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bagging-ensemble

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alifrmf / Mobile-Price-Prediction-Classification-Analysis

Star 15

Supervised Machine Learning Analysis Using Classification Models

python data-science random-forest svm exploratory-data-analysis machine-learning-algorithms supervised-learning xgboost hyperparameter-optimization classification ensemble-learning adaboost preprocessing decision-trees gradient-boosting voting-classifier mobile-price-prediction bagging-ensemble boosting-ensemble pruning-optimization
  • Updated Jul 10, 2023
  • Jupyter Notebook

somjit101 / Facebook-Friend-Recommendation

Star 11

This is a friend recommendation systems which are used on social media platforms (e.g. Facebook, Instagram, Twitter) to suggest friends/new connections based on common interests, workplace, common friends etc. using Graph Mining techniques. Here, we are given a social graph, i.e. a graph structure where nodes are individuals on social media plat…

machine-learning graph graph-algorithms social-network cross-validation xgboost graph-theory decision-trees social-network-analysis decision-tree-classifier gradient-boosting graph-similarity randomizedsearchcv bagging-ensemble boosting-ensemble facebook-friend-recommendation
  • Updated Feb 15, 2024
  • Jupyter Notebook

rochitasundar / Customer-profiling-using-ML-EasyVisa

Star 7

The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidate…

decision-tree-classifier gradient-boosting-classifier f1-score random-forest-classifier grid-search-hyperparameters precision-recall accuracy-metrics xgboost-classifier stacking-ensemble adaboostclassifier bagging-ensemble grid-search-cross-validation
  • Updated Jan 20, 2022
  • Jupyter Notebook

StrangeCoder1729 / FinancialFraudDetectionModels

Star 4

Developed and evaluated machine learning and deep learning models for detecting financial fraud.

python finance machine-learning deep-learning neural-network naive-bayes-classifier artificial-neural-networks logistic-regression convolutional-neural-network decision-tree-classifier knn-classification ensemble-classifier random-forest-classifier xgboost-classifier financial-fraud bagging-ensemble boosting-ensemble financial-fraud-detection
  • Updated May 12, 2024
  • Jupyter Notebook

Ayda-Darvishan / Tuning-ML-Classifiers

Star 3

The project includes building seven different machine learning classifiers (including Linear Regression, Decision Tree, Bagging, Random Forest, Gradient Boost, AdaBoost, and XGBoost) using Original, OverSampled, and Undersampled data of ReneWind case study, tuning hyperparameters of the models, performance comparisons, and pipeline development f…

pipeline random-forest exploratory-data-analysis cross-validation supervised-learning xgboost adaboost tuning-parameters gradient-boosting oversampling undersampling gridsearchcv randomizedsearchcv bagging-ensemble boosting-ensemble
  • Updated May 9, 2023
  • Jupyter Notebook

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