There are 1 repository under grid-search-hyperparameters topic.
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.
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.
ETL pipeline combined with supervised learning and grid search to classify text messages sent during a disaster event
Using deep learning techniques like 1D and 2D CNNs, LSTM to detect damage in a structure with hinges/joints after an earthquake.
Implementation of Grid Search to find better hyper-parameters for decision tree to reduce the over fitting.
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.
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.
This repo contains examples of binary classification with ANN and hyper-parameter tuning with grid search.
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.
Machine learning models for detection of diseases.
Generalized Improved Second Order RBF Neural Network with Center Selection using OLS
Programming assignments completed in the PG Program for AI ML
This package is an automatic machine learning module whose function is to optimize the hyper-parameters of an automatic learning model.
A logistic regression model that predicts whether or not a credit card application will get approved using SciKit.
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).
Analysis and prediction for the housing market prices using Cross Validation and Grid Search in several regression models
Using Scikit-Learn to optimize some of the hyperparameters of Classic ML Models
Experimental using on Iris dataset of MultiLayerPerceptron (MLP) tested with GridSearch on parameter space and Cross Validation for testing results.
Implementation of various machine learning models in scikit-learn
16. Exoplanet Exploration - Machine Learning Challenge
Automatic, efficient and flexible implementation of complex machine learning pipeline generation and cross-validation.
Using Grid Search to improve Machine Learning models
codes related to hyperparameter tuning and some classes, functions, etc. I have created to optmize classification problems (Continuously being updated ).
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
Prevendo Customer Churn em Operadoras de Telecom
ETL Pipeline / ML Pipeline of Disaster Data provided by figure8
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.
Hotel booking cancellation prediction model
Car price forecasting with one-variable, two-variable, three-variable, lasso, ridge, and elastic regression models
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
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.
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.
DEPTs: Parameter tuning for software fault prediction with different variants of differential evolution *** Parameter tuners for software analytics problems ***
Car price forecasting with one-variable, two-variable, three-variable, lasso, ridge, and elastic regression models
This repository contains my current model for the Housing Prices Kaggle competition.
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.