Location! location! location! Where people live in space tells us a lot about the space itself and people of who live there. Today's demo is about spatial data visualization with `tidycensus` package using population and race are two example variables I am curious about. First we will get the big picture with Virginia state level … Continue reading Spatial data visualization with `tidycensus`
Problem statement Two determinants of business revenue/profit are the price of the products and how many of them are sold. At higher price the revenue is expected to be high. But this is not the case all the time. We know from our everyday experience, as the price of something goes up, people have less … Continue reading Price optimization: maximizing revenue/profit
Results and Discussion “The internet is killing retail. Bookstores are just the first to go.” -- quoted in the NYT article. Retail bookstores are in death row; looks like it's just a matter of time for those to be history. eBooks are partly to blame, but with eBook sales leveling off recently, the remaining affect … Continue reading Predicting the demise of retail bookstores: a time series forecasting
Beginner or an advanced learner, if you are interested in time series analysis and forecasting only few reading materials should meet your 95% needs. In Python, statsmodels is a good place to start with. statsmodels is a Python module for statistical analysis and has some good time series and forecasting examples. You should also read … Continue reading Time series forecasting: Python or R?
Table of Contents IntroductionObjectivesData & Methods Results 4.1 Exploratory Data Analysis (EDA) 4.2 Forecasting 4.2.1 Input data & decomposition 4.2.2 Forecasting with HW Exponential Smoothing 4.2.3 Forecasting with ETS 4.2.4 Forecasting with ARIMA Discussion & Conclusion 5.1 Model evaluation 5.2 General conclusions 5.3 Discussion 1. Introduction New house construction & sales plays a significant role … Continue reading Time Series Analysis & Forecasting of New Home Sales
Disclaimer I'm not going to discuss time series and forecasting theories, not here, not anytime in the future in this writing series. I am deliberately avoiding any and all theories, as much as possible (if you are curious why, I wrote about it in here). If you are interested in theories, there are plenty of materials … Continue reading Benchmark forecasting example: Japanese population by 2030
What is a good starting point in data science learning process? Traditionally we start with theories, then the mechanics and finally make hands dirty. This rapidly kills excitement by exponentially loosing interest, and at some point get out of track. This happens more often then not before doing any of the problem solving work we … Continue reading Learning Data Science by examples rather than theories