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A Python-powered ML toolkit featuring a Decision Tree builder and Naive Bayes classifier implemented from scratch. Supports attribute selection using Entropy (ID3) and Gini Index (CART), with custom metric calculations, recursive tree construction, and Graphviz-based visualization for decision boundaries and probabilistic classification.
It takes date and time to forcast weather information condition. A simple predection project of machine learning with Python. It is trained by a dataset taken from kaggle.
На основании сырых данных с параметрами добычи и очистки золотоносной руды построить прототип модели для предсказания коэффициента восстановления золота из золотоносной руды с лучшей метрикой sMAPE.
👨💻👨💻 Large amount of customer data has been clustered and establishing predictive model by decision Tree.
Machine Learning practice, Linear Regression, Multi-Linear Regression, Polynomial, Support Vector, Decission Tree, Random Forest.
На основании данных о поведении клиентов построить модель с максимально большим значением accuracy для задачи классификации, которая предложит подходящий тариф.
На основании данных о геологоразведке построить модели прогноза запасов нефтяных скважин для регионов, выбрать регион для разработки с приемлемым порогом риска безубыточности и наиболее перспективными ресурсами.
This repository contains an in-depth exploratory data analysis (EDA) and training of various machine learning models on the famous Titanic dataset. The models tested include Logistic Regression, Support Vector Machine (SVM), and Decision Tree. Additionally, pipelines for the Decision Tree and Logistic Regression models have been created and saved.
Exercise on Linear and Logistic Regressions, Random Forest, and Decision Trees. It involves three datasets, covering variable identification, data visualization, linear regression modeling, logistic regression for real estate feasibility, and creating a decision tree regressor for predictions.
Advanced projects made for the Data Mining course at the AGH UST in 2024
На основании данных о поведении клиентов построить модель с максимально большим значением F1 для задачи классификации, которая будет определять клиентов, склонных к оттоку.
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