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Julia wrapper of the python library CatBoost for boosted decision trees

Using Pima Indians diabetes data set to predict whether a patient has diabetes or not based upon patient’s lab test result variables like Glucose, Blood Pressure, etc. using CART decision tree algorithm and K-Nearest Model achieving 76% accuracy. Python-Scikit Learn, SciPy, Pandas, MatPlotLib.

Supervised Machine Learning Classification Algorithms using Python and R (Logistic Regression, Decision Tree, Random Forest, SVM)

This project is on twitter sentimental analysis by combining lexicon based and machine learning approaches. A supervised lexicon-based approach for extracting sentiments from tweets was implemented. Various supervised machine learning approaches were tested using scikit-learn libraries in python ... Read more

🎬Trending Movies Data Analysis and Popularity Prediction – A data analytics project using Python, MySQL, Power BI, and Decision Tree Classification to analyse Netflix movies and shows, build interactive dashboards, and predict popularity categories under a calming moonlit theme.

The aim of this machine learning application is that it can use to predict divorce. The dataset is the Divorce Prediction dataset by Larxel from the website: Kaggle. There are few models tested when developing the application, including Tree-based models like Decision trees, Random forest, XGBoos... Read more

Use random forest, svm, decision tree, naive bayes to classify the wine quality in Python

A Python implementation of decision trees, compatible with sklearn.

包含线性回归、逻辑回归、决策树、随机森林、支持向量机与神经网络识别手写数字等机器学习基本模型的python实现，数据，以及详细的中文注释 "Python implementations of introductory machine learning models, including linear regression, logistic regression, decision tree, random forest, support vector machine, along with datasets and detailed comments in Chinese

Understanding gender differences in professional European football through Machine Learning interpretability and match actions data. This repository contains the full data pipeline implemented for the study *Understanding gender differences in professional European football through Machine Learni... Read more