Faces recognition example using eigenfaces and SVMs
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Updated
Oct 22, 2016 - Jupyter Notebook
Faces recognition example using eigenfaces and SVMs
Feature Engineering and Prediction of Survivors on the Titanic Dataset
Engaged in research to help improve to boost text sentiment analysis using facial features from video using machine learning.
Sentiment analysis on customer reviews using machine learning and python
Faces recognition example using eigenfaces and SVMs
A Comprehensive Guide to Titanic Machine Learning from Disaster
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
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
Machine Learning - Exoplanet Exploration. used SVM, KNN, RandomForest Models.
Created machine learning models capable of classifying candidate exoplanets from a raw dataset.
predict where the patient will be discharged to before surgery
The project aims to apply Naives Bayes on TF-IDF and Word2Vec Models .Use one of Selection Best Feature techniques to chose only features that contribute to the performance of the prediction
Assignment on Logistic Regression
Iris Data : Classification / Pattern Recognition, Predict the Class of Flower based on Available Attributes.
Evaluate Machine Learning Models with Yellowbrick
A collection of statistical methods
Supervised Machine Learning and Credit Risk
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…
This repo is about Machine Learning and Classification
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…
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