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๐Ÿš€ Data Science Projects

This is a collection of Data Science projects for learning and exploration purposes. The projects are organized and grouped by subject/topic covering different approaches, algorithms and data-sets. Each project consists of a Jupyter notebook and, it has its own folder under the notebooks folder.


โš ๏ธ This repository contains code and models experiments and are not production-ready, reusable, optimised and fine-tuned code and models. This is rather a sandbox or a playground for learning and trying different data science, machine learning techniques and approaches. Models might not perform well, and there is a place for overfitting/underfitting.

Acknowledgements: This repository was originally inspired by ๐Ÿค– Interactive Machine Learning Experiments.

Projects

Projects were built using different libraries and tools, and the most used were pandas, scikit-learn and Tensorflow 2 with Keras API. The dependencies for each project is included in a requirements.txt file, for projects where the data-sets were auto-generated or scraped, a data folder is present.

Blog: For some projects, I have written a dedicated blog post on my website. The projects with a blog post have an ๐Ÿ“ icon link next to the project name.

Supervised Learning

ย  Project Notebook Tags Dataset
Titanic Titanic: Machine Learning from Disaster ๐Ÿ“ Binder
Open In Colab
Classification Titanic
Credit Card Fraud Detection Credit Card Fraud Detection ๐Ÿ“ Binder
Open In Colab
Imbalanced Classification Credit Card Fraud Detection

Setup

To run the repository locally, I suggest using docker to launch a Jupyter Notebook server.

It is based on the jupyter/tensorflow-notebook, which includes popular packages from the scientific Python ecosystem.

Requirements

Launch Jupyter Server

Run Jupyter with docker-compose and open the link shown on your terminal (something like http://localhost:8888).

$ docker-compose up

The projects will be available under the notebooks folder.

Configuration

Feel free to change any settings of the Jupyter Notebook server by editing the docker-compose.yml file.

Articles