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.

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

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

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).

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

Here are some of my projects on different topics!

Hotel booking cancellation prediction model

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

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. Thi…

Programming assignments completed in the PG Program for AI ML

Using Scikit-Learn to optimize some of the hyperparameters of Classic ML Models

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

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 site…

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.

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 prof…

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 candidate…

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.

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

This repo contains examples of binary classification with ANN and hyper-parameter tuning with grid search.

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