-foundation-model-for-time-series-forecasting-bf0a75e63b3a)

- [Staggering Returns with PyCryptoBot](https://trading-data-analysis.pro/staggering-returns-with-pycryptobot-39dd2ef5ead5)

- [Trading Data Analysis](https://trading-data-analysis.pro/)

- [Phenomenal Returns with PyCryptoBot](https://trading-data-analysis.pro/phenomenal-returns-with-pycryptobot-16e62f5f684)

- [Leveraging PyCryptoBot for Optimal Cryptocurrency Trading](https://coinsbench.com/leveraging-pycryptobot-for-optimal-cryptocurrency-trading-5b7082354cd3)

- [Forecasting Stock Using Deep Learning Along With Indicators | Medium](https://medium.com/@redeaddiscolll/forecasting-stock-using-deep-learning-along-with-indicators-c1523101c08d)

- [Forecasting Stock Using Deep Learning Along With Indicators | OnePageCode@SubStack](https://onepagecode.substack.com/p/forecasting-stock-using-deep-learning-220)

- [Advanced Stock Pattern Prediction using LSTM with the Attention Mechanism in TensorFlow: A step by step Guide with Apple Inc. (AAPL) Data](https://drlee.io/advanced-stock-pattern-prediction-using-lstm-with-the-attention-mechanism-in-tensorflow-a-step-by-143a2e8b0e95)

- [Spark and Docker: Your Spark development cycle just got 10x faster!](https://towardsdatascience.com/spark-and-docker-your-spark-development-cycle-just-got-10x-faster-f41ed50c67fd)

- [datamechanics/spark on Docker Hub](https://hub.docker.com/r/datamechanics/spark)

- [Setting up a Spark standalone cluster on Docker in layman terms](https://medium.com/@MarinAgli1/setting-up-a-spark-standalone-cluster-on-docker-in-layman-terms-8cbdc9fdd14b)

- [Apache Spark Standalone Cluster on Docker](https://github.com/cluster-apps-on-docker/spark-standalone-cluster-on-docker)

- [Visualizing Trading Signals in Python - Plot buy and sell trading signals in Python's graph](https://eodhd.medium.com/visualizing-trading-signals-in-python-3cab01cc5847)

- [Apache Hadoop and Apache Spark for Big Data Analysis](https://towardsdatascience.com/apache-hadoop-and-apache-spark-for-big-data-analysis-daaf659fd0ee)

- [Agent-Based Stock Trading: Design and Implementation](https://medium.com/@redeaddiscolll/agent-based-stock-trading-design-and-implementation-c2141fc8f984)

- [Mastering K-Means Clustering](https://towardsdatascience.com/mastering-k-means-clustering-065bc42637e4)

- [Additive Decision Trees - An interpretable classification and regression model](https://towardsdatascience.com/additive-decision-trees-85f2feda2223)

- [Interpretable kNN (ikNN) - An interpretable classifier](https://towardsdatascience.com/interpretable-knn-iknn-33d38402b8fc)

- [Interpretable Outlier Detection: Frequent Patterns Outlier Factor (FPOF)](https://towardsdatascience.com/interpretable-outlier-detection-frequent-patterns-outlier-factor-fpof-0d9cbf51b17a)

- [Telco Customer Churn - Kaggle](https://www.kaggle.com/datasets/blastchar/telco-customer-churn)

- [End-to-End Machine Learning Project: TelCo Churn Prediction](https://medium.com/@ramazanolmeez/end-to-end-machine-learning-project-churn-prediction-e9c4d0322ac9)

- [Predicting Stock Prices with Monte Carlo Simulations](https://medium.com/@antoine.boucher012/predicting-stock-prices-with-monte-carlo-simulations-0884ef32c35b)

- [Artificial Intelligence (AI) models for Trading - Exploring Random Forests from Machine Learning](https://medium.com/coinmonks/artificial-intelligence-ai-models-for-trading-0bfd308d012d)

- [XGBoost for Stock Price Forecasting](https://medium.com/@bugragultekin/xgboost-for-stock-price-forecasting-64f89719a8e4)

- [Model Interpretability Using Credit Card Fraud Data](https://towardsdatascience.com/model-interpretability-using-credit-card-fraud-data-f219ff7ec89d)

- [How Many Pokémon Fit?](https://towardsdatascience.com/how-many-pok%C3%A9mon-fit-84f812c0387e)

- [Gold Price Prediction Using Machine Learning](https://medium.com/@iabbasali/gold-price-prediction-using-machine-learning-24e23841de52)

- [Data Leakage in Preprocessing, Explained: A Visual Guide with Code Examples](https://towardsdatascience.com/data-leakage-in-preprocessing-explained-a-visual-guide-with-code-examples-33cbf07507b7)