Toolkit for Data Science & Statistics

In this project, I will be implementing Principal Component Analysis (PCA) from scratch on an ecological footprint consummation database for countries and a three-dimensional scale using a movie database. The goal of this project is to gain a deeper understanding of PCA and to demonstrate its capabilities in exploring complex datasets.

Implementation of PCA with KNN Clustering

This is a 16S amplicon analysis for visualizing microbiome data using QIIME, QIIME2R and Phyloseq. DNA was isolated fom both sediment cores and seabird fecal samples for this analysis.

Modeling Portfolio (Python based)

a PCA project demonstrated on the Iris dataset

RGB data processing pipeline including auto-white-balance based on principle component analysis (PCA).

Permutation Test for Principal Components Analysis

Apply modern, deep learning techniques for anomaly detection to identify network intrusions.

Northwestern Master of Science in Machine Learning and Data Science (formerly MSiA) | Winter 2023 | MSiA421 Data Mining

Insights and Analysis - Using Various Deep Learning Architectures on Image Classification Datasets

A cross compiled Scala.js port of JAMA for JVM, JavaScript, and Scala Native projects.

This Repository Contains R-Codes executed on various Datasets in RStudio. I Hope This Repository is very helpful for those who are Willing to build their Career in Data Science, Big Data.

Final Project for STA 135 with Dr. Xiucai Ding

Datamining concepts

import datasets, perform exploratory data analysis, scaling & different models such as linear or logistic regression, decision trees, random forests, K means, support vectors etc.

PCA implementation of on Iris Dataset

This Git repository contains a machine learning algorithm for DiabetesAI, which predicts the likelihood of type 2 diabetes. The algorithm takes input from various features such as glucose levels, BMI, and age, and uses a predictive model to generate a probability score for diabetes diagnosis.

This Git repository contains a machine learning algorithm for HeartDiseaseAI, which predicts the likelihood of heart disease. The algorithm takes input from various features such as blood pressure, cholesterol levels, and age, and uses a predictive model to generate a probability score for heart disease diagnosis.