👷 Predicting the price of construction machines with ML

Credit card users segmentation with multiple methods: K-means, agglomerative_clustering

A GUI tool in Python to calculate water quality parameters

Project for car valuation from otomoto.pl

Silver medal solution for the "M5 Forecasting - Accuracy" Kaggle competition

Python project that utilizes Supervised Classification methods to detect anomalies in iTrust Secure Water Treatment (SWaT) Dataset.

Gradient boosted classification and regression trees in python

General boosting framework for any regression estimator

This repository is a solution to Analytics Vidhya Practice problem called 'Predicting Loan Status Approval'

Uses ML models to predict the type of crime that is likely to happen at a given time of the day at a specific location on a college campus.

Using the Sklearn classifiers: Naive Bayes, Random Forest, Adaboost, Gradient Boost, Logistic Regression and Decision Tree good success rates are observed in a very simple manner. In this work sensitivity is also considered. Treating each record individually, differences are found in the results for each record depending on the model used, which…

Taking into account that the accuracy of statistical results depend on the accuracy of the input data, not only on the algorithm, a Hastie file has been created in which all the records have the correct class assigned and tests of hit rates and sensitivity have been carried out

AI model to predict computationally expensive vertex-wise descriptors like the local gyrification index from the mesh structure.

Learning to rank is an algorithmic technique employing machine learning models to solve ranking problems

House price prediction using ensemble methods including AdaBoost, Gradient Boost and XGBoost.

Testing word classification by novel machine learning methods on a complaints dataset

Researching how old classical machine learning – gradient boosting – and new modern deep learning algos cope with tabular data

This repository contains machine learning model for predicting medical insurance cost using gradient boost algorithm.

This project employs ensemble learning methods to forecast cybercrime rates, utilizing datasets with population, internet subscriptions, and crime incidents. By analyzing trends and employing metrics like R2 Score and Mean Squared Error, it aims to enhance prediction accuracy and provide insights for effective prevention strategies.