Analyzing the vast data of learners can uncover patterns in their professional backgrounds and preferences. Allowing Scaler to make tailored content recommendations and provide specialized mentorship.

MHCCL: Masked Hierarchical Cluster-wise Contrastive Learning for Multivariate Time Series - a PyTorch Version (AAAI-2023)

Codes for Practical experiments of Data Warehousing and Mining (Semester V - Computer Engineering - Mumbai University)

Conducted a comprehensive clustering analysis to categorize beers based on features such as Astringency, Alcohol content, Bitterness, Sourness, and more. Utilized k-medoids and hierarchical agglomerative clustering algorithms to achieve this classification. Tech: Python (numpy, pandas, seaborn, matplotlib, sklearn, scipy)

Regression, Classification and Clustering

Grouping of drinks according to their nutritional values, making it easier to categorize them in a future catalog, increasing organization and facilitating the search depending on individual preferences

This module allows users to analyze k-means & hierarchical clustering, and visualize results of Principal Component, Correspondence Analysis, Discriminant analysis, Decision tree, Multidimensional scaling, Multiple Factor Analysis, Machine learning, and Prophet analysis.

Six portfolio optimization strategies were considered, plus one benchmark, across 3 scenarios,. We considered methods relying both in ML and common statistical procedures; and we run an out-of-sample back-test for each strategy, for every scenario.

A python module to draw a circular dendrogram

Demonstrating the application of clustering algorithms with various parameter sets and feature combinations, along with necessary data preprocessing tasks in Python.

Python programming labs done throughout the course CSC406 - Artificial Intelligence

Code for the paper "Guiding sentiment analysis with hierarchical text clustering: Analyzing the German X/Twitter conversation on face masks in the 2020 COVID-19 pandemic"

Python library for portfolio optimization built on top of scikit-learn

Mall Customer Segmentation based on Annual Income and Spending Score.

Hierarchical divisive clustering algorithm execution, visualization and Interactive visualization.

1. Digital Marketing Advertisement Data Segmentation using clustering techniques. 2. Identify Optimum Principal Components that explains the most variance in the Primary Census data.

Scripts, figures, and working notes for the participation in ImageCLEFmedical GANs task, part of the 14th CLEF Conference, 2023.

This repository contains the code and analysis for a research project aimed at enhancing social media impact for food safety security organizations. The project focuses on understanding text tone preferences based on user demographics using machine learning techniques.

Machine learning models for enhanced fraud detection in e-commerce transactions, exploring feature engineering, distance prediction, and clustering analysis.

Unsupervised ML exercises and projects done at alx training