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genetic_algorithm_challenge

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Genetic Algorithm Challenge by @Sirajology on Youtube

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# genetic_algorithm_challenge
Genetic Algorithm Challenge for Learn Python for Data Science #6 by @Sirajology on [Youtube](https://youtu.be/dSofAXnnFrY)

Overview
============
This is the code for Genetic Algorithms by @Sirajology on [Youtube](https://youtu.be/dSofAXnnFrY). In this demo code we use the [MAGIC Gamma Telescope dataset](https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope) to build a classifer. The classifier will train on the dataset and then be able to classify whether or not some energy is either Gamma Radiation or Hadron Radiation. Instead of guessing and checking the best ML model and hyperparameters to use, we use a genetic programming library called [tpot](https://github.com/rhiever/tpot) to do that for us by trying out a bunch of them. See [this](https://github.com/rhiever/tpot/tree/master/tutorials/MAGIC%20Gamma%20Telescope) link for an IPython notebook version of this code.

Dependencies
============

* Numpy 
* tpot
* scikit-learn
* pandas

Use [pip](https://pypi.python.org/pypi/pip) to install any missing dependencies

Usage
===========

To run the demo code, after installing the dependencies, just run the following in terminal

``python3 demo.py``

Challenge
===========
The challenge for this video is to use the TPOT library to make a discovery based on a question you pose. '

Step 1 - Download this Climate Change Dataset

Step 2 - Think of a question that [this](https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data) dataset will help you answer like "Has the temperature in India risen over the past 20 years?"

Step 3 - Clean the data and use TPOT to help you build a machine learning pipeline to answer your question

Step 4 - Post your GitHub link in the comments!

Credits
===========
Credit for the vast majority of code here goes to [Randy Olson](https://github.com/rhiever). I've merely created a wrapper around all of the important functions to get people started.

genetic_algorithm_challenge

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