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

CSE601 Course Projects - Fall 2017

A novel incremental hierarchical clustering algorithm (KDD 22)

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.

Machine Learning with Python, Numpy & SciKitLearn

To sanitize and analyze the data to profile the sellers present and develop selection criteria to identify the best or most promising sellers in this dataset, that the Acquisitions team at any company should reach out to, and acquire. The model used Kmeans clustering and Heirarchical clustering to identify sellers targets for acquisition.

Which countries can be grouped together and have the highest need of help from the charity?