ds-boost 0.1.0

Package for Practical & efficient Data Science in Python. Initially written for data-science-keras repo

Data science projects with Keras (Poetry Version)

Author: Angel Martinez-Tenor

Repository: Github link

This repo contains a set of data science projects solved with artificial neural networks implemented in Keras. It is based on a set of use cases from Udacity, Coursera & Kaggle

The repo also introduces a minimal package ds_boost initally implemented as a helper for this repo

Disclaimer: This notebooks-based repo was developed in early 2018. Since July 2022, I'm updating it using the best practices I've learned implementing solutions in production environment my experience as a lead data scientist

A non-poetry version of this repo is available in the branch no-poetry

Scenarios

Classification models

• Enron Scandal Identifies Enron employees who may have committed fraud

• Property Maintenance Fines Predicts the probability of a set of blight tickets to be paid on time

• Sentiment IMDB Predicts positive or negative sentiments from movie reviews (NLP)

• Spam detector Predicts the probability that a given email is a spam email (NLP)

• Student Admissions Predicts student admissions to graduate school at UCLA

• Titanic Predicts survival probabilities from the sinking of the RMS Titanic

Regression models

• Bike Rental Predicts daily bike rental ridership

• House Prices Predicts house sales prices from Ames Housing database

• Simple tickets Predicts the number of tickets requested by different clients

Recurrent models

• Machine Translation Translates sentences from English to French (NLP)

• Simple Stock Prediction Predicts Alphabet Inc. stock price

• Text generator Creates an English language sequence generator (NLP)

Social network models

• Network Predicts missing salaries and new email connections from a company's email network

Setup & Usage

Python 3.8+ required. Conda environment with Python 3.10 suggested

1. Clone the repository using git:

   git clone https://github.com/angelmtenor/data-science-keras.git
   

2. Enter to the root path of the repo and use or create a new conda environment for development:

$ conda create -n dev python=3.10 -y && conda activate dev

3. Install the minimal package developed as a helper for this repo:

   pip install dist/ds_boost-0.1.0-py3-none-any.whl
   

4. Open the desired project/s with Jupyter Notebook

   cd data-science-keras
   jupyter notebook
   

Development Mode

In the root folder of the cloned repository, install all the required dev packages and the ds-boost mini package (Make required):

make setup

To install tensorflow with GPU support, follow the instructions of this guide: Install TensorFlow GPU.

QA (manual pre-commit):

make qa

Development Tools Required:

A Container/Machine with Conda, Git and Poetry as closely as defined in .devcontainer/Dockerfile:

• This Dockerfile contains a non-root user so the same configuration can be applied to a WSL Ubuntu Machine and any Debian/Ubuntu CLoud Machine (Vertex AI workbench, Azure VM ...)
• In case of having an Ubuntu/Debian machine with non-root user (e.g.: Ubuntu in WSL, Vertex AI VM ...), just install the tools from non-root user (no sudo)* section of .devcontainer/Dockerfile (sudo apt-get install <software> may be required)
• A pre-configured Cloud VM usually has Git and Conda pre-installed, those steps can be skipped
• The development container defined in .devcontainer/Dockerfile can be directly used for a fast setup (Docker required). With Visual Studio Code, just open the root folder of this repo, press F1 and select the option Dev Containers: Open Workspace in Container. The container will open the same workspace after the Docker Image is built.

Contributing

Check out the contributing guidelines

License

ds_boost was created by Angel Martinez-Tenor. It is licensed under the terms of the MIT license.

Credits

ds_boost was created from a Data Science Template developed by Angel Martinez-Tenor. The template was built upon py-pkgs-cookiecutter [template] (https://github.com/py-pkgs/py-pkgs-cookiecutter)