randomforest

🌲 broom helpers for decision tree methods (rpart, randomForest, and more!) 🌲

A Unified Framework for Random Forest Prediction Error Estimation

A machine learning project to predict NCAA Men's Basketball outcomes

Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification

An R package for Private Evaporative Cooling feature selection and classification with Relief-F and Random Forests

CLI tool for testing Office documents with macros using MaliciousMacroBot

PUBG- PlayerUnknown's BattleGrounds (PUBG)

Fraud detection using lgb, catboost, rf, etc

Recursive Partitioning for Structural Equation Models

Analytics Vidhya Hackathon

Prediction of Customer Churn using Spark Mllib

A predictive model that leverages classification models such as Random Forest and Naive Bayes to predict customer churn

A framework of python to do general machie learning using sk-learn, numpy, matplotlib and pandas

Detecting phishing website using machine learning

First capstone project on predicting expected arrival time of a container vessel from Shanghai to Ningbo, China using Automatic Identification System (AIS) data sent by ships.

Random Forest Binary Classification is applying on sample data in PySpark on Jupyter Notebook

Plot xgboost and randomforest trees

In this project i used many supervised learning algorithms available in scikit-learn, and also provided a method of evaluating, just how each model works and performs on a certain type of data.

Application of Random Forest algorithm for the classification of retinal scans using optical coherence tomography (OCT) metrics. Research was conducted with Columbia University Medical Center.

Tensorflow Lattice Regression for predicting house prices

Classification of various products into different categories is a very important task. Doing this classification, one can get various types of insights about the specific product. This also helps in doing product matching when you try and search a product on a eCommerce site.

Efficient Android Malware detection using Random - Protype of BA's final project (Efficient Android Malware Detection using RL) - Amit Moshe (@Amit223) & Inbar Roth (@Inbaroth) & Liad Bercovich (@liadber)

Simple regression problem where by pre-loaded data I have shown different regression algorithm and their performance on data.

Approximate False Positive Rate Control in Selection Frequencies for Random Forest

Using spark streaming, predicts the violation location in real time for New York parking tickets dataset

This was a group project where we are comparing the effectiveness of supervised learning using various multivariate data sets and i was involved doing so using Random Forest Model. I implemented the feature importance of various predictor variables and how it effects the error rate(RMSE). I used the Student Performance Dataset to show the importance of various predictor variables. I implemented it in Python using various libraries like Numpy, Scipy, Scikit-learn, pandas, matplotlib and seaborn packages for plotting the figures.

The objective of this capestone is to identify types of prescribers that are a high-risk for opioid related fatalities across the country and predict most influential opioids leading to opioid related deaths.

SVM, Logistic Regression, K-Nearest Neighbors Classifier, GaussianNB, Random Forest, XGBoost, DecisionTree Classifier, Ensembled Classifier, ExtraTrees Classifier, Voting Classifier