fail Vector - Moozonian

GitHub Repo https://github.com/mostofa-faysal/Vector-Search-Failure-Analysis-Toolkit

mostofa-faysal/Vector-Search-Failure-Analysis-Toolkit

No repository description available.

GitHub Repo https://github.com/tylkomat/leaflet-vectorgrid-epsg4326

tylkomat/leaflet-vectorgrid-epsg4326

Demo for failed display on CRS EPSG4326

GitHub Repo https://github.com/ksharma67/Heart-Failure-Prediction

ksharma67/Heart-Failure-Prediction

This problem is a typical Classification Machine Learning task. Building various classifiers by using the following Machine Learning models: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Light GBM and Support Vector Machines with RBF kernel.

GitHub Repo https://github.com/Ifeoluwa-hub/Heart-Failure-Prediction-and-Deployment-with-Flask-and-Heroku

Ifeoluwa-hub/Heart-Failure-Prediction-and-Deployment-with-Flask-and-Heroku

Cardiovascular diseases are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. Early detection, and managment of cardiovascular diseases can be a great way to manage the fatality rate associated with cardiovascular diseases, and this is where a machine learning model comes in. For the purpose of predicting the risk of a heart failure in patients, I used the Support Vector Classifier to build a machine learning model, and deployed it using Flask and Heroku

GitHub Repo https://github.com/sam-006/heart-failure-prediction

sam-006/heart-failure-prediction

Predicting heart stroke using suppport vector machine

GitHub Repo https://github.com/caspiankeyes/Symbolic-Residue

caspiankeyes/Symbolic-Residue

Symbolic Residue Diagnostic Suite: Recursive Shells track and diagnose transformer model failure modes: silent inconsistencies or "residues" in reasoning paths. The structural data vectors behind why advanced reasoning fails.

GitHub Repo https://github.com/binoydutt/Resume-Job-Description-Matching

binoydutt/Resume-Job-Description-Matching

The purpose of this project was to defeat the current Application Tracking System used by most of the organization to filter out resumes. In order to achieve this goal I had to come up with a universal score which can help the applicant understand the current status of the match. The following steps were undertaken for this project 1) Job Descriptions were collected from Glass Door Web Site using Selenium as other scrappers failed 2) PDF resume parsing using PDF Miner 3) Creating a vector representation of each Job Description - Used word2Vec to create the vector in 300-dimensional vector space with each document represented as a list of word vectors 4) Given each word its required weights to counter few Job Description specific words to be dealt with - Used TFIDF score to get the word weights. 5) Important skill related words were given higher weights and overall mean of each Job description was obtained using the product for word vector and its TFIDF scores 6) Cosine Similarity was used get the similarities of the Job Description and the Resume 7) Various Natural Language Processing Techniques were identified to suggest on the improvements in the resume that could help increase the match score

GitHub Repo https://github.com/michaeldjeffrey/elbow-jason-datamelon-we-miss-you-star-this