This pipeline provides a way to perform pharmaceutical compounds virtual screening using similarity-based analysis, ligand-based and structure-based techniques. The pipeline contains a collections of modules to perform a variety of analysis.

The following repository contains source code for a 100 Day personal machine learning coding challenge. It contains projects that I do as a part of my learning

Modello Random Forest per la creazione di una mappa di suscettibilità da frane superficiali // // Tesi di Laurea Magistrale in Scienze della Terra (Geologia Applicata) - Università degli Studi di Milano

Machine Learning Software that predicts planets based on their distance from the sun, number of satellites and various properties

Exploring the effectiveness of Random Forests in developing intraday trading strategies using existing technical indicators for the Bitcoin-US Dollar (BTC-USD) pair.

Análise de dados sobre cotas de gênero e seu impacto nas eleições e proposições legislativas da Câmara dos Deputados Federais entre 1934 e 2021. Parte do TCC da pós-graduação em Inteligência Artificial e Aprendizado de Máquina na @pucminas

My Python learning experience 📚🖥📳📴💻🖱✏

A machine learning pipeline for classifying cybersecurity incidents as True Positive(TP), Benign Positive(BP), or False Positive(FP) using the Microsoft GUIDE dataset. Features advanced preprocessing, XGBoost optimization, SMOTE, SHAP analysis, and deployment-ready models. Tools: Python, scikit-learn, XGBoost, LightGBM, SHAP and imbalanced-learn

This project develops an activity recognition model for a mobile fitness app using statistical analysis and machine learning. By processing smartphone sensor data, it extracts features to train models that accurately recognize user activities.

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The Aim of this project is used to identify whether a new transaction is fraudulent or not.

Identification of fake currency is a challenging problem for all. Fake banknotes are becoming more and more identical to the real ones. In this Fake Currency Detection model, I have used multiple machine learning algorithms to determine fake or real banknotes and was able to achieve more than 90% accuracy.

It is a full stack ml app , compared multiple ml models(KNeighborsClassifier, LogisticRegression, RandomForestClassifier ) , later deploy the best model using flask , and the frontend is created with react.js

The Heart Disease Predictor is a Python project developed to classify whether an individual has heart disease based on specific input parameters. It utilizes the scikit-learn and NumPy libraries for implementation.

Machine learning model Visualizer in web using streamlit

In this project I intend to predict customer churn on bank data.

Predicting transaction fraud using classification problems such as Guardian Boosting as well as user interfaces using Streamlite, Accuracy: 98% AUC-ROC

Evaluation of the Models (Regression and Classification)

Build a Machine Learning model that is able to classify whether or not a person believes in climate change, based on their novel tweet data

Ai Doctor Assistant is Ai Programmed web site to Detect health related Problems using Ai , This is My final year Project

Repository for the ENSF 612 final project.

This fraud detection system is powered by a Machine Learning model, which accurately identifies whether an initiated transaction is fraudulent.

Machine learning project for predicting customer churn based on user behavior, contract type, and monthly charges. Includes preprocessing, model training, and evaluation. /// Проект по предсказанию оттока клиентов телеком-компании на основе их контрактов, активности и платежей.

This project predicts credit card defaults using machine learning. The XGBoost model, optimized with under-sampling, was the best performer, effectively handling class imbalance and achieving strong recall and accuracy.

A parser for scikit-learn exported text models to execute in the Java runtime.

Train and apply a RandomForestClassifier on large images

ML models for HR classification problem. For more information please visit the link: https://datahack.analyticsvidhya.com/contest/wns-analytics-hackathon-2018-1/

Final Project Of Computational Intelligence - Fall 2021 - LightGBM, RandomForest and StackingClassifier

EcoWaste AI uses MobileNetV2 to classify waste as organic or recyclable and a RandomForest model to estimate CO₂ savings based on item weight. It helps users make better disposal choices by providing predictions, confidence scores, carbon-impact estimates, and simple eco-tips through an easy interactive interface.