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

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 📚🖥📳📴💻🖱✏

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.

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

Machine learning model Visualizer in web using streamlit

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.

Repository for the ENSF 612 final project.

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

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

Natural Language Processing

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

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

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

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