Classification of Data Using Decision Tree and Random Forest

Assignments and Project from NJIT CS 675

The AdaBoost algorithm is an ensemble learning method that combines multiple weak learners (base estimators) to create a stronger predictive model.

Content: Root node, Decision node & Leaf nodes, Attribute Selection Measure (ASM), Feature Importance (Information Gain), Gini index

Contains KNN regression, Linear regression, KNN classification and Decision trees (using gini index, entropy & misclassification rate), all implemented from scratch.

Here are some projects based on static methods of information processing. There are many different methods that you can use to process your information, depending on your needs.

Implemented a Decision Tree from Scratch using binary univariate split, entropy, and information gain. Used Gini index and Pruning for performance improvement.

Supervised and unsupervised analysis

A decision tree built in python for my 4th year module CA4010, Data Mining

Using pyhthon with pandas and scikit-learn libraries to split, train, test and check the accuracy of DecisionTree model on top of diabetes dataset

Graphical user interface for azapy library - Finacial Portfolio Optimization Algorithms

A decision tree classification model based on bank marketing data

This repository contains ml model for applying decision tree on Iris dataset

These are coding assignments and projects for the CS 675 Machine Learning course.

This repository contains code to build various versions of Decision Tree Models (Gini, Entropy) for the Titanic Passenger Survival prediction from Kaggle Competitions. Pruning is also performed to reduce the Overfitting

2D Gini Index Implementation

This project focuses on implementing and analyzing the learning process in decision trees using Connect 4.

EDA project for GDP, Inflation, Income Per Capita, Income Inequality in Egypt (1961-2020)