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classification-report

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roshansridhar / Multimodal-Sentiment-Analysis

Star 28

Engaged in research to help improve to boost text sentiment analysis using facial features from video using machine learning.

svm machine-translation cross-validation pandas nltk bag-of-words logistic-regression tf-idf openface sparse-matrix data-cleaning classification-report count-vectorizer utterance multimodal-sentiment-analysis recursive-feature-elimination moud mosi openear
  • Updated Jan 12, 2018
  • Jupyter Notebook

faysal37 / titanic_hosted_by_kaggle

Star 0

A Comprehensive Guide to Titanic Machine Learning from Disaster

data-transformation cross-validation model-selection imputation recall outlier-detection confusion-matrix feature-engineering hyperparameter-tuning precision learning-curve univariate-distributions classification-report multivariate-analysis f1-score model-training bivariate-analysis feature-importance precision-recall-curve auc-roc-curve
  • Updated Aug 28, 2018
  • Jupyter Notebook

sharmaroshan / Fraud-Detection-in-Online-Transactions

Star 65

Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting

finance machine-learning query deep-learning data-visualization data-analytics classification data-analysis sampling confusion-matrix large-dataset classification-report anamoly-detection auprc under-sampling over-sampling
  • Updated May 23, 2019
  • Jupyter Notebook

sushantdhumak / Fashion-Clothing-Classification

Star 7

Kaggle Machine Learning Competition Project : In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset of Zalando's article images

keras splitting dropout classification keras-classification-models maxpooling keras-neural-networks model-evaluation classification-report adam-optimizer categorical-cross-entropy conv2d subplots reshaping
  • Updated Nov 21, 2019
  • Jupyter Notebook

shaunwang1350 / CreditLoans_MachineLearning

Star 1

Credit risk is an inherently unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. I employed Machine Learning techniques to train and evaluate models with unbalanced classes. I used imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling. I also evaluated the…

random-forest classification-report credit-risk cluster-centroids randomoversampler smote-sampling tp-fp balanced-accuracy-scores
  • Updated Nov 19, 2020
  • Jupyter Notebook

fischlerben / Machine-Learning-Credit-Risk

Star 10

Machine-Learning project that uses a variety of credit-related risk factors to predict a potential client's credit risk. Machine Learning models include Logistic Regression, Balanced Random Forest and EasyEnsemble, and a variety of re-sampling techniques are used (Oversampling/SMOTE, Undersampling/Cluster Centroids, and SMOTEENN) to re-sample th…

python random-forest modeling pandas logistic-regression confusion-matrix resampling classification-report smote ensemble-machine-learning cluster-centroids accuracy-score smoteenn
  • Updated Jan 24, 2021
  • Jupyter Notebook

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