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In this article, I'll discuss map matching, which is the process of matching GPS locations to a digital road network in order to create a reconstructed trajectory. The following section looks at Valhalla, an open-source routing engine. Also, highlight Valhalla's Meili, a map-matching system that utilizes the Hidden Markov Model. Finally, the article explains how to build and start a map-matching container using gis-ops/docker-valhalla, a lightweight and configurable docker image, as well as all of the python code required for map matching.
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In this blog post, I explore the history of earthquakes in Greece and analyze a dataset of earthquake occurrences in the country. I use Python and several libraries such as Pandas, NumPy, Seaborn, Folium, and Matplotlib to clean and analyze the data. I create visualizations and perform statistical analyses to identify patterns and trends in the data, such as the most active regions and the magnitude and frequency of earthquakes over time. The post includes code snippets and visualizations to help readers follow along. Finally, I provide some concluding thoughts on the significance of the findings and some ideas for further research.
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In this blog post, I introduce the concept of classification in machine learning and walk through a practical example of building a classifier to predict whether a passenger on the Titanic survived or not. I use Python and several libraries such as Pandas, NumPy, and Scikit-learn to preprocess the data, split it into training and testing sets, and train and evaluate several classification models, including logistic regression and decision trees. I explain the concepts of accuracy, precision, recall, and F1 score and use them to compare and select the best model. The post includes code snippets and visualizations to help readers follow along. Finally, I provide some concluding thoughts on the importance of classification and some ideas for further exploration.
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In this blog post, I discuss the basics of data analysis, including importing and cleaning data, exploratory data analysis, and data visualization. I use Python and several libraries such as Pandas, Matplotlib, and Seaborn to analyze a real-world dataset of COVID-19 cases. I walk through the steps of cleaning the data, performing descriptive statistics, and creating visualizations to gain insights into the data. The post includes code snippets and visualizations to help readers follow along. Finally, I provide some concluding thoughts on the importance of data analysis and some next steps for further analysis.
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In this blog post, I discuss solving Sudoku puzzles with programming, specifically the problem statement from Project Euler to solve 50 Sudoku puzzles using computer algorithms. I introduce the rules of Sudoku, explain how to approach the problem by creating a Sudoku class in Python, and implement various techniques to solve the puzzles. I include code snippets and performance comparisons between different methods. Finally, I present the final solution to the Project Euler problem and share some thoughts on the overall experience.