{"repo":"Sara12-2/Unsupervised_Machine_learning_projects","free":true,"listed":false,"github":"https://github.com/Sara12-2/Unsupervised_Machine_learning_projects","clone":"git clone https://github.com/Sara12-2/Unsupervised_Machine_learning_projects.git","description":"This repository contains multiple unsupervised machine learning projects including DBSCAN and K-Means clustering for real-world applications like earthquake epicenter detection, image compression, and social media user segmentation.","language":"Jupyter Notebook","stars":10,"topics":["data-preprocessing","data-visualization","dbscan-clustering","k-means-clustering","matplotlib","numpy","pandas","pillow","scikit-learn","streamlit"],"license":null,"category":"machine-learning","readme_excerpt":"🌍 Machine Learning Clustering Projects Collection This repository contains multiple Unsupervised Machine Learning projects focused on clustering, pattern detection, and data visualization using DBSCAN and K-Means algorithms. These projects demonstrate how ML can uncover hidden structures in real-world datasets. --- 📊 Project 1: Earthquake Epicenter Detection using DBSCAN (Streamlit App) ⚡ Overview A Streamlit application that detects earthquake epicenters using DBSCAN clustering on latitude and longitude data. 🚀 Features - Upload earthquake dataset (CSV) - DBSCAN clustering for epicenter detection - Adjustable parameters (eps, min samples) - Cluster visualization (2D scatter plot) - Cluster summary insights 📂 Dataset Format Required columns: - latitude - longitude - magnitude (optional) 🛠️ Tech Stack - Streamlit - Scikit-learn (DBSCAN) - Pandas - Matplotlib --- 🖼 Project 2: Image Compression using K-Means Clustering (Streamlit App) ⚡ Overview This project compresses images using K-Means clustering by reducing the number of colors while preserving image quality. 🚀 Features - Upload images (jpg, png, jpeg) - Select number of clusters (K) - Image compression using K-Means - Before vs After comparison - Download compressed image 🛠️ Tech Stack - Streamlit - NumPy - Scikit-learn - Pillow --- 📱 Project 3: Instagram User Clustering using DBSCAN ⚡ Overview This project clusters Instagram users based on Followers and Likes using DBSCAN to identify patterns and outliers. 🚀 Fea","default_branch":null,"files":null,"tree":[],"storefront":"/r/Sara12-2","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Sara12-2/Unsupervised_Machine_learning_projects/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}