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https://github.com/alvertogit/datascience

Data Science AI Artificial Intelligence Machine Deep Learning Classification Regression Python Keras TensorFlow TensorFlow2 TPOT XGBoost Matplotlib NumPy Pandas scikit-learn Folium Seaborn Jupyter Lab Notebook
https://github.com/alvertogit/datascience

artificial-intelligence data-science deep-learning jupyter-lab jupyter-notebook keras machine-learning matplotlib pandas python scikit-learn tensorflow tensorflow2

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Data Science AI Artificial Intelligence Machine Deep Learning Classification Regression Python Keras TensorFlow TensorFlow2 TPOT XGBoost Matplotlib NumPy Pandas scikit-learn Folium Seaborn Jupyter Lab Notebook

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README 

        
# DATA SCIENCE



This repository stores [Jupyter Notebooks] to demonstrate skills in [Data Science], [Artificial Intelligence], classification and regression problems with [Python], [Keras], [scikit-learn], [Matplotlib], [NumPy], [Pandas], [TPOT], [XGBoost], [Folium], [Seaborn] among others.



## DEPENDENCIES



The code has been tested using:



- [Python] (3.10): an interpreted high-level programming language for general-purpose programming.

- [Jupyter Lab] (4.3): a web-based interactive development environment for [Jupyter Notebooks], code, and data.

- [Keras] ([TensorFlow] built-in): a high-level neural networks [API], written in [Python] and capable of running on top of [TensorFlow], CNTK, or Theano.

- [TensorFlow] (2.18): an open source [Deep Learning] library for high performance numerical computation using data flow graphs.

- [Matplotlib] (3.9): a plotting library for [Python] and its numerical mathematics extension [NumPy].

- [NumPy] (2.0): a library for [Python], adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

- [Pandas] (2.2):  an open source library providing high-performance, easy-to-use data structures and data analysis tools for [Python].

- [scikit-learn] (1.5): a [machine learning] library for [Python]. It features various classification, regression and clustering algorithms including support vector machines, [random forest], [gradient boosting], k-means and DBSCAN.

- [scikit-image] (0.24): a collection of algorithms for image processing with [Python].

- [TPOT] (0.12): a [Python] Automated [Machine Learning] tool that optimizes [machine learning] pipelines using genetic programming.

- [XGBoost] (2.1): an optimized distributed [gradient boosting] library designed to be highly efficient, flexible and portable.

- [Folium] (0.18): an open source library to visualize data that has been manipulated in [Python] on an interactive [Leaflet.js] map.

- [ipyleaflet] (0.19): a [Jupyter] / [Leaflet.js] bridge enabling interactive maps in [Jupyter Notebook].

- [Seaborn] (0.13): a [Python] visualization library based on [Matplotlib]. It provides a high-level interface for drawing attractive statistical graphics.

- [imbalanced-learn] (0.12): a [Python] package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. It is compatible with [scikit-learn] and it allows [SMOTE (Synthetic Minority Over-sampling Technique)].

- [joblib] (1.4): a set of tools to provide lightweight pipelining in [Python].

- [findspark] (2.0): a package to make [Spark] Context available in [Jupyter Notebook].



Virtual environment (=**.venv**) can be generated with  **requirements.txt** file found in main folder.



Command to configure virtual environment with [venv]:



```bash

~/datascience$ python3 -m venv .venv

~/datascience$ source .venv/bin/activate

(.venv)~/datascience$ python3 -m pip install pip==24.3.1

(.venv)~/datascience$ python3 -m pip install setuptools==75.5.0

(.venv)~/datascience$ python3 -m pip install -r requirements.txt

(.venv)~/datascience$ pre-commit install

```



### HOW TO RUN NOTEBOOKS



A good way to play with the [Jupyter Notebooks] is through [Jupyter Lab]. To run any of them use the command shown below:



```bash

(.venv)~/datascience$ jupyter lab .ipynb

```



It might be also necessary to install locally [Graphviz] for rendering graph images with the command:



```bash

~/datascience$ sudo apt-get install graphviz

```



Graph image example of a decision tree is shown below.



![Graph image example of a decision tree](images/tree_top3.png)



## CREDITS



author: alvertogit

copyright: 2018-2024



[Data Science]: https://en.wikipedia.org/wiki/Data_science

[Artificial Intelligence]: https://en.wikipedia.org/wiki/Artificial_intelligence

[Python]: https://www.python.org/

[Machine Learning]: https://en.wikipedia.org/wiki/Machine_learning

[Deep Learning]: https://en.wikipedia.org/wiki/Deep_learning

[Random Forest]: https://en.wikipedia.org/wiki/Random_forest

[Gradient Boosting]: https://en.wikipedia.org/wiki/Gradient_boosting

[API]: https://en.wikipedia.org/wiki/Application_programming_interface

[Keras]: https://keras.io/

[TensorFlow]: https://www.tensorflow.org/

[Matplotlib]: https://matplotlib.org/

[NumPy]: https://numpy.org/

[Pandas]: https://pandas.pydata.org/

[scikit-learn]: https://scikit-learn.org/stable/

[scikit-image]: https://scikit-image.org/

[TPOT]: https://github.com/EpistasisLab/tpot

[XGBoost]: https://github.com/dmlc/xgboost

[Folium]: https://github.com/python-visualization/folium

[Leaflet.js]: https://leafletjs.com/

[ipyleaflet]: https://github.com/jupyter-widgets/ipyleaflet

[Seaborn]: http://seaborn.pydata.org/

[imbalanced-learn]: https://github.com/scikit-learn-contrib/imbalanced-learn

[SMOTE (Synthetic Minority Over-sampling Technique)]: https://jair.org/index.php/jair/article/view/10302

[joblib]: https://pypi.org/project/joblib/

[Jupyter]: https://jupyter.org/

[Jupyter Lab]: https://jupyter.org/

[Jupyter Notebook]: https://jupyter.org/

[Jupyter Notebooks]: https://jupyter.org/

[findspark]: https://github.com/minrk/findspark

[Spark]: https://spark.apache.org/

[venv]: https://docs.python.org/3/library/venv.html

[Graphviz]: https://www.graphviz.org/