This repository contains introductory notebooks for principal component analysis.
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Updated
Nov 17, 2022 - Jupyter Notebook
This repository contains introductory notebooks for principal component analysis.
List of Kaggle notebooks
Notebook to Perform Market Segmentation using K-means clustering, PCA, and Auto-encoders.
Notebooks on PCA (Principal Component Analysis).
A simple Jupyter notebook to visualize data in latent space using dimensionality reduction techniques.
Pan sharpening algorithms run on Jupyter Notebook: Brovey, weighted Brovey, PCA, and simple mean.
Mathematics for Machine Learning Notebooks and files
Repository for the Wine K-Means Clustering Kaggle notebook.
Various Template Notebooks for Deploying ML models with Amazon Sagemaker
Jupyter notebooks implementing Machine Learning algorithms in Scikit-learn and Python
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
This Python notebook demonstrates the application of Support Vector Machines (SVM) for classification tasks on the MNIST dataset. The notebook covers data preprocessing, hyperparameter tuning, and dimensionality reduction using PCA.
This repository contains a highly detailed notebook that serves as an assignment for the Data Analysis course at the Higher School of Computer Science ESI. The notebook covers the topic of PCA (Principal Component Analysis), providing thorough explanations and examples.
This repository contains a Jupyter Notebook that implements PCA (Principal Component Analysis) from scratch for facial recognition. It demonstrates the steps involved in PCA, including eigenface computation and accuracy comparisons for different components.
This Jupyter Notebook demonstrates the implementation of a K-Nearest Neighbors (KNN) algorithm using the concept of nearest neighbors without using direct classifiers. It also includes exploratory data analysis (EDA) and comparison of three classifiers.
Jupyter notebook using machine learning techniques to explore the complex drivers of modern slavery. Models from a research paper are replicated and evaluated . Actions also include filling missing data, training regression models, and analyzing feature importance.
The purpose of this project is to promote understanding -- my own and others' -- of fundamental data science and machine learning concepts and tools. It currently consists of one notebook that classifies fruit types based on weight, volume, and image data.
Face-Recognition Notebook & Demo using principal component analysis.
Final year project experimenting with clustering and topological data analysis of scRNA-seq data using Python and R across two Jupyter notebooks
Principal Component Analysis Example Notebook.
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