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| ====== Forecasting ====== | ====== Forecasting ====== | ||
| - | //Updated: 1/19/2016// | + | //Updated: Fall 2020// |
| - | Forecasting focuses on creating a statistical model from data and then using it to predict future trends based on the test data. It is considered a field of data science/statistics. There is also a large craze concerning machine learning recently--the purpose of this team is to learn to adapt statistical and machine learning analysis techniques to weather-related data. | + | Forecasting focuses on creating a statistical model from existing data and then using it to predict future trends based on the test data. The purpose of this team is to take meteorological data from the weatherboxes and apply **machine learning** algorithms to predict future weather conditions across the Manoa campus. |
| - | + | ||
| - | ---- | + | |
| + | {{ :forecasting:forecastingapproach.png?500 |}} | ||
| ===== Introduction ===== | ===== Introduction ===== | ||
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| ===== History ===== | ===== History ===== | ||
| + | |||
| + | *Fall 2020 Edit* | ||
| The SCEL Forecasting team was started in the Fall 2015 semester, headed by Jeremy Garcia under Dr. Anthony Kuh. In the prior months, a few people from the University of California, Santa Cruz, came down to give a presentation about the new iPython (and recently renamed then, Jupyter) platform. They expressed that the University of California system had invested quite heavily into the platform as an accessible, flexible, powerful, and convenient platform to teach analysis alongside the modern, Python programming language. Motivated to demo out this new platform for analysis, Dr. Kuh wanted the team to explore this platform and, with it, begin to learn more about machine learning, as it was a hot topic along with "big data" at the time. | The SCEL Forecasting team was started in the Fall 2015 semester, headed by Jeremy Garcia under Dr. Anthony Kuh. In the prior months, a few people from the University of California, Santa Cruz, came down to give a presentation about the new iPython (and recently renamed then, Jupyter) platform. They expressed that the University of California system had invested quite heavily into the platform as an accessible, flexible, powerful, and convenient platform to teach analysis alongside the modern, Python programming language. Motivated to demo out this new platform for analysis, Dr. Kuh wanted the team to explore this platform and, with it, begin to learn more about machine learning, as it was a hot topic along with "big data" at the time. | ||
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| * Learn and review various statistical techniques and models as applied in Python. | * Learn and review various statistical techniques and models as applied in Python. | ||
| * Start on a new and well documented machine learning framework for SCEL. | * Start on a new and well documented machine learning framework for SCEL. | ||
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| - | ----- | ||
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| - | {{:forecasting:blockdiagramsp16.jpg?700|}} | ||
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| ===== Resources ====== | ===== Resources ====== | ||
| *Fall 2020 Update* | *Fall 2020 Update* | ||
| + | |||
| This semester, we have decided to register for an online machine learning course on Udemy. The course we have chosen is **Machine Learning A-Z™: Hands-On Python & R In Data Science** by Kirill Eremenko. At the time of writing, it is the highest rated course on Udemy for Machine Learning. The price for the course is listed at $100+, but there are frequent sales on Udemy where the courses may be purchased at only 10% of the original cost. Just be patient. | This semester, we have decided to register for an online machine learning course on Udemy. The course we have chosen is **Machine Learning A-Z™: Hands-On Python & R In Data Science** by Kirill Eremenko. At the time of writing, it is the highest rated course on Udemy for Machine Learning. The price for the course is listed at $100+, but there are frequent sales on Udemy where the courses may be purchased at only 10% of the original cost. Just be patient. | ||
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| Previous SCEL machine learning teams have used Jupyter as their development environment of choice. The Udemy course mentioned above uses Google Colab, and so we switch over to that as well. | Previous SCEL machine learning teams have used Jupyter as their development environment of choice. The Udemy course mentioned above uses Google Colab, and so we switch over to that as well. | ||
| - | ==== Jupyter ==== | + | ==== Udemy ==== |
| + | * [[https://www.udemy.com/course/machinelearning/ | Udemy]] - Online learning platform | ||
| + | * [[https://www.udemy.com/share/101WciAEMbdlpWTXQJ/ | Machine Learning A-Z™: Hands-On Python & R In Data Science]] - Online course taken | ||
| + | |||
| + | ==== Jupyter (No longer using)==== | ||
| * [[http://jupyter.readthedocs.org/en/latest/install.html | Install iPython]] - Installation tutorial for iPython. Anaconda recommended for users new to Python. | * [[http://jupyter.readthedocs.org/en/latest/install.html | Install iPython]] - Installation tutorial for iPython. Anaconda recommended for users new to Python. | ||
| * [[forecasting: iPython Tutorials]] | * [[forecasting: iPython Tutorials]] | ||
| * [[.:how to download data]] | * [[.:how to download data]] | ||
| - | ==== Connecting MATLAB to a database ==== | + | ==== Connecting MATLAB to a database (No longer using)==== |
| {{:forecasting:connecting_matlab_to_pos_database_kdelchev.pdf|}}\\ | {{:forecasting:connecting_matlab_to_pos_database_kdelchev.pdf|}}\\ | ||
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| ===== Meeting Minutes ===== | ===== Meeting Minutes ===== | ||
| + | [[forecasting: Fall 2020 Meeting Minutes]]\\ | ||
| [[forecasting: Spring 2017 Meeting Minutes]]\\ | [[forecasting: Spring 2017 Meeting Minutes]]\\ | ||
| [[forecasting: Fall 2016 Meeting Minutes]]\\ | [[forecasting: Fall 2016 Meeting Minutes]]\\ | ||
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| - | ===== Documentation and Notes ==== | + | ===== Documentation and Notes ===== |
| + | |||
| + | ==== Current Notes ==== | ||
| + | [[Data Preprocessing]]\\ | ||
| + | [[Regression]]\\ | ||
| + | [[Classification]]\\ | ||
| + | [[Clustering]]\\ | ||
| + | |||
| + | |||
| + | ==== Archived Notes ==== | ||
| [[https://docs.google.com/document/d/1keX9LUpu0hDK2uMDyuJe8mySTMc1qQ0sMVJLVDT1odA/edit|General iPython Notes]] | [[https://docs.google.com/document/d/1keX9LUpu0hDK2uMDyuJe8mySTMc1qQ0sMVJLVDT1odA/edit|General iPython Notes]] | ||
| \\ | \\ | ||