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MaurizioFD / RecSys2019_DeepLearning_Evaluation

Star 986

This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.

deep-learning neural-network reproducible-research collaborative-filtering matrix-factorization hyperparameters bpr recommendation-system recommender-system reproducibility recommendation-algorithms knn matrix-completion evaluation-framework content-based-recommendation hybrid-recommender-system funksvd bprmf bprslim slimelasticnet
  • Updated May 25, 2023
  • Python

MBKraus / Predicting_real_estate_prices_using_scikit-learn

Star 168

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

real-estate python machine-learning neural-network random-forest lasso xgboost polynomial ensemble-learning ols decision-trees ridge-regression polynomial-regression knn multi-layer-perceptron amsterdam predicting-housing-prices lasso-regression mlp-regressor knn-regression
  • Updated Apr 9, 2019
  • Python

ggulgun / NIDS-Intrusion-Detection

Star 89

Simple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN

data-science machine-learning data-mining svm pca ids intrusion-detection dimensionality-reduction data-mining-algorithms knn intrusion-detection-system kdd99 kdd-dataset
  • Updated Oct 18, 2019
  • Python

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