8-Puzzle solver implemented using search algorithms: DFS, BFS, A-Star (Manhattan and Euclidean heuristics) with GUI for user interactivity
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Updated
Nov 6, 2023 - Python
8-Puzzle solver implemented using search algorithms: DFS, BFS, A-Star (Manhattan and Euclidean heuristics) with GUI for user interactivity
Content-based recommendation engine using Python and Scikitlearn, using concepts of Cosine distance and Euclidean distance. Finally, by using IMDB 5000 movie dataset built a content-based recommendation engine using CountVectorize and Cosine similarity scores between movies.
A Banking Dataset is taken from Kaggle and applied 3 of the most used naive bayes formulas to get the accuracy
Agglomerative Hierarchical Clustering with Centroid method and Euclidean Distance
In this repo i have tried to explain how to calculate Euclidean Distance,manhattan distance, and Finally Calculating the Dissimilarity Matrix/Distance Matrix using python.
Parallel-Indexed multi-dimensional query engine for efficient vector search
MNIST_WITHOUT_SKLEARN: The MNIST_Scipy.py module is presented which, using the Scipy library, is applied to the recognition of handwritten characters contained in the MNIST file, achieving a similar hit rate than the module referenced at https://github.com/ablanco1950 / MNIST_KNN Although with a longer execution time.
Java Program Designed to recognise a digit from a 8*8 grid of numbers, Categorisation Task, Completed using 2 Solutions. Euclidean Distance and Self Organising Maps.
Pesticide Recommendations using case-based reasoning method to cure a pets by comparing old case - P
implements the elbow method to determine the optimal number of clusters (k) for a given dataset using the K-means clustering algorithm.
Network animation using Euclidean Distance with Python and Pygame
Classification Model of Potential Credit Card Customers
Machine Learning
In this project, we're trying to find similar garments given a query image from a dataset by comparing feature matrix.
Coding searchable notes for data science and machine learning
Simple classifiers based on Euclidean distance. Tests with two types of signals, using some input attributes such as skewness and kurtosis. Tested classifiers: KNN (K-Nearest Neighbors) and NPC (Nearest Prototype Classifier)
This code detects whether the text input is in German or English.
From Scratch
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