A broad, easy and fast framework for machine/deep learning in Go.

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

A collection of 8 Applied Data Science projects.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn and TensorFlow-Keras

Scikit-learn compatible decision trees beyond those offered in scikit-learn

University assignments and sandbox projects connected to machine learning study.

Stakeholder-Specific Vulnerability Categorization

Some of the topics, algorithms and projects in Machine Learning & Deep Learning that I have worked on and become familiar with.

Diabetes is a medical disorder that affects how the body uses food for energy. When blood sugar levels rise, the pancreas releases insulin. If diabetes is not managed, blood sugar levels can rise, increasing the risk of heart attack and stroke. We used Python machine learning to forecast diabetes.

Data Mining and Machine Learning Group Project

A Machine Learning Model developed to detect and classify Hate Speech on Twitter built using streamlit

Julia implementation of Modal Decision Trees & Forests, for interpretable classification of spatial and temporal data. Long live Symbolic Learning!!

Spam Email Detector using Naive Bayes, C4.5 Decision Tree, and K Nearest Neighbor algorithms. We found Naive Bayes to be the most accurate at classifying spam.

Predicting Customer Churn using Data Mining and Machine Learning techniques - Logistic Regression, Decision Trees and Random Forests

Second step in decision tree building

Comprehensive exploration of decision tree regressors, including data cleaning, model building, and performance evaluation on various datasets.

Welcome to the Loan Approval Prediction project repository! This project focuses on predicting the approval of loan applications using various machine learning algorithms. By analysing applicant details and financial information, the model aims to assist financial institutions in making data-driven and reliable loan approval decisions.

Data Analysis Projects