AutoEncoder model for finding N similar images to a given input image and partitioning the entire image dataset into K groups.

Task 2 from the Sparks Foundation GRIP internship

This project analyses different clustering methods over three different datasets

Unsupervised machine learning, K-Means clustering, PCA algorithm to analyze a database of crypto.

The project uses KMeans clustering on the Global Superstore dataset to categorize customers based on their buying habits, aiming to help retailers make better business decisions by tailoring their marketing strategies and improving their inventory management.

Unsupervised Machine Learning-Netflix Recommender recommends Netflix movies and TV shows based on a user's favorite movie or TV show. It uses a a K-Means Clustering model to make these recommendations. These models use information about movies and TV shows such as their plot descriptions and genres to make suggestions.

Course projects of Applied Data Science Capstone by IBM on Coursera

Clustered bank's clients data using K-Means to launch targeted marketing campaigns tailored for their specific needs and behaviors.

Task is to predict optimum number of clusters using elbow method.

Prediction of optimum number of clusters using K-Means Clustering on Iris dataset

This project focused on applying machine learning to build a clustering model to segment and analyze customer characteristics in the airline industry based on LRFMC scores using K-Means and suggest business strategy recommendations based on the results.

Content: Unsupervised ML, Clustering, Customer Segmentation, WCSS, elbow method

Beer data clustering and pricing, evidence based pricing with Random Forest.

📉 Clustering of HTTP responses using k-means++ and the elbow method

Using Spotify data to create a recommendation system for The Beatles

The project creates a robust song recommendation system using K-means clustering with Spotify data. By grouping songs based on musical attributes like danceability, energy, and acousticness, personalized recommendations will be generated, enhancing user satisfaction and engagement in music discovery.

OptimalCluster is the Python implementation of various algorithms to find the optimal number of clusters. The algorithms include elbow, elbow-k_factor, silhouette, gap statistics, gap statistics with standard error, and gap statistics without log. Various types of visualizations are also supported.