Loan-WebML-powered Loan-Marketer Customer Filtering Engine
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eForestThis is the official implementation for the paper 'AutoEncoder by Forest'
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loloA random forest
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pykitmlMachine Learning library written in Python and NumPy.
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Trajectory-Analysis-and-Classification-in-Python-Pandas-and-Scikit-LearnFormed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently t…
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cqrConformalized Quantile Regression
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AIML-ProjectsProjects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning
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Decision Tree JsSmall JavaScript implementation of ID3 Decision tree
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Orange3🍊 📊 💡 Orange: Interactive data analysis
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QuickmlA fast and easy to use decision tree learner in java
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ShifuAn end-to-end machine learning and data mining framework on Hadoop
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InfiniteboostInfiniteBoost: building infinite ensembles with gradient descent
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Tensorflow Ml Nlp텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)
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ChefboostA Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python
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RandomforestexplainerA set of tools to understand what is happening inside a Random Forest
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Machine Learning Is All You Need🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!
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Machine Learning ModelsDecision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
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EmlearnMachine Learning inference engine for Microcontrollers and Embedded devices
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Machine Learning With PythonPractice and tutorial-style notebooks covering wide variety of machine learning techniques
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Benchm MlA minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
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Machine Learning In RWorkshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
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Awesome Decision Tree PapersA collection of research papers on decision, classification and regression trees with implementations.
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Ml ProjectsML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python
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Isl PythonSolutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
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Predicting real estate prices using scikit LearnPredicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)
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GcforestThis is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'
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SporfThis is the implementation of Sparse Projection Oblique Randomer Forest
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RoffildlibraryLibrary for MQL5 (MetaTrader) with Python, Java, Apache Spark, AWS
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Edarfexploratory data analysis using random forests
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Stock Market Sentiment AnalysisIdentification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka
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25daysinmachinelearningI will update this repository to learn Machine learning with python with statistics content and materials
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TpotA Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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DtreevizA python library for decision tree visualization and model interpretation.
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