Habitat Suitability Modeling with Random Forest Classification in Google Earth Engine

Used the Global Terrorism Database to Explore Features of Suicide Bombings

Predict your diseases based on the symptoms provided And Image Processing technique is used to predict the skin cancer

All my Machine Learning Projects from A to Z in (Python & R)

This repository contains the implementation of a machine learning project aimed at predicting the stage of cirrhosis based on clinical features.

A Data Mining Streamlit Application for Astrophysical Prediction using Random Forest Classification in Python

Audio Pattern Recognition project - Music Genres Classification

Driver Analysis with Factors and Forests: An Automated Data Science Tool using Python

If you miss payments or you don't pay the right amount, your creditor may send you a default notice, also known as a notice of default. If the default is applied it'll be recorded in your credit file and can affect your credit rating. An account defaults when you break the terms of the credit agreement.

Full machine learning practical with R.

Full machine learning practical with Python.

Three-dimensional scatter plot visualization of dataset and predict of different machine learning models on diabetes data

Random forests is a supervised learning algorithm. It can be used both for classification and regression. It is also the most flexible and easy to use algorithm. A forest is comprised of trees. It is said that the more trees it has, the more robust a forest is. Random forests creates decision trees on randomly selected data samples, gets prediction from each tree and selects the best solution by means of voting. It also provides a pretty good indicator of the feature importance.

No description provided.

This project basically aims to provide a visual representation and comparative analysis of close price data related to different company ticker. It involves an interactive dashboard for users to display analysis and prediction of stocks data by using LSTM + XG-Boost model

Presentation in Graduate Research Symposium event at Stockton University

Assignments in Machine Learning class at Stockton University

Data analysis project on Digital Addiction for master thesis

Build and evaluate classification model using PySpark 3.0.1 library.

random forest classification (with hyperparameter tuning) on heart disease dataset.

AI-NIDS is an advanced student-built cybersecurity project that uses a Random Forest ML model to detect malicious network traffic from the CIC-IDS dataset. It combines real-time attack simulation, visual analytics, and Explainable AI via Groq LLM to deliver SOC-style, human-readable threat explanations—like a virtual security analyst in action.

Sentiment Analysis of Movies Dataset

MACHINE LEARNING ALGORITHMS

Machine Learning Mastery is a comprehensive repository designed to teach machine learning with Python. It covers essential techniques from data preprocessing to advanced methods in classification, regression, and clustering, catering to beginners and advanced learners alike.

Machine Learning model to predict Red Wine Quality using Random Forest Classifier

Machine learning algorithms implemented in python. Some are implemented in R. Algorithms include XGBoost, Convolutional Neural Network, Recursive Neural Network, Support Vector Machine, K-nearest neighbors, Naive Bayes, Natural Language Processing

In this project the data is been used from UCI Machinery Repository. Main aim of this project is to predict telling tumor of each patient is Benign (class – 2) or Malignant (class – 4) the models used are – Decision tree Classification, Logistic Regression, K-Nearest Neighbors, SVM, Kernel SVM, Naïve-Bayes and Random Forest Classification.

Implemented and compared Random Forest, Decision Tree, KNN, SVM, and Logistic Regression outcomes with a confusion matrix. Concluded that Random Forest achieved the highest accuracy of 85% to predict the loan status for investors.

Predicted the disease using the symptoms observed in the patients.

Prediction of students' dropout using classification models. Data visualisation, feature selection, dimensionality reduction, model selection and interpretation, parameters tuning.