Source files for "Fun Q: A Functional Introduction to Machine Learning in Q"

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

A group of small robots capable of organizing themselves in any given structure using OpenCV and Clustering.

A Repository Maintaining My Summer Internship Work At Datalogy As A Data Science Intern Working On Customer Segmentation Models Using Heirarchical Clustering, K-Means Clustering And Identifying Loyal Customers Based On Creation Of Recency, Frequence, Monetary (RFM) Matrix.

A Shape Finder using Corner-Harris and Heirarchical Clustering

5 Projects based on Unsupervised learning

A novel incremental hierarchical clustering algorithm (KDD 22)

CSE601 Course Projects - Fall 2017

A hub that contains notebooks that implement Regression models, illustrates LR via Gradient Descent, compares K-means vs Spectral vs Hierarchical, compares PCA vs t-SNE

Unlock personalized content recommendations on Netflix with my cutting-edge ML project. Say goodbye to aimless scrolling and elevate your binge-watching experience with our user-centric content-based recommender system.

Complete package for all Data Science models using R. Starting form Preprocessing, Data Manipulation, Feature Engineering, Model Building, and Model Validation.

Customer Segmentation using kmeans, hc, dbscan and gmm

Performed PCA on wine dataset and applied clustering algorithms

Streamlit App for The Analysis of team tactics and player performances

This repository contains all the projects carried out to understand and experiment on Machine Learning using Python. Projects scripts are created to build model on classification , clustering and regression machine learning models for future predictions.

Performed clustering analysis on OnSports player data for the English Premier League. The clustering analysis successfully identified 4 unique player clusters and uncovered valuable business recommendations by identifying trends and patterns in the EDA, meeting the objective of determining player pricing next season.

Repository for Customer Segment Analysis using Python & Shiny App Dashboard

Perform Principal component analysis and perform clustering using first 3 principal component scores