An overview of my understanding of PCA for dimensionality reduction and Logistic Regression for model training and evaluation.

sciBASIC# is a kind of dialect language which is derive from the native VB.NET language, and written for the data scientist.

This is a repository with the assignments of IE678 Deep Learning course at University of Mannheim.

Deep Learning vs Tranditional ML methods for TB Drug Resistance prediction from Genomic data

Implementation of t-SNE with c++ (including the random walk version), visualization of the process of t-SNE on MNIST with python.

TV Shows recommendation system using cosine-similarity in ML with the help of TMDB dataset 🍿🎬.

ASAP : Automated Single-cell Analysis Pipeline

Welcome to my Classical Learning Projects repository, where I showcase my work in the fields of supervised and unsupervised learning. Here, you'll find code and datasets for various projects, such as classification and clustering tasks, implemented using popular algorithms like decision trees, neural networks, and k-means.

Predicting breast cancer survival using machine learning models

t-distributed stochastic neighborhood embedding (t-SNE) is a unsupervised non-linear dimensionality reduction and data visualization technique. The math behind t-SNE is quite complex but the idea is simple. It embeds the points from a higher dimension to a lower dimension trying to preserve the neighborhood of that point. I compared PCA and t-SN…

An evidence map of the climate change adaptation policy literature with an implementation of gender analysis.

"Welcome to my GitHub repository! Here, you'll find a hub of projects and code dedicated to the fascinating world of statistical analytics. From crunching numbers to extracting meaningful insights, join me on a journey through the realm of data-driven decision-making. Let's explore the power of statistics together!"

Minimal t-distributed stochastic neighbor embedding (t-SNE) implementation in JAX.

Guide for dimensionality reduction and clustering analysis.

Data Analysis of fuel efficiency

Data analytics case study of air pollution

Data analysis of marketing campaigns

The key dimensionality reduction techniques: ISOMAP, PCA (Principal Component Analysis), and t-SNE (t-Distributed Stochastic Neighbor Embedding) are presented and compared.

Source code for the project on the textual analysis of the US President/Secretary foreign travel