R interface to Keras Tuner
-
Updated
Apr 15, 2024 - R
#
hypertuning
Here are 14 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.
python keras transfer-learning flask-api nsfw data-augmentation mobilenet imagedatagenerator hypertuning nsfw-detection-api
-
Updated
Jul 10, 2021 - Jupyter Notebook
Hyper-tuned federated learning using flower framework.
python machine-learning decentralized jupyter-notebook cnn logistic-regression flower federated-learning hypertuning fedavg
-
Updated
May 22, 2024 - 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/
machine-learning deployment linear-regression ml supervised-learning lasso-regression developed xgboost-regression 3rd-year-project hypertuning randon-forest
-
Updated
Nov 27, 2022 - Python
This repository contains work that has been done on various concepts of Python like linear regression, logistic regression, decision tree, Random forest, KNN, and K-means algorithm
random-forest linear-regression accuracy gamma confusion-matrix decision-tree kmeans-clustering svm-classifier rmse kernal knn-algorithm hypertuning logestic-regression
-
Updated
Jul 14, 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.
data-science machine-learning machine-learning-algorithms kaggle xgboost boston-housing-price-prediction kaggle-dataset xgboost-model xgboost-regression hypertuning
-
Updated
Nov 26, 2023 - Jupyter Notebook
This repository contains the Assignments and Projects of Deep Learning Specialization Course on Coursera.
-
Updated
Nov 15, 2023 - Jupyter Notebook
Выполненные лабораторные работы по курсу: "Искусственный интеллект и машинное обучение".
python clustering scikit-learn jupyter-notebook naive-bayes-classifier logistic-regression hypertuning future-selection
-
Updated
Jun 25, 2022 - Jupyter Notebook
A Supervised Machine Learning project in which I forecast upcoming six weeks of sales in advance using different ML Model For Rossmann Stores
-
Updated
Mar 8, 2023 - Jupyter Notebook
👩🏻🍳🍽️Restaurant Success Prediction using ML
machine-learning data-visualization sqlite3 outlier-detection restaurant feature-engineering feature-importance random-forest-classifier feature-encoding distplot hypertuning one-hot-encoding wordcloud-visualization grid-search-cross-validation success-prediction handling-outlier histplot data-cleaning-and-preprocessing model-building-and-evaluation randomised-search-cross-validation
-
Updated
Sep 21, 2024 - Jupyter Notebook
Lending Club Data Analysis
-
Updated
Apr 29, 2019 - Jupyter Notebook
-
Updated
Aug 23, 2023 - 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 …
python machine-learning random-forest exploratory-data-analysis gridsearchcv hypertuning logestic-regression
-
Updated
Nov 23, 2021 - Jupyter Notebook
📔 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.
machine-learning numpy pandas seaborn confusion-matrix kfold-cross-validation sklearn-library precision-recall hypertuning sklearn-metrics
-
Updated
May 27, 2024 - Jupyter Notebook
Improve this page
Add a description, image, and links to the hypertuning topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the hypertuning topic, visit your repo's landing page and select "manage topics."