Package: ds-boost
Data science projects with Keras (Poetry Version)Author:Angel Martinez-TenorRepository:Github linkThis repo contains a set of data science projects solved with artificial neural networks implemented inKeras. It is based on a set of use cases fromUdacity,Coursera&KaggleThe repo also introduces a minimal packageds_boostinitally implemented as a helper for this repoDisclaimer: 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 scientistAnon-poetry versionof this repo is available in the branchno-poetryScenariosClassification modelsEnron ScandalIdentifies Enron employees who may have committed fraudProperty Maintenance FinesPredicts the probability of a set of blight tickets to be paid on timeSentiment IMDBPredicts positive or negative sentiments from movie reviews (NLP)Spam detectorPredicts the probability that a given email is a spam email (NLP)Student AdmissionsPredicts student admissions to graduate school at UCLATitanicPredicts survival probabilities from the sinking of the RMS TitanicRegression modelsBike RentalPredicts daily bike rental ridershipHouse PricesPredicts house sales prices from Ames Housing databaseSimple ticketsPredicts the number of tickets requested by different clientsRecurrent modelsMachine TranslationTranslates sentences from English to French (NLP)Simple Stock PredictionPredicts Alphabet Inc. stock priceText generatorCreates an English language sequence generator (NLP)Social network modelsNetworkPredicts missing salaries and new email connections from a company's email networkSetup & UsagePython 3.10+ requiredClone the repository usinggit:gitclonehttps://github.com/angelmtenor/data-science-keras.gitEnter to the root path of the repo and use or create a new conda environment for development:$condacreate-ndevpython=3.10-y&&condaactivatedevInstall the minimal package developed as a helper for this repo:pipinstalldist/ds_boost-0.1.0-py3-none-any.whlOpen the desired project/s withJupyter Notebookcddata-science-keras
jupyternotebookDevelopment ModeIn the root folder of the cloned repository, install all the required dev packages and the ds-boost mini package (Makerequired):makesetupTo install tensorflow with GPU support, follow the instructions of this guide:Install TensorFlow GPU.QA (manual pre-commit):makeqaDevelopment 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 fromnon-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 skippedThe development container defined in.devcontainer/Dockerfilecan be directly used for a fast setup (Docker required).  With Visual Studio Code, just open the root folder of this repo, pressF1and select the optionDev Containers: Open Workspace in Container. The container will open the same workspace after the Docker Image is built.ContributingCheck out the contributing guidelinesLicenseds_boostwas created by Angel Martinez-Tenor. It is licensed under the terms of the MIT license.Creditsds_boostwas created from a Data Science Template developed by Angel Martinez-Tenor. The template was built uponpy-pkgs-cookiecutter[template] (https://github.com/py-pkgs/py-pkgs-cookiecutter)