Using radial velocity data to identify exoplanet companions
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Updated
Mar 6, 2023 - Python
Using radial velocity data to identify exoplanet companions
This is a project that was completed while taking the Udemy course - Python for Machine Learning & Data Science Masterclass.
R based data analytics on German Cars Market
Custom built Decision Tree + Boosted Trees + KernelPLS in python
Determining financial factors affecting the health of an individual
Compare different classification models to predict the accuracy of identifying credit card transactions as normal or fraud
CS760: Machine Learning
Neural Networks, Ada Boost, Random Forest, KNN, BoostedForest
Classifying the survival of passengers aboard the Titanic via the use of various Machine Learning algorithms.
Analysis of 2015 Mexican Ministry of Health administrative data
implement machine learning algorithm from scratch
Classification prediction model
This is the repository for my R project on modeling historical weather data in Santa Barbara.
Classification Trees, Random Forest, Boosting | Columbia Business School
Regression model for predicting house prices of residential homes in Ames, Iowa. Dataset contains 79 explanatory variables. Project includes key topics such as dataset cleaning, feature selection/engineering, EDA and applying grid search to find the best model.
Projects using tree methods (CART, Random Forests, Boosted Trees)
Protein classification with deep learning and boosted trees using topological features
This repository implements the basic machine learning classifiers for the problem of Yelp reviews classification. We assume the problem to be a binary classification problem. The models implemented are Naive Bayes, Logistic Regression, Support Vector Machine (linear), Decision Trees, Bagged Decision Trees, Random Fforests, and Boosted Decision T…
This project aims at developing, validating, and testing several classification statistical models that could predict whether or not an office room is occupied using several data features, namely temperature (◦C), light (lx), humidity (%), CO2 (ppm), and a humidity ratio. The data is modeled using classification techniques i.e. Logistic regressi…
We downloaded and processed ten years of historic log data from the Tor project. Then we used boosted regression trees and generalized linear models to predict malicious exit nodes.
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