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

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

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.

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.

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

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

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

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

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