aepoetry
Data science and (urban) analytics enthusiast with a civil engineering diploma
Jakarta, Indonesia
Pinned Repositories
beam-me-up
This is a project about a ride-sharing business that operating in several big cities in Turkey. The company provide motorcycles ride-sharing service for Turkey’s citizen, and really value the efficiency in traveling through the traffic–the apps even give some reference to Star Trek “beam me up” to their order buttons. In this project, we are going to help them and forecast the driver demand for the next 5 working days.
dataanalysis
Course Materials for Practical Data Analysis with Python and SQL
happy_planet_index_2016
The Happy Planet Index (HPI) is an index of human well-being and environmental impact that was introduced by NEF, a UK-based economic think tank promoting social, economic and environmental justice. The index is weighted to give progressively higher scores to nations with lower ecological footprints. I downloaded the 2016 dataset from HPI website. My goal is to find correlations between several variables, then use clustering technic to seprarate these 140 countries into different clusters, according to happiness, wealth, life expectancy and carbon emissions.
heart-disease
Millions of people are getting some sort of heart disease every year and heart disease is the biggest killer of both men and women in the United States and around the world. Statistical analysis has identified many risk factors associated with heart disease such as age, blood pressure, total cholesterol, diabetes, hypertension, family history of heart disease, obesity, lack of physical exercise, etc. In this notebook, we're going to run statistical testings and regression models using the Cleveland heart disease dataset to assess one particular factor -- maximum heart rate one can achieve during exercise and how it is associated with a higher likelihood of getting heart disease.
pp_argentina
I will analyze ten economic and social indicators collected for each province in Argentina using Principal Component Analysis (PCA) to reduce redundancies and highlight patterns that are not apparent in the raw data. After visualizing the patterns and use k-means clustering to partition the provinces into groups with similar development levels. These results can be used to plan public policy by helping allocate resources to develop infrastructure, education, and welfare programs.
R-or-python
Throughout the world of data science, there are many languages and tools that can be used to complete a given task. While you are often able to use whichever tool you prefer, it is often important for analysts to work with similar platforms so that they can share their code with one another. Learning what professionals in the data science industry use while at work can help you gain a better understanding of things that you may be asked to do in the future.
aepoetry's Repositories
aepoetry/beam-me-up
This is a project about a ride-sharing business that operating in several big cities in Turkey. The company provide motorcycles ride-sharing service for Turkey’s citizen, and really value the efficiency in traveling through the traffic–the apps even give some reference to Star Trek “beam me up” to their order buttons. In this project, we are going to help them and forecast the driver demand for the next 5 working days.
aepoetry/dataanalysis
Course Materials for Practical Data Analysis with Python and SQL
aepoetry/happy_planet_index_2016
The Happy Planet Index (HPI) is an index of human well-being and environmental impact that was introduced by NEF, a UK-based economic think tank promoting social, economic and environmental justice. The index is weighted to give progressively higher scores to nations with lower ecological footprints. I downloaded the 2016 dataset from HPI website. My goal is to find correlations between several variables, then use clustering technic to seprarate these 140 countries into different clusters, according to happiness, wealth, life expectancy and carbon emissions.
aepoetry/heart-disease
Millions of people are getting some sort of heart disease every year and heart disease is the biggest killer of both men and women in the United States and around the world. Statistical analysis has identified many risk factors associated with heart disease such as age, blood pressure, total cholesterol, diabetes, hypertension, family history of heart disease, obesity, lack of physical exercise, etc. In this notebook, we're going to run statistical testings and regression models using the Cleveland heart disease dataset to assess one particular factor -- maximum heart rate one can achieve during exercise and how it is associated with a higher likelihood of getting heart disease.
aepoetry/pp_argentina
I will analyze ten economic and social indicators collected for each province in Argentina using Principal Component Analysis (PCA) to reduce redundancies and highlight patterns that are not apparent in the raw data. After visualizing the patterns and use k-means clustering to partition the provinces into groups with similar development levels. These results can be used to plan public policy by helping allocate resources to develop infrastructure, education, and welfare programs.
aepoetry/R-or-python
Throughout the world of data science, there are many languages and tools that can be used to complete a given task. While you are often able to use whichever tool you prefer, it is often important for analysts to work with similar platforms so that they can share their code with one another. Learning what professionals in the data science industry use while at work can help you gain a better understanding of things that you may be asked to do in the future.