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genetic_algorithm_challenge

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Data files, code, and results for Genetic Algorithm Challenge by @Sirajology on Youtube

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# genetic_algorithm_challenge
Data files, code, and results for Genetic Algorithm Challenge by @Sirajology on Youtube

The Challenge
=============
Use the TPOT library to make a discovery based on a question you pose. The question should be based on the Kaggle Climate Change Dataset (compiled from Berkeley Earth), but you get to choose the question.

My Approach
============
The Climate Change Dataset on Kaggle contains several data files for land and ocean temperatures both globally and locally. I chose to address the following question:

**What will the monthly global average temperatures be for the next 5 years?**

I chose to use the file, [GlobalTemperatures.csv](GlobalTemperatures.csv), which contains global average land temperatures in celsius for months from 1750-2015. Here is my process:

1. I cleaned the data using the script [clean_data.py](clean_data.py) to produce a .csv file with features that I wanted TPOT to use and no missing data. The main feature I was interested in was the date (dt, in the original data file). I split the date column in to year, month and day. Here, I also renamed the target variable (LandAverageTemperature) as 'class'. The output of clean_data.py is [cleaned_globaldata.csv](cleaned_globaldata.csv).

2. I created a "fake" .csv file containing the years and months for which I wanted to make temperature predictions. This .csv file is called [global_test.csv](global_test.csv). The original data only runs until the year 2015, so in order to predict temperatures for the next 5 years, my test file contains dates for 2016-2020.

3. I wrote the script [tpot-climate.py](tpot-climate.py) that read in the cleaned temperature data (cleaned_globaldata.csv), and applied the TPOT library to create a machine learning pipeline for predicting temperatures. This script also takes the test file of dates for which the temperature should be predicted (global_test.csv), and outputs the results file [climate_results.csv](climate_results.csv) with these predictions. It also outputs the TPOT optimized pipeline to [climate_pipeline.py](climate_pipeline.py). The file [output.txt](output.txt) is the standard out from running the tpot-climate.py script.

4. I used the outputted, optimized pipeline to see how it would perform when asked to predict monthly global land temperatures on the original data. I did this in [predict_current.py](predict_current.py) to produce [original_data_predictions.csv](original_data_predictions.csv).

5. With all of the predictions from the optimized pipeline, I created some visualizations to understand TPOT's performance, and the climate data.

Results
==========

The resulting optimized pipeline from TPOT implemented FeatureAgglomeration and GradientBoostingRegressor, scoring 0.6283690079003942 MSE on the cross-validation portion of the training data.

The original data set (cleaned), shows that the global land temperature has been gradually increasing over time (averaged by year, below).

![YearlyOriginal.png](https://raw.githubusercontent.com/nhrigby/genetic_algorithm_challenge/master/YearlyOriginal.png)

When we compare the original temperature data to TPOT's predictions, we see that TPOT does a pretty good job predicting global temperatures (averaged by year, below).

![YearlyPredicted.png](https://raw.githubusercontent.com/nhrigby/genetic_algorithm_challenge/master/YearlyPredicted.png)

Finally, to answer the original question, we can look at the monthly temperatures for the next 5 years. These were predicted by the script tpot-climate.py.

![MonthlyPredicted.png](https://raw.githubusercontent.com/nhrigby/genetic_algorithm_challenge/master/MonthlyPredicted.png)

Files in this repository
=======================
* [GlobalTemperatures.csv](GlobalTemperatures.csv) - Original data file of monthly average global land and ocean temperatures downloaded from Kaggle
* [MonthlyPredicted.png](MonthlyPredicted.png) - Figure of monthly temperatures for recent years predicted by TPOT
* [YearlyOriginal.png](YearlyOriginal.png) - Figure of global temperatures, averaged by year, from the cleaned data file
* [YearlyPredicted.png](YearlyPredicted.png) - Figure of global temperatures, averaged by year, for original and predicted values
* [clean_data.py](clean_data.py) - Python script I used to clean the original data file, GlobalTemperatures.csv.
* [cleaned_globaldata.csv](cleaned_globaldata.csv) - Output from clean_data.py. Contains cleaned data from original data file.
* [climate_pipeline.py](climate_pipeline.py) - Exported python script output from tpot-climate.py	containing the TPOT optimized pipeline
* [climate_results.csv](climate_results.csv) - Output from tpot-climate.py. Contains temperature predictions for next 5 years.
* [global_test.csv](global_test.csv) - 'Fake' data file used as testing set, for making 5 year future prediction
* [original_data_predictions.csv](original_data_predictions.csv) - Output from predict_current.py. Contains orininal and TPOT predicted global monthly temperatures.
* [output.txt](output.txt) - Standard out output from running tpot-climate.py
* [predict_current.py](predict_current.py) - Python code I used to run TPOT on cleaned original data. Based on pipeline from climate_pipeline.py.	
* [tpot-climate.py](tpot-climate.py) - Python code I used to run TPOT on training and test data.
* [visualizations.ipynb](visualizations.ipynb) - Jupyter notebook of code I used to create the figures.

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