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RezaSaadatyar / Machine-Learning-in-Python

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This repository contains functions/codes related to different methods of machine learning for classification and clustering in python.

random-forest clustering naive-bayes machine-learning-algorithms classification logistic-regression support-vector-machine adaboost decision-trees gaussian-mixture-model principal-component-analysis k-nearest-neighbours singular-value-decomposition k-means-clustering multilayer-perceptron independent-component-analysis t-distributed-stochastic feature-selection-methods feature-extraction-methods linear-discrimination-analysis
  • Updated Jul 7, 2024
  • Jupyter Notebook

serengil / chefboost

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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

python data-science machine-learning data-mining random-forest kaggle id3 gbdt gbm gbrt gradient-boosting-machine cart adaboost decision-trees gradient-boosting c45-trees categorical-features gradient-boosting-machines regression-tree
  • Updated Jul 4, 2024
  • Python

Vibhuarvind / Detection-of-Depression-Using-Late-Fusion-of-Sequential-Actigraphy-Features

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Analyzed time-series data (Depressjon) to detect depression from patient activity recorded via clinical actigraphy watches. Utilized features such as time domain, statistical metrics, and LSTM-extracted attributes.

lstm statistical-inference adaboost sleep-tracker svc time-series-analysis behavioural timedomain depression-detection featurefusion stacking-classifier votingcl latefusion
  • Updated Jun 30, 2024
  • Jupyter Notebook

harmanveer-2546 / Diabetes-Prediction-Using-Machine-Learning

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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.

python machine-learning random-forest numpy os pandas seaborn adaboost pickle decision-trees gradient-boosting-classifier knn-classification gridsearchcv svc-model
  • Updated Jun 10, 2024
  • Jupyter Notebook

Davityak03 / Ensemble-modelling

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This repository contains a comprehensive guide and implementation of ensemble modeling techniques, specifically focusing on Boosting, Bagging, and Voting. Ensemble methods are powerful techniques in machine learning that combine the predictions from multiple models to improve overall performance and robustness.

python svm voting machine-learning-algorithms logistic-regression adaboost ensemble-model boosting bagging desiciontree
  • Updated Jun 3, 2024

SarveshChandra / Machine-Learning-and-Artificial-Intelligence-Case-Studies

Star 2

AIML Projects

statistics computer-vision deep-learning random-forest feature-extraction batch-normalization neural-networks supervised-learning pca recommendation-system ensemble-learning object-detection adaboost decision-trees feature-engineering unsupervised-learning support-vector-machines object-recognition knearest-neighbor-algorithm gradient-boosting
  • Updated May 30, 2024
  • Jupyter Notebook

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