Classifying in R Identifying Categories for Customer Complaint’s Mediation Automation
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Updated
Jun 26, 2019
Classifying in R Identifying Categories for Customer Complaint’s Mediation Automation
Testing the quality of red wine as Good or Bad according to the given parameters from the Kaggle Dataset by using Support Vector Machine supervised ML Algorithm.
Sentiment Analyst using Supervised Learning using Women's E-Commerce Clothing Reviews Dataset
A collaborative project looking into the likelihood of Covid-19 infection in the United States.
Hyperparameter tuning using a robust simulation optimization framework
No-reference Stereoscopic Image Quality Predictor using Deep Features from Cyclopean Image
Spark Machine Learning Library - learning and developing Machine Learning algorithms
A web application to see effect of C hyperparameter on classification boundary and marginal threshold in SVM.
Investigated factors that affect the likelihood of charity donations being made based on real census data. Developed a naive classifier to compare testing results to. Trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. Selected the best model based on accuracy, a modif…
supervised machine learning classifier model
What is the relationship between airline sentiments and airlines? What is the reason for the negativity mentioned in the dataset? What is the relation of time with sentiments? Which model is best for sentiment analysis when we do ensemble learning?
Twitter sentiment analysis allows you to keep track of what's being said about your product or service on social media, and can help you detect angry customers or negative mentions before they they escalate.
Gender recognition system based on Support Vector Machine (SVM) and machine learning
We will be creating an algorithmic crypto trading bot that will use the Kraken API to get crypto prices. We will use machine learning to determine the trend of the market from historical data and determine the best strategies/indicators to use.
This project involves the implementation of efficient and effective LinearSVC on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.
Machine Learning Models for Absenteeism at Work Dataset
2019 [Julia] GPU CUDAnative SVM: a stochastic decomposition implementation of support-vector machine training
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