-diabetis_prediction_ml

abiroumghar

bigdata-kmeans-explorations

Explorations of k-means clustering for Big Data, featuring sequential, streaming, and distributed implementations tailored for scalability and efficiency.

chemnlp-materialsanalysis

🔍 ChemNLP-MaterialsAnalysis: Enhancing materials chemistry research with advanced NLP. Key features: 📚 Integrates with arXiv & PubChem datasets 🤖 Applies BERT embeddings & ML clustering (KMeans, t-SNE, UMAP, PCA) 🔄 Uses pickle for efficient data handling 🌐 Aims for deeper insights & accelerated discovery in materials science.

cornacpoweredrecommender

This project presents a movie recommendation system utilizing the AutoRec model with Cornac, aimed at delivering personalized movie recommendations based on user preferences.

covid-watcher-project

dimensionality-reduction-analysis

A scholarly Python endeavor examining PCA, TSNE, UMAP impacts on PubMed data clustering 📈, with BBC News/Web Content as optional datasets. It scrutinizes dimensionality reduction's influence on K-means cluster fidelity, aiming for robust analytical insights .

energyclusteranalytics

EnergyClusterAnalytics 🌟 is an academic project that showcases the power of unsupervised learning in analyzing residential electricity consumption 📈. Utilizing PCA, clustering, and Binary Segmentation Search , it identifies unique consumption patterns to inform energy management strategies .

energyforecast-ml

Embark on a time series journey exploring electricity usage patterns with XGBoost and Random Forest. Using UC Irvine's data plus weather and holiday insights, this project aims to forecast demand and enhance energy planning. Dive into our predictive analytics adventure for smarter energy management 🌍💡

exam-entry-form

html code

gcp-etl-toolkit

This repo represent a Jupyter notebooks facilitating ETL processes on Google Cloud Platform, with practical examples and a ready-to-use dataset for easy adaptation and testing in any GCP environment.

mnist-semisupervised-pseudolabels

A deep dive into optimizing MNIST digit predictions using semi-supervised learning with just 100 labeled samples. Utilizes pseudo-labels to bridge the gap between labeled and unlabeled data, leveraging TensorFlow for model implementation. A compact showcase of enhancing model accuracy with minimal labeled data.

object-detection

predict

prediction_puissance_optimale

predictionpuissanceelectriqueaerop

prj-prediction-price

project1

projet_final

repo_project_github

supervisedlearning-bankingandrelationalanalysis

This repository explores the application of supervised learning techniques to two key domains: banking credit data and relational datasets (Cora, CiteSeer, PubMed). It aims to tackle real-world challenges through a comparative analysis of methods such as Naive Bayes, KNN, SVM, and more, all implemented in Python.