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

There is war is going on between two countries submarine of the country is going under the water to another country and enemy country planted some mines in the oceans mine are nothing but explosive that explodes when some object comes in contact with it and there can be rocks in the ocean so submarine needs to predict whether it is crossing mine…

Finding High redshift quasars in large survey data using random forests. Code of Wenzl et al. 2021

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

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

In this project, based on the historical data of customers, who has applied for loans, we will identify whether loan should be approved or not. This dataset belongs to Lending Club.

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

Improving a Machine Learning Model

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

We have developed a Hybrid Model which consists of Random Forest, K-Nearest Neighbors, and Artificial Neural Network Algorithms using the Majority Voting Approach for detecting frauds in Credit Cards effectively and efficiently🙂.

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.

This is a script that reads in Landsat-8 data, Esri Sentinel-2 10m land cover time series data and train a random forest classification algorithm to estimate fractional built cover at 30m scale. The trained model can be used to produce fractional land cover for other regions.

Random forest ML model for phishing website detection

This project aims to build a machine learning model using K-Nearest Neighbor, LogisticRegression, RandomForestClassifier to classify whether or not a person has heart disease based upon his medical attributes. (accuracy achieved : 88.52%)

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

A notebook aimed at predicting and improving water safety by analyzing contaminants and pollution levels in water sources, enhancing public health and ensuring access to clean drinking water.

In this project, I worked on a classification problem using an imbalanced dataset which predicts ecological footprints. The aim of the project was not necessarily to build a classification model but to investigate the different methods of correcting an imbalanced dataset in order not to build a biased classifier

model is able to predict the type of Ultra or Smart plan that users need.

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.