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.

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.

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.

Used unsupervised machine learning, PCA algorithm, and K-Means clustering to analyze and classify a cryptocurrency database.

clustering the Netflix movies and TV shows based on various factor like country wise most watched, genre, rating given by viewer etc. by applying clustering algorithms.

This project explores and analyzes financial data of a number of securities, applies Hierarchical and K-means clustering to group securities and create cluster profiles to develop personalized portfolios and investment strategies for clients

Detecting ideal clusters from imdb's movie dataset to segment using unsupervised learning

By aligning marketing efforts with customer preferences and desires, this approach promises to enhance market presence and drive substantial sales growth.

This project offers the K-means clustering algorithm to identify potential customers for the manager to target with calls, streamlining the process by focusing on specific customer groups.

Deploying clustering machine learning algorithms to segment survey respondents

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.