All posts tagged: Python Programming

Data Analysis and Interpretation Capstone

So, this is the end. It took six months, but today I completed and was certified for the Data Analysis and Interpretation Specialization by Wesleyan University through Coursera. When I first started in October 2015, I had no idea how to write code in Python, let alone produce graphs and run statistical analysis. It has been a fun experience learning how to write code in Python and learning the different kinds of statistical methods. Ironically, I learned these after I left graduate school. One would think that these are method courses you would take in school. For the Capstone Project, I do wish the data was more complete and over a longer period of time. It is difficult to run analysis on data that only goes back as far as 1972 and in many cases, missing records for many years in between. The results can be quite misleading, as it pointed to fertility rate as being highly correlated with environmental sustainability. However, fertility rate, in many cases is contingent on many different factors that are both quantitative …

Capstone Project: Results

For those following my blog on my Data Analysis and Interpretation Specialization by Wesleyan University through Coursera, this is the final course and the Capstone project. Unlike previous courses, I will move away from urbanization data and try to tackle one of the problems provided by the course’s industry partner. This is my introduction. Below is our third assignment – the preliminary results. Results Only the results for Burundi, Ethiopia, and Liberia will be reported, as the other countries demonstrated no change or very slight change in the ensure environmental sustainability index. Descriptive Statistics: The following table shows the descriptive statistics for the Ensure Environmental Sustainability Index for each of the selected countries, starting from the lowest GDP per capita group to the highest. The standard deviations are much greater for the lowest GDP per capita group compared to the others. In three countries, Seychelles, Canada, and Ireland, no change in the value of the index was observed. It would appear that countries that reach a certain GDP per capita will have achieved a mean Ensure …

k-Means Cluster Analysis – Machine Learning

Machine Learning Data Analysis This is the last lesson of the fourth course of my Data Analysis and Interpretation Specialization by Wesleyan University through Coursera. If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? For this assignment, the goal is to run a k-Means Cluster Analysis using my variables: Urban Population, Urban Population Growth, GDP Growth, Population Growth, Employment Rate, and Energy Use per Capita in 2007. Here, GDP per Capita in 2007 is used as the validation variable. I am trying to identify if there are clusters of characteristics that associate with certain values of GDP per Capita based on national data from 2007. As before, the data is split into 70% training data and 30% test data. However, the k-means cluster analysis will only be run on the training data set. The Elbow Curve Graph shows that 2, 3, and 4 clusters could be interpreted, though it is …

Lasso Regression – Machine Learning

Machine Learning Data Analysis This is the third lesson of the fourth course of my Data Analysis and Interpretation Specialization by Wesleyan University through Coursera. If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? For this assignment, the goal is to run a Lasso Regression that identifies the impact of each of my explanatory variables: Urban Population, Urban Population Growth, GDP Growth, Population Growth, Employment Rate, and Energy Use per Capita in 2007. As it is a linear regression model, I am able to use a quantitative variable. Unlike the previous lesson, I can use GDP per Capita 2007 as is, without having to convert it into a categorical variable. This time, the training data set is 70% and the test data set is 30% of the original data, which means there are 100 observations in my training data set vs. 43 in my test data set. pred_train.shape = (100, 6) …

Random Forests – Machine Learning

Machine Learning Data Analysis This is the second lesson of the fourth course of my Data Analysis and Interpretation Specialization by Wesleyan University through Coursera. If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? For this assignment, the goal is to create a random forest that identifies the varying importance of my explanatory variables: Urban Population, Urban Population Growth, GDP Growth, Population Growth, Employment Rate, and Energy Use per Capita in 2007. For my response variable, I created a categorical variable from GDP per Capita 2007. I separated the data into two levels, where GDP per Capita 2007 is lower than 10000 is 0 or low and where GDP per Capita 2007 is higher than 10000 is 1 or high. Just as in the last assignment, when my test sample is set at 40%, the result is 58 test samples and 85 training samples out of 143 total, with …

Decision Trees – Machine Learning

Machine Learning Data Analysis This is the start of the fourth course of my Data Analysis and Interpretation Specialization by Wesleyan University through Coursera. If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? Now, as I have started working, I do not have as much time. For this course, I decided to focus solely on Python, instead of both Python and SAS as in the past. I am not abandoning SAS but I will probably take the time to learn SAS after this course ends. For this assignment, the goal is to create a decision tree that correct classifies samples according to a binary, categorical response variable. For my response variable, I created a categorical variable from GDP per Capita 2007. I separated the data into two levels, where GDP per Capita 2007 is lower than 10000 is 0 or low and where GDP per Capita 2007 is …

Logistics Regression on Economic Development

Last lesson of Regression Modelling in Practice… If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? Through the past two courses, Data Analysis Tools and Data Management and Visualization, I looked at the correlation between urbanization and economic development and established that there was a correlation between urban population and GDP per capita. For this last assignment in the course Regression Modelling in Practice, I am again examining GDP per Capita as the response variable. I am using the new data set I created in the last assignment from Gapminer, which as  I explained, holds a more complete set of data if I used the year 2007 instead of 2010. As a logistic regression is performed on a categorical response variable with two levels and multiple explanatory variables, I had to bin GDP per Capita into two and recode them: 0 = Countries with a GDP per Capita less than …