Machine Learning as a Service for HEP
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Updated
May 10, 2022 - Python
Machine Learning as a Service for HEP
This is an end-to-end ML project, which aims at developing a classification model for the problem of predicting credit card frauds using a given labeled dataset. The classifier used for this project is RandomForestClassifier. Deployed in Heroku.
Building a simple, not-so cool ML Model - Polynomial Fitting
A price predictor model that predicts the price of your old car, based on some required input fields.
BioSim model with Conditional Variational Autoencoder
Forecasting the crime in a city from OSN data
Heart attack risk prediction using machine learning (Random Forest Model)
Key word/wake word detection with espressif esp32s3
This repository contains a LSTM model, Google Stock Price Predictor
Tetuan City Electricity Consumption High-Frequency Time-Series Forecasting Using Arima, UCM, Machine Learning (Random Forest and k-NN), and Deep Learning (GRU Recurrent Neural Network) Models.
summer-search is a Python package that provides a simple interface for searching the web, extracting relevant content, and generating a summary based on the extracted information
Video-based surgical skill assessment using 3D convolutional neural networks
A logistic regression model to predict Airbnb superhosts with Skikit-learn
A Software for Cabs which comprises most innovative ideas to provide a best-personalized user experience.
With the help of this chatbot, users can communicate doubts regarding vaccine registration.
Classification and regression models for predicting the level of risk associated with extending credit to a borrower and the basic EPS amount respectively.
This is a very simple machine learning project. This project is using the iris dataset and will tell you which flower aligns closest with 4 measurements.
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