Codes for Practical experiments of Data Warehousing and Mining (Semester V - Computer Engineering - Mumbai University)
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Updated
Jul 14, 2024 - Python
Codes for Practical experiments of Data Warehousing and Mining (Semester V - Computer Engineering - Mumbai University)
The dataset includes the following columns: Id, SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm, and Species. We will use the Sepal and Petal measurements to predict the optimum number of clusters using the KMeans algorithm.
a kmeans algorithm proposed improvement
This Python script demonstrates how to perform customer segmentation using K-Means clustering based on annual income and spending score.
K-means clustering is a popular unsupervised machine learning algorithm used for partitioning a dataset into a pre-defined number of clusters. The goal is to group similar data points together and discover underlying patterns or structures within the data.
The K-Means Visualizer is an interactive web application designed to help users understand and visualize the K-Means clustering algorithm. Through an intuitive interface, users can experiment with different numbers of data points and clusters, and observe how the algorithm iteratively updates centroids and assigns data points to clusters.
clustering of night time satellite images and depicting them by use of different colors
Vector quantization compression for images using k-means clustering algorithm.
K-means clustering algorithm using MapReduce.
Credit scoring and segmentation refer to the process of evaluating the creditworthiness of individuals or businesses and dividing them into distinct groups based on their credit profiles.
Implementation of k-means clustering algorithm from scratch.
Machine Learning Code Implementations in Python
This repository is a machine learning project entailing clustering of regions/districts based on crime types features. Application of k-means simplifies this clustering as you can easily tell districts with similar crime patterns, know regions of high risk due to the diversity of crimes committed.
Clustering Visualizer is a Web Application for visualizing popular Machine Learning Clustering Algorithms (K-Means, DBSCAN, Mean Shift, etc.).
Using various forms of Singular Value Decomposition(SVD) for a recommendation and prediction system
This project applies K-means algorithm to group cryptocurrencies based on 24-hour and 7-day price changes. It also investigates the impact of dimensionality reduction using PCA on clustering outcomes.
Hamming Network implementation using PCA implementation from scratch
Image Clustering Algorithm implemented in C++
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