AUST_CSE 4.2 Pattern Recognition Lab Codes and Experiments

This notebook is about creating a 2D dataset and using unsupervised machine learning algorithms like kmeans, kmeans++, and Agglomerative Hierarchical clustering methods to classify data points, and finally comparing the results.

Mall Customer Segmentation Data

AgglomerativeClustering for Responden Data Answer

Comparison of various distance metrics used in clustering techniques for unsupervised learning

Repo contains my personal Machine Learning projects with emphasis on explanability and Insights that are relevant for various stake holders in a business.

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

This repository implements customer segmentation techniques to analyze credit card user behavior and identify distinct customer groups. By leveraging Python libraries like pandas, Scipy and scikit-learn.

The task is to cluster NBA players based on the players' per-game average performance in the 2018-2019 season. The goal is to achieve the best performance by exploring several different clustering methods, feature engineering, distance metrics, and evaluation measures.

Performed KMeans, Agglomerative, Divisive, DBSCAN clustering on FIFA dataset along with outlier detection and cluster analysis

I used Agglomerative Hierarchical Clustering and K-Means Clustering. The goal of this project is to find the best way to characterize the variety of consumers that a wholesale distributor deals with

Coronavirus tweets NLP - Text Classification mini-project work for Data Science course, FCSE, Skopje

COVID-19 Survival Rate Prediction and Analysis Using Medical History of Patients in the US

In this project, unsupervised learning methods, particularly clustering, are employed to determine the optimal algorithm for predicting whether a company has achieved net profit or incurred a net loss.

CIA Country Analysis and Clustering

This repository is a collection of programs implemented as part of Machine Learning Laboratory course at JSS Science And Technology University(SJCE).

Classifying and clustering different types of leaves based on their features and texture

Simple k-means clustering and agglomerative clustering implementation

A clustering problem to cluster a dataset of stars done in python with scikit-learn