svm

Build a lightweight, fast, and feature-rich Python editor optimized for low-end hardware, with tabs, dark mode, and smart indentation.

This Folder consists of Python Programming for Machine Learning Projects, which was used in real time LAB performance.

Browse a curated, ranked list of open-source algorithmic trading bots, frameworks, and resources updated on a regular schedule.

Predict survival on the Titanic using machine learning models trained on passenger data highlighting key survival factors like age and class.

🌟 Explore machine learning algorithms and deep learning architectures with hands-on examples in TensorFlow and Keras, from basics to advanced implementations.

🐙 Lung cancer prediction with logistic regression using clinical features and feature selection, with robust evaluation metrics. Code and results live in the analysis notebook.

Predict diabetes risk using a Support Vector Machine model. Achieve high accuracy with medical indicators. Join us in this GitHub project! 🩺🤖

Classify ozone days using SVM models with 7 years of data. Explore Gaussian RBF and Linear Kernel methods for accurate predictions. 🌍📊

Optimize lightweight deep learning models for real-time human activity recognition on resource-constrained edge devices using smartphone sensor data.

Analyze and predict remote work burnout using synthetic data to support occupational health and improve employee well-being.

Provide a flexible, engine-agnostic multiplayer backend platform with realtime Rust data handling and secure WebSocket communication.

Run time series foundation models locally on Apple Silicon with a Swift SDK supporting HuggingFace and multiple model architectures.

📊 Predict student success in higher education using a machine learning model to enhance academic outcomes and support informed decision-making.

🔋 Analyze smartphone battery consumption data over 72 hours, capturing power usage with user activities for better power management insights.

🔍 Predict customer churn with a machine learning system that identifies at-risk clients and recommends tailored retention strategies for better ROI.

📡 Implement advanced RF modulation classification using MATLAB with ML techniques, featuring BPSK, QPSK, 8PSK, and 16QAM for effective signal processing.

A machine learning project to predict the winner of NFL games

Classificação de doença cardíaca com SVM, GridSearchCV e análise exploratória de dados

ابزار جمع ‌آوری و تبدیل کانفیگ‌های وی‌پی‌ان به پروتکل‌های مختلف ، توسعه‌یافته توسط سینا

Seven dry-bean varieties from real morphology features with duplicate-aware splits, calibrated SVM and held-out error analysis