grid-search-hyperparameters | Topic

Topic: "grid-search-hyperparameters"

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.

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sanjeevai/disaster-response-pipeline

ETL pipeline combined with supervised learning and grid search to classify text messages sent during a disaster event

Language: Python - Size: 73.9 MB - Last synced at: about 2 years ago - Pushed at: about 6 years ago - Stars: 16 - Forks: 12

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.

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bhattbhavesh91/decision_tree_grid_search

Implementation of Grid Search to find better hyper-parameters for decision tree to reduce the over fitting.

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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.

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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.

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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.

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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.

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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.

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aanchal1308/disease-diagnosis-ML

Machine learning models for detection of diseases.

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SwamiKannan/Post-Graduate-Program-AI-ML

Programming assignments completed in the PG Program for AI ML

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JeremieGince/AutoMLpy

This package is an automatic machine learning module whose function is to optimize the hyper-parameters of an automatic learning model.

Language: Python - Size: 13.4 MB - Last synced at: about 1 month ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 0

danielxu04/credit-card-approvals

A logistic regression model that predicts whether or not a credit card application will get approved using SciKit.

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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

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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

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eakgun/ISO_OLS_RBF

Generalized Improved Second Order RBF Neural Network with Center Selection using OLS

Language: MATLAB - Size: 2.09 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

sa5r/Hotel-Guest-Prediction

Hotel booking cancellation prediction model

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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

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joeanton719/useful_codes

codes related to hyperparameter tuning and some classes, functions, etc. I have created to optmize classification problems (Continuously being updated ).

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glubbdubdrib/lazygrid

Automatic, efficient and flexible implementation of complex machine learning pipeline generation and cross-validation.

Language: Python - Size: 781 KB - Last synced at: 7 months ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 0

GabbyOlivares/Machine-Learning-Challenge

16. Exoplanet Exploration - Machine Learning Challenge

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marinafajardo/prevendo-customer-churn

Prevendo Customer Churn em Operadoras de Telecom

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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

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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.

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anshul1004/MachineLearningClassificationModels

Implementation of various machine learning models in scikit-learn

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Mcamin/Disaster-Response-Pipeline

ETL Pipeline / ML Pipeline of Disaster Data provided by figure8

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imehrdadmahdavi/grid-search

Using Grid Search to improve Machine Learning models

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mnochtioui/House-Prices---Advanced-Regression-Techniques

Detailed walkthrough of a data science project for the Kaggle House Prices challenge, covering data cleaning, EDA, feature engineering, and regression modeling.

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

Car price forecasting with one-variable, two-variable, three-variable, lasso, ridge, and elastic regression models

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alejimgon/Housing-Prices-Kaggle-Competition-Model

This repository contains my current model for the Housing Prices Kaggle competition.

Language: Python - Size: 27.3 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

Mahsa-Zf/Mahsa-Zf

Here are some of my projects on different topics!

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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.

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nazgol-nikravesh/DEPTs

DEPTs: Parameter tuning for software fault prediction with different variants of differential evolution *** Parameter tuners for software analytics problems ***

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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).

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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.

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Shuhaib-Ahamed/Email-Spam-Classification

Email spam classification for Naive Bayes, Gradient Boosting Machine, Support Vector Machine and Random Forest

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manasik29/Assignment_Prediction_ForestFires_BurnedArea

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

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AmishaMurarka/Facebook-Comment-Volume-Prediction

Data in the social networking services is increasing day by day. So, there is heavy requirement to study the highly dynamic behavior of the users towards these services. The task here is to estimate the comment count that a post is expected to receive in next few(H) hours. Data has been scraped from one of the most popular social networking sites - Facebook.

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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.

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Topic: "grid-search-hyperparameters"

jiandanjinxin/tf-dev-summit-tensorboard-tutorial Fork of martinwicke/tf-dev-summit-tensorboard-tutorial