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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.
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.
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.
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.
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.
A.I. and Machine Learning notebooks: Using Supervised Learning, Unsupervised Learning, Re-enforcement Learning to solve Classification, Clustering and Regression problems
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