Knn Libraries

9 Components & Libraries

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Knn Libraries

Python code for common Machine Learning Algorithms

Python codes for common Machine Learning Algorithms

A curated list of Best Artificial Intelligence Resources

Multi-Modal Database replacing MongoDB, Neo4J, and Elastic with 1 faster ACID solution, with NetworkX and Pandas interfaces, and bindings for C 99, C++ 17, Python 3, Java, GoLang 🗄️

Would you prefer to connect to a remote UStore server? UStore comes with an Apache Arrow Flight RPC interface! Function calls may look identical, but the underlying implementation can be addressing h…

Replacing MongoDB, Neo4J, and Elastic with 1 transactional database. Features: zero-copy semantics, swappable backends, bindings for C, C++, Python, Java, GoLang

UKV is more than a database. It is a "build your database" toolkit and an open standard for NoSQL potentially-transactional databases, defining zero-copy binary interfaces for "Create, Read, Update, …

Data Science & Machine Learning projects and tutorials in python from beginner to advanced level.

Solve soduko in a image based on OpenCV and Python

Get numbers in the image Recognise the numbers (Train a knn here) Generate soduko and solve it Display the output

Scikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.

Sequentia is a Python package that provides various classification and regression algorithms for sequential data, including methods based on hidden Markov models and dynamic time warping. Some exam…

Trajectory-Analysis-and-Classification-in-Python-Pandas-and-Scikit-Learn

Formed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently trajectories.

A university project for the postgraduate class of Data Mining. We were given a train_set with geographical points paired with the time interval. Firstly, we cleaned the dataset and then we formed t…