#geohash

All About AI: Hash, GeoHash, Binary & More: 5 Categorical Encoding Tricks for Your ML Model Discover the power of encoding techniques in machine learning with this detailed guide! In this video, we explore five essential encoding methods—Hash Encoding, Geo Hash Encoding, Gray Encoding, Base N Encoding, and Binary Encoding—to convert categorical data (like temperature, color, and city names) into numerical values that machine learning models can understand. Learn how each technique works, its benefits, drawbacks, and real-world use cases, including text data processing, location-based apps, hardware applications, and tree-based models like LightGBM and XGBoost. We dive into practical examples using a simple table with categories like temperature and color, showing how to transform them into numbers with Python code. Follow along as we implement these encodings step-by-step using libraries like pandas, category_encoders, and geohash, with a focus on high-cardinality data, dimensionality reduction, and model performance optimization. Whether you're dealing with big data, spatial proximity, or compact representations, this video has you covered! Perfect for beginners and advanced learners, this tutorial includes a visual demonstration using Manim to animate the encoding process for temperature values. Boost your ML skills by choosing the right encoding technique for your project—hash for scalability, geo hash for location clustering, gray for hardware stability, base n for memory constraints, or binary for tree-based models. Don’t miss the Python code walkthrough to see these techniques in action! Subscribe for more machine learning tutorials and hit the like button if you found this helpful! Let us know in the comments which encoding you’ll use next! Timestamps: • 0:00 - Introduction to Encoding Techniques • 0:40 - Hash Encoding Explained • 1:48 - Geo Hash Encoding for Locations • 2:50 - Gray Encoding for Hardware • 4:20 - Base N Encoding Overview • 5:47 - Binary Encoding for ML Models • 7:31 – Comparison of 5 methods • 8:24 – Choosing the Right Encoding from these 5 methods • 9:23 - Python Implementation with Code • 16:00 - Choosing the Right Encoding • 14:30 – Manim visualization and Conclusion For more videos on categorical variable encoding, you can bookmark this playlist: https://youtube.com/playlist?list=PLSOxlehGDVekco2u5EFQaLtsQ5ltByoQi&si=23mxubE9wRkORhmR For Video on Advanced level AI (AI Practitioner) you can watch video playlist: https://youtube.com/playlist?list=PLSOxlehGDVelNTUs8IEtkpgE0ySwAysXe&si=sj-nYswCRhH39HtY For Video on All about AI basic level tutorial (AI Enthusiast) you can follow below playlist: https://youtube.com/playlist?list=PLSOxlehGDVekaOfY5TMmI5MS1zbNJLUFL&si=9i3d03xIxO4ghYMK If you are interested in Generative AI , please follow this playlist: https://youtube.com/playlist?list=PLSOxlehGDVen1UFY0daIegmmZHm3jjQ_Y&si=3TCTX-87q58KUXju If you are looking for videos on book summary, about life, psychology and philosophy, you can follow this playlist: https://www.youtube.com/playlist?list=PLSOxlehGDVelY34fDoL3qtppXuzmvo5Of For videos on AI , Machine Learning and Data Science, follow this playlist: https://www.youtube.com/playlist?list=PLSOxlehGDVenLrEROl1JxkNuCIBVeddHu https://youtu.be/8KwmesGwhXo #MachineLearning #EncodingTechniques #HashEncoding #GeoHashEncoding #GrayEncoding #BaseNEncoding #BinaryEncoding #CategoricalData #DimensionalityReduction #PythonML #HighCardinality #MLPreprocessing #TreeBasedModels #LightGBM #XGBoost #LocationEncoding #HardwareEncoding #DataTransformation #ManimAnimation #mltutorial machine learning encoding, hash encoding, geo hash encoding, gray encoding, base n encoding, binary encoding, categorical data, dimensionality reduction, Python machine learning, high cardinality data, ML preprocessing, tree-based models, LightGBM, XGBoost, location-based encoding, hardware encoding, data transformation, Manim animation, ML tutorial, encoding techniques explained via YouTube https://www.youtube.com/watch?v=8KwmesGwhXo

Landing in Hong Kong.

https://code.google.com/p/python-geohash/

With the introduction of yet another geocoding, open location codes by google. I want to propose one I described earlier on hn. Please reply to @mxfh if you think this has some valid use, might code a sample implementation later this year. --- I'm pretty certain one could make a geohash compatible partly human readable hybrid that performs way better [...]. Something like DE.BERLIN.2.1H First, human readable part defines a look-up to defined geohash of center of region on a given level. So DE.BERLIN could resolve to u33, u33d or u33db (length/level 5) the last one seems reasonable, since a few multiples of the area cover the whole city of Berlin http://geohash.2ch.to/u33db Second, optional, part encodes offset of Z-Curve index of location at same zoom level. Z-Curve on globe: http://www.bigdatamodeling.org/2013/01/intuitive-geohash.htm... This enables global coverage at any level if needed, and makes the code robust to administrative changes that affect the area of the given region! The offset part could start at 1 and alternate the sign with each increment. isOdd(offsetPart) ? -(offsetPart-1)/2 : offsetPart/2; 1 → 0, 2 → 1, 3 → -1, 4 → 2 An offset part of 1 equals an offset of 0 and could be omitted. The third part adds to the precision of that given cell. Brandenburg Gate, within area of origin cell of region part. could be written as DE.BERLIN.1.2M3 or short DE.BERLIN.2M3 http://geohash.2ch.to/u33db2m3 DE.Berlin.2.1H would be the area and center point of the Berliner Fernsehturm with sufficient accuracy u33db + 1 = u33dc http://geohash.2ch.to/u33dc1h This example uses base 32 to comply with geohash but other more error robust encodings and variations (e.g. second part as base ten numerals) could be used as well. Add optional check sum part based on unshortened geohash if needed to verify integrity. Variants: since geohash, covers a varying amount of area depending on the latitude. A similar approach could be realized on a UTM based grid like MGRS if a precise area is required (https://en.wikipedia.org/wiki/MGRS) Additions: If the site, browser or system sets a default locale the first parts of a code (normally country level) could be omitted as well. Possible lookup authorities could be Wikidata, OSM, geonames, compatibility with ISO 3166-2 might be desirable.

This is my first one, like, ever, so it's a bit crap. However, it can only get better.

Drawing SoHo from memory.