Project for real-time anomaly detection using Kafka and python
Machine Learning-powered car price prediction web app built with Flask, Scikit-learn, and feature engineering for real-time resale price estimation.
This is End to end project for potato disease detection using deep learning. I build cnn model to predict potato 3 classes early-blight, late-blight and healthy.
Features injected recurrent neural networks for short-term traffic speed prediction
A reproducible, leak-free machine learning benchmarking lab for regression model comparison, cross-validation, diagnostics, experiment tracking, and interactive Streamlit-based inference using the California Housing dataset.
🤖 AI-powered smart lighting system with 85-96% occupancy prediction, 30-50% energy savings, weather integration, and real-time optimization. Built with React, Flask, and Machine Learning.
AI-powered phishing URL detection system using Machine Learning and Streamlit that analyzes website URLs and predicts whether they are safe or phishing.
SwiftLearning is iOS side of scikit-learning
AI-Powered Audio Deepfake Detection System built with Python, Flask, librosa, scikit-learn and SQLite
Churn prediction pipeline with Python and scikit-learn. Focus on data processing, modeling and analysis.
Machine learning project. Learn to deploy and serve a trained model, save and load model artifacts, wrap a model in a FastAPI service, and handle inputs, validation, and prediction requests.
“An independent predictive sports analytics model designed to classify table tennis match dynamics and evaluate player tactical features"
IBM stock price forecasting using Random Forest, LSTM (4-layer), and ARIMA models with Python
This project builds multiple ML classification models to predict visa approval outcomes using the EasyVisa dataset. The repository includes the full notebook, documentation, and results.
Empirical probability calibration study on UCI Bank Marketing with repeated holdouts, sensitivity analysis, frozen data provenance, and reproducible research artifacts.
A machine learning project that predicts wine quality (good vs bad) using physicochemical properties. Built using Python, Pandas, Scikit-learn, with models like Random Forest and SVM, including data preprocessing, feature engineering, scaling, and hyperparameter tuning. machine-learning classification python scikit-learn data-science ml-project
Industrial Distribution & Operations Intelligence Platform
End-to-end speech emotion recognition pipeline using Librosa & Scikit-Learn with 180-D acoustic feature extraction, split-window analysis, and an active learning retraining loop.
Análise exploratória (em inglês) do dataset Heart Disease UCI. Foram analisados fatores que podem estar associados a doenças cardíacas e foi criado um modelo para classificar a presença de doenças cardíacas em novos pacientes.
𝐋𝐢𝐯𝐞𝐫 𝐂𝐢𝐫𝐫𝐡𝐨𝐬𝐢𝐬 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
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