squaredev-io/whitebox

[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes

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SWHL/TextRecMetric

Compute the metric of text recognition algorithm.

Language: Python - Size: 26.4 KB - Last synced: 5 days ago - Pushed: about 1 month ago - Stars: 0 - Forks: 0

lex-hue/Stock-Predictor-V4 📦

A reinforcement learning model specialized in stock prediction utilizing deep learning techniques, incorporating reward mechanisms, compatible with any machine equipped with Python.

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sanskriti-lal/EmoFuse

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ericyoc/simple-language-model-creation-demo

A language model that generates text based on a given prompt.

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avid7-tech/Predicting-stars-galaxies-and-quasars-using-ML-algorithms

This repository contains code for classifying galaxies into three classes: Galaxy, Quasar, and Star, using machine learning techniques. The dataset used in this project is the Sloan Digital Sky Survey (SDSS) dataset.

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hollobit/ML_evaluation_metrics

Landscape of ML/DL performance evaluation metrics

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Neha-Shrestha/Diabetes-Prediction

Modelled data on various machine learning models like Support Vector Machine, Logistic Regression, KN Neighbors, Decision Tree Classifier, Random Forest Algorithm and Naive Bayes Classifier to compare the accuracy metrics for each algorithm.

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Honey28Git/Machine-Learning

The folliwing ML project involves EDA analysis of Election Dataset, Data preparation for modelling, and prediction using ML models. Also Text Analysis on the inaugral corpora from nltk to analyse the most frequently used words in Presidents' Speeches.

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carmengg/accuracy-assessment-implementation

This is a Python notebook giving an overview and implementing the accuracy assessments in *Good practices for estimating area and assessing accuracy of land change* by Olfosson et al. (2014).

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maryamsoftdev/multi-class-SVM-and-single-affine-layer

Day 9

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SyrineB11/Classification-projects

Solved Classification Problems

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neeraj1397/Radiography-Based-Diagnosis-Of-COVID-19-Using-Deep-Learning

Developed a Convolutional Neural Network based on VGG16 architecture to diagnose COVID-19 and classify chest X-rays of patients suffering from COVID-19, Ground Glass Opacity and Viral Pneumonia. This repository contains the link to the dataset, python code for visualizing the obtained data and developing the model using Keras API.

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eXascaleInfolab/xmeasures

Extremely fast evaluation of the extrinsic clustering measures: various (mean) F1 measures and Omega Index (Fuzzy Adjusted Rand Index) for the multi-resolution clustering with overlaps/covers, standard NMI, clusters labeling

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Neethuharii/Titanic-Survival-Predictoin

It is a Titanic Survival Prediction using Machine Learning with Python.

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business-science/modeltime.resample

Resampling Tools for Time Series Forecasting with Modeltime

Language: R - Size: 17.9 MB - Last synced: 5 months ago - Pushed: 5 months ago - Stars: 17 - Forks: 7

Shipra-09/ML-Project-Multiple-Linear-Regression

Exploring Insights/Inferences by performing EDA on the given project data (50_Startups and Toyota Corolla data) . Model fitting via linear regression by Importing sklearn package. Selecting the best fitted model via python programming.

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WINKAM/Classification-Metrics-Manager

Classification Metric Manager is metrics calculator for machine learning classification quality such as Precision, Recall, F-score, etc.

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randleon/Understanding-Classification-Model-Performance-Metrics-On-Diabetes-Dataset

Evaluation of the performance of classification models can be facilitated through a combination of calculating certain types of performance metrics and generating model performance evaluation graphics. The purpose of this exercise is to calculate a suite of classification model performance metrics via Python code functions.

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manishghop/Machine-Learning-Notebooks

Practicing ML algorithms on various Kaggle Datasets

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LailaMahmoudi/Breast-Cancer-Predictions-With-SVM

Implementation of SVM Classifier To Perform Classification on the dataset of Breast Cancer Wisconin; to predict if the tumor is cancer or not.

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chandru-engineer/MNIST-Classification-

In this project, the numeric digits are classified by using deep learning algorithm.

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shreyaskorde16/YOLOv4-Computer-Screens

Multiple Object Tracking in video Using Deep learning

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ymeghana/Early-Detection-Of-Sepsis

To Detect Early Sepsis Disease

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HCYENDLURI/Comparing-6-Classifiers-for-Sepsis-Dataset

To Detect Sepsis Disease using six Classifiers on clinical data

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gopiashokan/Diabetes-Prediction-using-Machine-Learning

Experience predictive healthcare with our Streamlit app. Utilizing Random Forest, our tool analyzes medical data to assess diabetes risk swiftly. Ideal for healthcare professionals and researchers, this user-friendly app simplifies risk evaluation. Join us in the fight against diabetes.

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cmu05391-gsilvera/PointingBakeOff

Open design challenge for optimizing accuracy and speed of a user clicking icons on a screen. Teammates: Jeffrey Chiu, Juliette Wong, Gustavo Silvera

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Mervin-Abraham/Traffic-Sign-Detection-and-Recognition

Prediction Analysis on TSR using CNN with 99% accuracy

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FilippoPisello/easypred

Get insights over your prediction's accuracy regardless of how it was obtained

Language: Python - Size: 3.24 MB - Last synced: 2 days ago - Pushed: about 2 years ago - Stars: 0 - Forks: 0

omogbolahan94/Customer-Churn-Prediction

This project demonstrates how to build a logistic regression model and understanding the evaluation metrics to determine if the model is performing well or not

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SaadTariq01DataAnalyst/Prediction-of-Bank-Churn-Customer

The goal of this project is to develop a machine learning model that can help banks to identify customers who are likely to churn and take appropriate measures to retain them

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aimananees/PM-Project

Performance Analysis of Multiple Classifiers using different Term Weighting Schemes for Sentiment Analysis

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fau-masters-collected-works-cgarbin/decision-threshold-effect-on-accuracy

What is "accuracy"? The effect of changing the decision threshold on a model's accuracy.

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eXascaleInfolab/GenConvNMI

Generalized Conventional Mutual Information (GenConvMI) - NMI for overlapping (soft, fuzzy) clusters (communities), compatible with standard NMI, pure C++ version (single executable)

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YugalPatil97/Cluster_Analysis

Creating clusters for gobal development measurements

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apple-fritter/weechat.typo-aggregator

Script to spell check that records misspelled words in a TSV (tab-separated values) file. Python.

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ptsourav21/NewsHeadlneCategorizationNLP

XLM-Roberta is a transformer model that categorized bengali news headline into six different categories. This code is used in research work which is published in one of the IEEE conferences.

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ptsourav21/MachineLearningImageClassify

Three skin diseases images classified with support vector machine. The dataset is collected from kaggle and some other sources. This code is used in a research work in one of the IEEE conferences.

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francaisse/DMWL-CW2

Voting Ensemble & Stock Prediction - Project Submission for Data Mining & Machine Learning Module

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angelicavelez/megaline_classification_prediction_model

model is able to predict the type of Ultra or Smart plan that users need.

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rochitasundar/Customer-profiling-using-ML-EasyVisa

The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidates more likely to have the visa certified.

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rochitasundar/TwitterSentimentAnalysis-BigDataProject

Scrapped tweets using twitter API (for keyword ‘Netflix’) on an AWS EC2 instance, ingested data into S3 via kinesis firehose. Used Spark ML on databricks to build a pipeline for sentiment classification model and Athena & QuickSight to build a dashboard

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srishilesh/Machine-learning

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HebaHossam68/Heart-Disease-

Machine learning project

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Anas436/Credit-Card-Fraud-Detection-using-Machine-Learning-with-Python

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Careless-Caramel/Raisin-NB

Gaussian Naive Bayes Classification on raisin dataset.

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johannesuhl/local_accuracy

A set of python scripts for spatially explicit accuracy assessments of binary, gridded geospatial data, e.g., for human settlement data mapping built-up (1) and not built-up (0) areas.

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KrishArul26/Password-Strength-Prediction-Deployment

This repository, focus on password strength prediction. I have created an SVM model to classify whether password strength or medium or weak. After training the model, It has deployed on the Heroku platform.

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jaiminjariwala/Image-classification-Keras.CIFAR10-dataset

Image_Classification_CIFAR10_Dataset

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UmarRajpoot/Identify-Spam-Email-ML-Model

This Model is used to Predict Emails data. Either emails are Spam or Normal (Ham) Mail.

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Careless-Caramel/Diabetes-Prediction

The goal of this project is to predict whether a patient reported is diabetic or not based on diagnostic measures using a Machine Learning algorithm. We are focusing on KNN Classifier for this problem.

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niranjana1997/-Content-Based-Image-Retrieval-Using-Hybrid-Feature-Extraction-Techniques

Images consist of visual components such as color, shape, and texture. These components stand as the primary basis with which images are distinguished. A content-based image retrieval system extracts these primary features of an image and checks the similarity of the extracted features with those of the image given by the user. A group of images similar to the query image fed is obtained as a result. This paper proposes a new methodology for image retrieval using the local descriptors of an image in combination with one another. HSV histogram, Color moments, Color auto correlogram, Histogram of Oriented Gradients, and Wavelet transform are used to form the feature descriptor. In this work, it is found that a combination of all these features produces promising results that supersede previous research. Supervised learning algorithm, SVM is used for classification of the images. Wang dataset is used to evaluate the proposed system.

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danielmuthama/DL-Classifier-Face-Verification-

Deep Learning Face Detection and Verification

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proacc2022/HAR-tl

The project is an integral cog in Computer Vision and Artificial Intelligence and Machine learning. It aims to determine the activity of a human from a video provided to the machine. It is a step forward in solving various problems like surveillance, fall detection for elderly or sick people, robotics and computer interaction, security among many others. We want a higher accuracy in doing so with respect to the videos used for training the machine.

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miwermi/credit-risk-analysis

Experiemented with different machine learning methods to review loan data and analyze credit-lending risk and accuracy of prediction methods.

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manasik29/Predict-whether-an-individual-will-hire-an-Attorney-or-not-by-Logistic_Regression

Build a model which will predict whether an individual will hire an attorney or not.

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manasik29/Predict-whether-a-client-has-subscribed-a-term-deposit-or-not

Predict-whether-a-client-has-subscribed-a-term-deposit-or-not

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manasik29/Assignment_Prediction_ForestFires_BurnedArea

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

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Neetz78/HDFS_anomaly

Performed data mining on HDFS log to gather important features to detect anomalies in the error logs.

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marizombie/f1-score-vs-accuracy

This notebook shows how the f1 metric differs accuracy on imbalanced data. The heart disease dataset from kaggle is used (https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease).

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ShehanSanjula/Spam-Email-Filtering-System-Public

End-to-end implementation of Spam Detection in Email using Machine Learning, Python, Flask, Gunicorn, Scikit-Learn, and Logistic Regression on the Heroku cloud application platform.

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walkerdustin/Vergleich-von-Messmethoden-fuer-Punktwolken

Bei der Vermessung eines physischen Raumes ist das Ergebnis eine Punktwolke. Diese Punktwolke beschreibt dann ausgewählte Punkte im Raum, zum Beispiel auf den Wänden und der Decke. Wenn diese Punkte in zwei seperaten Messungen gemessen werden, vielleicht sogar von unterschiedlichen Geräten, soll hinterher herausgefunden werden wie genau diese Punktwolken übereinstimmen. Dafür gibt es zwei grundsätzlich verschiedene Methoden. Diese sollen hier verglichen werden.

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swarna0712/San-Fransisco-Crime-Classification-using-PySpark

Big Data Project - SSML - Spark Streaming for Machine Learning

Language: Python - Size: 21.5 KB - Last synced: about 1 year ago - Pushed: over 2 years ago - Stars: 2 - Forks: 1

Areesha-Tahir/K-Means-And-KNN-On-MultiClass-Dataset

Applying K Means and KNN on a multiclass dataset to make clusters and find nearest neighbours.

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TUW-GEO/ABCRaster

ABCRaster stands for Accuracy assessment of Binary Classified Raster. It is a package for performing validation, accuracy assessment, or comparing binary classified rasters (.tiff) versus a reference (.shp). Primary use case is to compare flood maps encoded as (1,0) in tiff file format against a reference vector from CEMS.

Language: Python - Size: 111 KB - Last synced: 11 days ago - Pushed: 11 days ago - Stars: 0 - Forks: 0

nikhiljsk/Hangman-CLI

Python CLI which plays hangman, given knowledge of a dictionary. Contains accuracy metrics for word-level and character-level. Does NOT include human interaction.

Language: Python - Size: 227 KB - Last synced: about 1 year ago - Pushed: about 4 years ago - Stars: 0 - Forks: 0

Pradnya1208/Naive-Bayes

This project aims to understand and build Naive Bayes classifier to predict the salary of a person.

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ProsperChuks/binary-classification-on-image

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mntalha/Top-K-Accuracy-Error

Shows how to calculate Top-K value in image classification and gives related examples.

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barbaraneves/machine-learning-2020-1

Atividades e trabalhos feitos para a disciplina de Aprendizado de Máquina, semestre 2020.1, Universidade Federal do Ceará - Campus Quixadá.

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d4rthm4ul/ML-Classification-LogReg-SupVecMach-KNN

With the help of Logistic Regression, Support Vector Machine and K-Nearest Neighbors models you can predict a person's income that it will be below or above 50K.

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AISWARYA-NANDAKUMAR/Deep-learning-for-classification-of-EEG-signals

Classification of EEG signals using Deep learning. This is a binary classification problem. The EEG signals are classified into 2 target classes, normal and abnormal signals. Keras and tensorflow are used. this classification can be used for the prediction of various brain disorders.

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subhayuroy/MotorEvents

This ML⚒ project is to prove the dependencies of a motor🛠 in an everyday pump system👷‍♂️👨‍🏭

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gasparl/esdi

A Shiny R web application to estimate differences in diagnostic efficiency based on differences between single-condition cases.

Language: R - Size: 747 KB - Last synced: about 1 year ago - Pushed: over 3 years ago - Stars: 0 - Forks: 0

priscilla-chk/top_k_models

Python Data Tool: Top-k classification and model selector

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forvis/forvision_data

Datasets for the Forvision project

Size: 77.8 MB - Last synced: 4 months ago - Pushed: over 5 years ago - Stars: 0 - Forks: 1

bunji2/metrics

Sample of metrics by GoLang

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