Classification of Data Using Decision Tree and Random Forest

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

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

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

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

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

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)

Fast computation of the Gini coefficient

Udacity Project 1 - Investigate economic, inequality, & corruption data. This project sets out to find a relationship between the fastest-growing countries, corruption, & inequality.

Code created during a Machine Learning class at Columbia University

Uniswap Transaction Analysis Repository: Layer-1 and Layer-2 Transaction Measures