Topic: support-vector-classifier Goto Github
Some thing interesting about support-vector-classifier
Some thing interesting about support-vector-classifier
support-vector-classifier,Breast Cancer Prediction
User: abhinav-chakravarty
support-vector-classifier,A machine learning model after detailed image processing applications that classifies a bee as either honey bee or bumble bee
User: abrar2652
support-vector-classifier,Integrative Biomechanical and Clinical Features Predict In-Hospital Trauma Mortality
User: absaw
support-vector-classifier,Visualize scATAC-seq profiles using PCA and UMAP. Construct a support vector classifier (SVC) to predict cell type given ATAC-seq expression profile.
User: adler-sudo
support-vector-classifier,The sinking of the RMS Titanic is one of the most infamous shipwrecks in world history. In this model, need to analyse what sorts of people were likely to survive. We also need to apply the tools of machine learning to predict which passengers survived in this tragedy.
User: anandanraju
support-vector-classifier,Build and evaluate various machine learning classification models using Python.
User: ansu-john
support-vector-classifier,This is about how to make Diabetes Prediction with Machine Learning. We are developing a machine learning model capable of predicting whether someone may have diabetes based on health data and specific parameters. Using the right machine learning algorithms, we will process this data to provide valuable predictions for patients and medical
User: aqilafadia
support-vector-classifier,Dari dataset tersebut akan mengklasifikasi kualitas pencemaran udara berdasarkan kategori perhitungan indeks standar pencemaran udara. Dimana dataset Indeks Standar Pencemaran Udara (ISPU) Tahun 2021 yang didapatan dari website Jakarta Open data dengan link: https://data.jakarta.go.id/dataset/indeks-standar-pencemaran-udara-ispu-tahun-2021
User: aryapangestu
Home Page: https://klasifikasi-kualitas-udara.herokuapp.com/
support-vector-classifier,To create a system that effectively detects and prevents credit card fraud using machine learning techniques, ensuring the security of financial transactions and protecting customers from fraudulent activities.
User: chemoshtony
support-vector-classifier,Predicting financial well-being through survey data from the Consumer Financial Protection Bureau
User: denistanjingyu
support-vector-classifier,Built machine learning algorithms (Decision Tree Classifier, Random Forest Classifier & Support Vector Classifier) to best predict the credit card approval.
User: dheerajpittala8055
support-vector-classifier,[Completed] Complete framework on multi-class classification covering EDA using x-charts and Principle Component Analysis; machine learning algorithms using LGBM, RF, Logistic Regression and Support Vector Algorithms; as well as Bayesian Optimizer with l1 and l2 regularization for Hyperparameter Tuning.
User: gracengu
support-vector-classifier,Compared the metrics and performance of different classification algorithms on Heart Failure dataset from UCI ML Repository
User: grvnair
support-vector-classifier,NTHU EE6550 Machine Learning slides and my code solutions for spring semester 2017.
User: howardyclo
support-vector-classifier,Titanic Survivor Analysis and Prediction
User: iamkirankumaryadav
support-vector-classifier,Contains nongraded challenge during FTDS Batch 001-Phase 1 at Hacktiv8
User: imfdlh
support-vector-classifier,A web app for visualizing Binary Classification Results using Streamlit module in Python deployed on Heroku.
User: indrapaul824
Home Page: https://bin-cls-viz-app.herokuapp.com/
support-vector-classifier,Classification ML models for predicting customer outcomes (namely, whether they're likely to opt into email / catalog marketing) depending on customer demographics (age, proximity to store, gender, customer loyalty duration) as well as sales and shopping frequencies by department
User: jspano95
support-vector-classifier,Assignments from Applied Machine Learning Class (UTD BUAN-6341)
User: krish1919ls
support-vector-classifier,This project implements the Support Vector Machine (SVM) algorithm for predicting user purchase classification. The goal is to train an SVM classifier to predict whether a user will purchase a particular product or not.
User: kshitizrohilla
support-vector-classifier,Machine Learning with Python, Numpy & SciKitLearn
User: kumarpython
support-vector-classifier,Customer Churn Analysis
User: larryloveiv
support-vector-classifier,Classification to predict whether a bank currency note is authentic or not based on variance of the image wavelet transformed image, skewness, entropy, and curtosis of the image using Machine Learning classifiers.
User: likarajo
support-vector-classifier,Predictions for the English Premier League season
User: likarajo
support-vector-classifier,Created a web app, that predicts the type of flower using Iris Dataset by the University of California, Irvine
User: lionelsamrat10
Home Page: https://iris-flower-detection-samrat.herokuapp.com/
support-vector-classifier,Identification of the Employees who are most likely to switch the jobs for package negotiations & Job offerings. Also, Analyzing the particular departments where the attrition rate is high and to take the preventive measures by using Decision Tree Classifer, Random Forest Classifier, Support Vector Classifier, Logistic Regression, K Nearest Neighbor and Gaussian Naive Bayes.
User: makrandbhandari
support-vector-classifier,This repository contains a notebook that examines the performance of various classification models on the Kaggle dataset: https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data. The best performing model was a Random Forest Classifier with 86.67% accuracy.
User: matthewcsc
support-vector-classifier,This is an exploration using synthetic data in CSV format to apply QML models for the sake of binary classification. You can find here three different approaches. Two with Qiskit (VQC and QK/SVC) and one with Pennylane (QVC).
User: maximer-v
support-vector-classifier,Predicting whether or not a customer will be approved for a credit card based on 15 predictor variables.
User: mdkearns
support-vector-classifier,Development and comparison of 12 machine learning models to predict autism as well as a discussion of the process.
User: neonostrich
support-vector-classifier,
User: nottynerd
support-vector-classifier,🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.
Organization: osspk
Home Page: https://github.com/harismuneer
support-vector-classifier,This is just a theoretical Machine Learning Model that will analyze the data and determine where the stroke can occur.
User: psavarmattas
support-vector-classifier,Unsupervised and supervised learning for satellite image classification
User: rifatsdas
support-vector-classifier,Intro to Machine Learning Assignment 2
User: roccyk
support-vector-classifier,Intro to Machine Learning Final Project
User: roccyk
support-vector-classifier,This repository contains the Iris Classification Machine Learning Project. Which is a comprehensive exploration of machine learning techniques applied to the classification of iris flowers into different species based on their physical characteristics.
User: ruban2205
Home Page: https://irisclassifier.streamlit.app/
support-vector-classifier,Diabetes Predictor Web App Predict diabetes in patients using classification models such as Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machines. It is deployed in a Flask web application on AWS Elastic Beanstalk.
User: sai-manas
support-vector-classifier,Project made in Jupyter Notebook with "News Headlines Dataset For Sarcasm Detection" from Kaggle.
User: sanjarh
support-vector-classifier,This repository provides a cancer classification model using Support Vector Classifier (SVC). The model aims to classify cancer cases into benign or malignant based on various features obtained from medical examinations.
User: shaadclt
support-vector-classifier,This project aims to predict diabetic patients using three different classification algorithms: Logistic Regression, Support Vector Classifier, and Random Forest Classifier. The project is implemented using Python and leverages scikit-learn, a popular machine learning library.
User: shaadclt
support-vector-classifier,A simple Flask application for data preprocessing, visualization and classification
User: sherwyn11
support-vector-classifier,Click below to checkout the website live
User: somenath203
Home Page: https://suicidal-thought-and-depression-predictor-frontend.vercel.app/
support-vector-classifier,This project is to build a model that predicts the human activities such as Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing or Laying using readings from the sensors on a smartphone carried by the user.
User: somjit101
support-vector-classifier,Machine learning library for classification tasks
Organization: starlangsoftware
support-vector-classifier,I contributed to a group project using the Life Expectancy (WHO) dataset from Kaggle where I performed regression analysis to predict life expectancy and classification to classify countries as developed or developing. The project was completed in Python using the pandas, Matplotlib, NumPy, seaborn, scikit-learn, and statsmodels libraries. The regression models were fitted on the entire dataset, along with subsets for developed and developing countries. I tested ordinary least squares, lasso, ridge, and random forest regression models. Random forest regression performed the best on all three datasets and did not overfit the training set. The testing set R2 was .96 for the entire dataset and developing country subset. The developed country subset achieved an R2 of .8. I tested seven different classification algorithms to classify a country as developing or developed. The models obtained testing set balanced accuracies ranging from 86% - 99%. From best to worst, the models included gradient boosting, random forest, Adaptive Boosting (AdaBoost), decision tree, k-nearest neighbors, support-vector machines, and naive Bayes. I tuned all the models' hyperparameters. None of the models overfitted the training set.
User: tboudart
support-vector-classifier,Interactive ML web application will allow users to choose classification algorithm, let them interactively set hyper-parameter values, and Input Image.
User: tekraj15
support-vector-classifier,Python project for Banknotes Analysis.
User: tharangachaminda
support-vector-classifier,Using a support vector machine to classify emails
User: wesbarnett
support-vector-classifier,
User: yuhexiong
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