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#

hypertuning

Here are 20 public repositories matching this topic...

Nudity, violence and drugs detection using nudeNet for nudity, for violence and drugs detection I hyper-tuned mobilenet model on my own collected dataset, the final results is a python flask API that takes an image or a set of images, will return a score on how much it's suitable for work.

  • Updated Jul 10, 2021
  • Jupyter Notebook

This project implements hyper-tuned federated learning using the Flower framework, combining FedAvg, Logistic Regression, and a 2-layer CNN. It enables decentralized model training across devices, optimizing performance while ensuring data privacy and improving accuracy on both simple and complex tasks.

  • Updated May 22, 2024
  • Jupyter Notebook

In this project, XGBoost is applied to forecast real estate prices using the Boston Housing Dataset. The primary aim is to create an effective predictive model, assess its accuracy through metrics like Mean Absolute Error (MAE), and refine its performance by tuning hyperparameters with HYPEROPT.

  • Updated Nov 26, 2023
  • Jupyter Notebook

AQI Predictor V2 use multiple Supervised Machine Learning with Hyper tuning. ML algorithms used Linear Regressor, Lasso Regressor, Decision Tree Regressor, Random Forest Regressor, XGboost Regressor. The Model deployed on web and can predict AQI visit https://aqipredictor.up.railway.app/

  • Updated Nov 27, 2022
  • Python

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  • Updated Jun 25, 2022
  • Jupyter Notebook

πŸ“” This repository delves into Logistic Regression for loan approval prediction at LoanTap. It covers data preprocessing, model development, evaluation metrics, and strategic business recommendations. Explore model optimization techniques such as confusion matrix, precision, recall, Roc curve and F1 score to effectively mitigate default risks.

  • Updated Apr 3, 2025
  • Jupyter Notebook

One of the challenges faced by any IT company is about 30% of the candidates who accept the jobs offer do not join the company. This leads to huge loss of revenue and time as the companies initiate the recruitment process again to fill the workforce demand. This project builds a model can be used to predict the likelihood of a candidate joining ...

  • Updated Nov 23, 2021
  • Jupyter Notebook

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