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api
CS591Report.pdf
MLP.png
README.md
correlations.py
getData.py
merge2012.py
merge2013.py
poster.pdf
removeOutliers.py
removeOutliers2013.py
runMachineLearning.py
statsmodel.py
statsmodel_correlations.png
statsmodel_results_with_outliers.png
statsmodel_results_without_outliers.png

README.md

angelay_maulikjs Project 2

This project is a continuation of angelay's project 1.

getData.py retrieves the 7 datasets

merge2012.py merges all the data from 2012 and puts them into a collection called all2012. There are 129 entries in all2012.

merge2013.py merges all the data from 2013 and puts them into a collection called all2013. There are 129 entries in all2013.

removeOutliers.py takes the data in all2012 and removes all entries with outliers. This leaves us 87 entries left and stored in collection clean2012.

removeOutliers2013.py takes the data in all2013 and removes all entries with outliers. This leaves us 87 entries left and stored in collection clean2013.

correlations.py takes all the data from clean2012 and returns the correlation coefficient of CO2 emissions and all 6 other attributes: carbon intensity, energy intensity, energy use, GDP per capita, Human Development Index, and population. The results are stored in collection corr2012.

statsmodel.py runs the statistical analysis and runMachineLearning.py runs the machine learning.

In project 2, we are trying to build a model that will predict a country's CO2 emissions given its population, GDP per capita, carbon intensity, energy intensity, energy use, and HDI. We took two different approaches to solve this problem: machine learning and statistical analysis.

In statsmodel.py, we performed statistical analysis and built our model by adding one independent variable at a time, in descending order of the R-squared value of the linear model of CO2 emissions with only that variable. We based our methodology on the Kaya Identity, which states that CO2 emissions is equal to population * GDP per capita * carbon intensity * energy intensity. We added energy use and HDI to optimize our model.

We first built our model with clean 2012 data without outliers and tested it on clean 2013 data without outliers, and obtained an R-squared value of 0.984442. Then we built our model with all 2012 data with outliers and tested it in all 2013 data with outliers, and obtained an R-squared value of 0.998919. When running statsmodel.py, it will produce three graphs saved in the same directory, the first one is the pair-wise correlations between all variables, the second one is the results of the model on clean data, and the third one is the result of the model on all data.

In Machine Learning we are using a Multilyer Perceptron Network to try and fit the model to the given data with regularization:0.01. (To prevent overfitting). We used n^5 hidden layers where n is the number of variables. The model runs for max 1000 iterations which gives us a good enough model given some margin of error but it hasnt converged yet. It has an inital learning rate of 0.001 and a momentum of 0.4

We first scaled the input using StandardScaler and then transformed both the training and testing inputs using the same scaler we used to scale the training data.
We then trained the model on the training set and used the testing set (clean2013 data) to predict the output.
We compared the predicted values from the model to the known output values of clean2013.
The model yields an R-squared value of 0.9978 for 1000 iterations which is pretty accurate and ideally this could go higher than the statistical model if allowed to converge, but this would take a really long time.
The results can be seen in the plot MLP.png.
REF: http://dstath.users.uth.gr/papers/IJRS2009_Stathakis.pdf

To run all files, execute the following command in root directory (course-2017-fal-proj):

python3 execute.py angelay_maulikjs
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