TL;DR I specify the software versions that I used for the analysis of the EU industry production dataset and provide links to the Github repository where the Jupyter notebooks are stored. Project Background For the analysis of the EU industry production dataset that is summarized here, I made use of various software packages: Anaconda with…
TL;DR I discuss the scope of the analysis of the EU industry production dataset and point to possible extensions with additional datasets. Long Description In the previous study I found that geography is an important factor for characterizing the industry production growth of EU countries. However, this leaves more questions open than it answers. Such…
TL;DR I give a high-level description and discussion of the projects in this portfolio that deal with the analysis of the EU industry production dataset, from finding the data to possible next steps. Long Description In this report, I cover all the way from identifying a dataset on the EU countries’ industry production development in…
TL;DR I use the visualization of the EU countries’ manufacturing growth rate with a pandas/matplotlib bar chart to show that the performance mostly depends on geographical position: the East beats the South. Long Description In the previous project, I plotted the growth dynamics of the EU countries’ industry production manufacturing branch, measured as a constant…
TL;DR I use pandas’ interface to the matplotlib library to create bar charts that visualize the manufacturing growth dynamics of European countries. Long Description I read in the dataframe that contains the slope and intercept parameter values from the linear regression with scikit-learn that I performed in the last project. Using the plot method of…
TL;DR Using the linear model from Python’s scikit-learn package, I obtain the slopes in the EU industry production time series for each country. Long Description I prepare the normalized EU industry production index dataset for the fit routine of the scikit-learn linear model by forcing the time stamps into a 2D numpy array and the…
TL;DR I select a linear model with slope and intercept parameters to describe the growth dynamics of the EU industry production index of each country. Long Description Inspired by line plots of the EU industry production index time series that were previously normalized by the EU average time series, I choose to model the individual…
TL;DR I divide the EU industry production index time series for each country by the smoothed EU average time series to bring out the countries’ individual development for further modeling. Long Description Using a chain of pandas methods to obtain a rolling-mean average, I smooth the EU average time series of the industry production index….
TL;DR I use statistical and graphical tools to perform exploratory data analysis (EDA) on the EU industry production dataset as a starting point for modeling the time series. Long Description With the help of the pandas describe method and the matplotlib package I explore the statistics of the EU industry production dataset, more precisely the…
TL;DR I make EU industry production index values, which I previously put in a tidy form, ready for analysis by splitting numbers and flag values with pandas methods. Long Description Now that the EU industry production dataset has a tidy dataframe structure, I clean up the production index values. The values are stored as strings…