grid-search-hyperparameters

There are 1 repository under grid-search-hyperparameters topic.

AliAmini93 / Fault-Detection-in-DC-microgrids
Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.
dwt feature-selection fft neural-network random-forest xgboost-classifier digsilent anova-analysis pearson-correlation cnn mutual-information svm data-augmentation linear-discriminant-analysis grid-search-hyperparameters
Language:Jupyter Notebook 40
uzunb / house-prices-prediction-LGBM
This repo has been developed for the Istanbul Data Science Bootcamp, organized in cooperation with İBB and Kodluyoruz. Prediction for house prices was developed using the Kaggle House Prices - Advanced Regression Techniques competition dataset.
data-science data-visualization grid-search-cross-validation grid-search-cv grid-search-hyperparameters gridsearchcv house-price-prediction lgbm lgbm-regressor lgbmregressor streamlit streamlit-application streamlit-dashboard streamlit-sharing streamlit-ui
Language:Jupyter Notebook 22
sanjeevai / disaster-response-pipeline
ETL pipeline combined with supervised learning and grid search to classify text messages sent during a disaster event
grid-search-hyperparameters etl-pipeline supervised-learning disaster-event sqlite-database data-engineering-pipeline
Language:Python 16
ShagunSharma98 / Global-Structural-Earthquake-Damage-Prediction
Using deep learning techniques like 1D and 2D CNNs, LSTM to detect damage in a structure with hinges/joints after an earthquake.
earthquakes damage-detection convolutional-neural-networks lstm-neural-networks spectrogram structural-engineering grid-search-hyperparameters training-1d-cnn 2d-cnns spectrograms
Language:Jupyter Notebook 15
bhattbhavesh91 / decision_tree_grid_search
Implementation of Grid Search to find better hyper-parameters for decision tree to reduce the over fitting.
grid-search-hyperparameters random-search decision-tree-algorithm
Language:Jupyter Notebook 12
sandipanpaul21 / Tree-Based-Models-in-Python
Tree based algorithm in machine learning including both theory and codes. Topics including from decision tree regression and classification to random forest tree and classification. Grid Search is also included.
classification-tree decision-boundary decision-regions decision-tree grid-search grid-search-hyperparameters random-forest regression-trees
Language:Jupyter Notebook 12
project_floodlight
asaficontact / project_floodlight
Crisis incidents caused by rebel groups create a negative influence on the political and economic situation of a country. However, information about rebel group activities has always been limited. Sometimes these groups do not take responsibility for their actions, sometimes they falsely claim responsibility for other rebel group’s actions. This has made identifying the rebel group responsible for a crisis incident a significant challenge. Project Floodlight aims to utilize different machine learning techniques to understand and analyze activity patterns of 17 major rebel groups in Asia (including Taliban, Islamic State, and Al Qaeda). It uses classification algorithms such as Random Forest and XGBoost to predict the rebel group responsible for organizing a crisis event based on 14 different characteristics including number of fatalities, location, event type, and actor influenced. The dataset used comes from the Armed Conflict Location & Event Data Project (ACLED) which is a disaggregated data collection, analysis and crisis mapping project. The dataset contains information on more than 78000 incidents caused by rebel groups that took place in Asia from 2017 to 2019. Roughly 48000 of these observations were randomly selected and used to develop and train the model. The final model had an accuracy score of 84% and an F1 Score of 82% on testing dataset of about 30000 new observations that the algorithm had never seen. The project was programmed using Object Oriented Programming in Python in order to make it scalable. Project Floodlight can be further expended to understand other crisis events in Asia and Africa such as protests, riots, or violence against women.
xgboost-model random-forest classification-model crisis grid-search-hyperparameters acled python3 object-oriented-programming sklearn asia grid-search pandas matplotlib
Language:Jupyter Notebook 9
SadmanSakib93 / ANN-Stratified-K-Fold-Cross-Validation-Keras-Tensorflow
This repo contains examples of binary classification with ANN and hyper-parameter tuning with grid search.
keras-neural-networks artificial-neural-networks deep-learning stratified-cross-validation cross-validation grid-search-hyperparameters machine-learning keras-tensorflow ann ipynb grid-search
Language:Jupyter Notebook 8
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.
decision-tree-classifier bagging-ensemble random-forest-classifier gradient-boosting-classifier adaboostclassifier xgboost-classifier stacking-ensemble grid-search-hyperparameters grid-search-cross-validation accuracy-metrics precision-recall f1-score
Language:Jupyter Notebook 7
aanchal1308 / disease-diagnosis-ML
Machine learning models for detection of diseases.
scikit-learn machine-learning-algorithms keras-neural-networks medical-diagnostics grid-search-hyperparameters
Language:Jupyter Notebook 6
eakgun / ISO_OLS_RBF
Generalized Improved Second Order RBF Neural Network with Center Selection using OLS
machine-learning matlab rbf-network rbf-kernel feature-selection levenberg-marquardt grid-search-hyperparameters optimization subset-selection rbfs center-selection rbf-neural-networks ols
Language:MATLAB 4
SwamiKannan / Post-Graduate-Program-AI-ML
Programming assignments completed in the PG Program for AI ML
great-learning supervised-learning unsupervised-learning exploratory-data-analysis statistics computer-vision nlp-machine-learning ann decision-trees ensemble-models recommendation-engines computer-vision-algorithms capstone-project decision-trees-algorithm hyperparameter-optimization open-cv tensorflow2 tokenizer cross-validation grid-search-hyperparameters
Language:Jupyter Notebook 4
JeremieGince / AutoMLpy
This package is an automatic machine learning module whose function is to optimize the hyper-parameters of an automatic learning model.
automl machine-learning deep-learning grid-search-hyperparameters random-search gaussian-processes multiprocessing python3 tensorflow pytorch sklearn
Language:Python 3
danielxu04 / credit-card-approvals
A logistic regression model that predicts whether or not a credit card application will get approved using SciKit.
grid-search-hyperparameters logistic-regression machine-learning python scikit-learn
Language:Jupyter Notebook 2
srinithya-halaharvi / Analyzing_Predicting_Election_Outcomes_Indian_Politics
A study to analyze and predict Election Outcome in Indian Politics using multiple machine-learning algorithms Decision Trees, Random Forests, SVM, and XGBoost with hyper parameters tuning (Grid search).
cross-validation data-mining data-visualization decision-tree-classifier grid-search-hyperparameters machine-learning mit-manipal predictive-analysis random-forest-classifier svm-classifier xgboost-classifier dmpa-project manipal-institute-of-technology
Language:Jupyter Notebook 2
yjeong5126 / housing_prices
Analysis and prediction for the housing market prices using Cross Validation and Grid Search in several regression models
cross-validation decision-tree-regression grid-search-hyperparameters housing-prices k-nearest-neighbours linear-regression machine-learning python ridge-regression
Language:Jupyter Notebook 2
AmirHHasani / Auto-tuning-of-Classic-Machine-Learning-Models-Hyperparameters-Using-Scikit-Learn
Using Scikit-Learn to optimize some of the hyperparameters of Classic ML Models
hyperparameter-optimization hyperparameter-tuning machine-learning scikit-learn tuning cross-validation grid-search-hyperparameters random-search
Language:Jupyter Notebook 1
angeloruggieridj / MLPClassifier-with-GridSearchCV-Iris
Experimental using on Iris dataset of MultiLayerPerceptron (MLP) tested with GridSearch on parameter space and Cross Validation for testing results.
mlp-classifier grid-search-hyperparameters gridsearchcv grid-search-cross-validation confusion-matrix
Language:Jupyter Notebook 1
anshul1004 / MachineLearningClassificationModels
Implementation of various machine learning models in scikit-learn
adaboost bagging decision-trees gaussian-naive-bayes google-colab gradient-boosting grid-search grid-search-hyperparameters gridsearchcv k-nearest-neighbours logistic-regression machine-learning neural-networks parameter-tuning python random-forest scikit-learn supervised-learning svm xgboost
Language:Jupyter Notebook 1
GabbyOlivares / Machine-Learning-Challenge
16. Exoplanet Exploration - Machine Learning Challenge
exoplanet machine-learning preprocess tune-model tune-hyperparameters comparison-report sklearn grid-search-hyperparameters test-train-dataset
Language:Jupyter Notebook 1
glubbdubdrib / lazygrid
Automatic, efficient and flexible implementation of complex machine learning pipeline generation and cross-validation.
machine-learning cross-validation python pipeline memoization optimization scikit-learn keras tensorflow model-selection model-comparison database openml artificial-intelligence neural-networks grid-search grid-search-hyperparameters
Language:Python 1
imehrdadmahdavi / grid-search
Using Grid Search to improve Machine Learning models
grid-search grid-search-hyperparameters decision-tree-classifier decision-tree machine-learning cross-validation
Language:Jupyter Notebook 1
joeanton719 / useful_codes
codes related to hyperparameter tuning and some classes, functions, etc. I have created to optmize classification problems (Continuously being updated ).
machine-learning hyperparameter-optimization optuna hyperopt grid-search-hyperparameters random-search scikit-optimize classification-model regression-models feature-selection
Language:Jupyter Notebook 1
mafda / disaster_response_pipeline
This project involves developing an ETL pipeline integrated with supervised learning and grid search to classify text messages sent during disaster events. It includes an ML pipeline and a web app designed to categorize disaster response messages in real time using NLP techniques
data-engineering data-science etl-pipeline flask-application grid-search-hyperparameters nlp-keywords-extraction nlp-machine-learning supervised-machine-learning
Language:Jupyter Notebook 1
marinafajardo / prevendo-customer-churn
Prevendo Customer Churn em Operadoras de Telecom
customer-churn binary-classification logistic-regression cross-validation linear-discriminant-analysis knn gaussian-naive-bayes cart svm-classifier accuracy auc variance-inflation-factor vif imbalanced-data normalization standardization exploratory-data-analysis grid-search-hyperparameters python machine-learning
Language:Jupyter Notebook 1
Mcamin / Disaster-Response-Pipeline
ETL Pipeline / ML Pipeline of Disaster Data provided by figure8
sqllite3 etl-pipeline grid-search-hyperparameters data-engineering-pipeline disaster-data pipeline
Language:Jupyter Notebook 1
rochitasundar / Classification-booking-cancelation-prediction-StarHotels
The aim is to develop an ML- based predictive classification model (logistic regression & decision trees) to predict which hotel booking is likely to be canceled. This is done by analysing different attributes of customer's booking details. Being able to predict accurately in advance if a booking is likely to be canceled will help formulate profitable policies for cancelations & refunds.
logistic-regression sklearn-library statsmodels scipy-stats variance-inflation-factor chi-squared-test roc-auc-curve precision-recall decision-tree-classifier confusion-matrix grid-search-hyperparameters cost-complexity-pruning
Language:Jupyter Notebook 1
sa5r / Hotel-Guest-Prediction
Hotel booking cancellation prediction model
hotel grid-search-hyperparameters hotel-booking kaggle machine-learning preprocessing scikit-learn support-vector-machine uga
Language:Jupyter Notebook 1
sorna-fast / Car-price-prediction-exercise-with-regression-model
Car price forecasting with one-variable, two-variable, three-variable, lasso, ridge, and elastic regression models
elastic-net grid-search-hyperparameters jupyter-notebook lasso-regression matplotlib numpy pandas pandas-dataframe pandas-python plotly regression regression-models ridge-regression
Language:Jupyter Notebook 1
vaitybharati / Forecasting_Model_Arima
Persistence/ Base model, ARIMA Hyperparameters, Grid search for p,d,q values, Build Model based on the optimized values, Combine train and test data and build final model
python forecasting model-arima arima-hyperparameters grid-search-hyperparameters statsmodels
Language:Jupyter Notebook 1
manjugovindarajan / EasyVisa-US-visa-applicant-profiling-using-ML
Analyze data of US work Visa applicants, build a predictive model to facilitate approvals, and based on factors that significantly influence visa status, recommend profiles for whom visa should be certified or denied.
adaboost-classifier decision-tree-classifier f1-score gradient-boosting-classifier grid-search-hyperparameters gridsearch-crossvalidation precision-recall random-forest-classifier stacking-ensemble xgboost-classifier
Language:Jupyter Notebook 0
MohamedLotfy989 / Credit-Card-Fraud-Detection
This repository focuses on credit card fraud detection using machine learning models, addressing class imbalance with SMOTE & undersampling, and optimizing performance via Grid Search & RandomizedSearchCV. It explores Logistic Regression, Random Forest, Voting Classifier, and XGBoost. balancing precision-recall trade-offs for fraud detection.
classic-machine-learning credit-card-fraud ensemble-learning fraud-detection grid-search-hyperparameters hyperparameter-tuning imbalanced-data logistic-regression precision-recall random-forest randomizedsearchcv smote threshold-tuning undersampling voting-classifier xgboost
Language:Jupyter Notebook 0
nazgol-nikravesh / DEPTs
DEPTs: Parameter tuning for software fault prediction with different variants of differential evolution *** Parameter tuners for software analytics problems ***
cart defect-prediction differential-evolution grid-search grid-search-hyperparameters hyperparameter-optimization knn-classification parameter-tuning random-forest random-search-optimization random-seed svm-classifier
Language:Python 0
Rizasaurus / Car-price-prediction-exercise-with-regression-model
Car price forecasting with one-variable, two-variable, three-variable, lasso, ridge, and elastic regression models
elastic-net grid-search-hyperparameters jupyter-notebook lasso-regression matplotlib numpy pandas pandas-dataframe pandas-python plotly regression regression-models ridge-regression
0
alejimgon / Housing-Prices-Kaggle-Competition-Model
This repository contains my current model for the Housing Prices Kaggle competition.
artificial-neural-networks autoencoder data-preprocessing grid-search-hyperparameters k-fold-cross-validation machine-learning pca python random-forest-regressor recursive-feature-elimination stacking-ensemble
Language:Python
tejb96 / ml-pipelines
This repository contains code for building a machine learning model to predict NFL players' selection for the Pro Bowl based on player statistics from the NFL Pro Bowl 2022 dataset. Three different supervised learning models, including one linear and two non-linear models, are implemented.
data-analysis grid-search-hyperparameters machine-learning pipeline-optimization predictive-modeling python supervised-learning
Language:Jupyter Notebook

grid-search-hyperparameters

AliAmini93 / Fault-Detection-in-DC-microgrids

uzunb / house-prices-prediction-LGBM

sanjeevai / disaster-response-pipeline

ShagunSharma98 / Global-Structural-Earthquake-Damage-Prediction

bhattbhavesh91 / decision_tree_grid_search

sandipanpaul21 / Tree-Based-Models-in-Python

asaficontact / project_floodlight

SadmanSakib93 / ANN-Stratified-K-Fold-Cross-Validation-Keras-Tensorflow

rochitasundar / Customer-profiling-using-ML-EasyVisa

aanchal1308 / disease-diagnosis-ML

eakgun / ISO_OLS_RBF

SwamiKannan / Post-Graduate-Program-AI-ML

JeremieGince / AutoMLpy

danielxu04 / credit-card-approvals

srinithya-halaharvi / Analyzing_Predicting_Election_Outcomes_Indian_Politics

yjeong5126 / housing_prices

AmirHHasani / Auto-tuning-of-Classic-Machine-Learning-Models-Hyperparameters-Using-Scikit-Learn

angeloruggieridj / MLPClassifier-with-GridSearchCV-Iris

anshul1004 / MachineLearningClassificationModels

GabbyOlivares / Machine-Learning-Challenge

glubbdubdrib / lazygrid

imehrdadmahdavi / grid-search

joeanton719 / useful_codes

mafda / disaster_response_pipeline

marinafajardo / prevendo-customer-churn

Mcamin / Disaster-Response-Pipeline

rochitasundar / Classification-booking-cancelation-prediction-StarHotels

sa5r / Hotel-Guest-Prediction

sorna-fast / Car-price-prediction-exercise-with-regression-model

vaitybharati / Forecasting_Model_Arima

manjugovindarajan / EasyVisa-US-visa-applicant-profiling-using-ML

MohamedLotfy989 / Credit-Card-Fraud-Detection

nazgol-nikravesh / DEPTs

Rizasaurus / Car-price-prediction-exercise-with-regression-model

alejimgon / Housing-Prices-Kaggle-Competition-Model

tejb96 / ml-pipelines