Have you ever wondered who your most valuable customers are? This project, created for a software company, sought to identify those who stand out above the rest.

Simple decision tree in Python as practice

Predict the best classifier for the given data.

Implementation of various machine learning algorithms from scratch.

decision trees made easy

A project which shows the difference between the results obtained by a decision tree and those obtained by a random forest. Made with sklearn.

Homework assignments from the course, Data Mining. Topics covered include: data warehousing, Apriori - market basket analysis, decision trees, supervised learning algorithms, and unsupervised learning algorithms.

Implementing binary classification for id3, c45 and cart trees.

Implement the decision tree learning algorithm using Information gain heuristic & Variance impurity heuristic

This Project is an implementation of Decision Trees and Random Forests using bootstrap aggregation(Bagging).

Machine Learning prototype that assess the performance of 1 Decision Tree (no bagging) vs multiple Decision Trees (with bagging)

Project for Artificial Inteligent classes created with @SmoothCrimminal @Maciasty15 and @Kajgal.

Heart Disease Classification: This project focuses on utilizing machine learning algorithms to classify heart disease based on various features. The code performs data preprocessing, feature scaling, and applies different classification algorithms.

ML algorithms implementation only with Numpy

python 0基础入门机器学习

Python code for ML models from scratch

[NeurIPS23] Locally differentially Private Decision Tree

Decision Tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart-like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.

We have analysis their drug addiction behavior. From this research work we can identify drug addiction behavior also. We have used classification model to classified different types of drug addiction people problem.

A comprehensive set of programs demonstrating machine learning techniques have been made.