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 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…

A Machine Learning Model built in scikit-learn using Support Vector Regressors, Ensemble modeling with Gradient Boost Regressor and Grid Search Cross Validation.

💚 A heart disease classifier using 4 SVM kernels and decision trees, with PCA, ROC, pruning, grid search cv, confusion matrix, and more

This repo contains all machine learning algorithms using python and scikit-learn

Experimental using on Iris dataset of MultiLayerPerceptron (MLP) tested with GridSearch on parameter space and Cross Validation for testing results.

Improving a Machine Learning Model

Built Random Forest classifier from scratch on top of Scikit Learn decision trees. Using Scikit Learn to create data cleaning pipelines, perform grid searches for hyper parameter tuning, and decision tree modeling

SVM

This repository contains all the work projects carried out with respect to learning and experiments on Big Data Analytics. The scripts are formed to build machine learning models for future predictions.

This project is made for search the best regression model based in some metrics training some models and evaluating them

I leveraged an algorithmic approach for document classification and document clustering. Various models have been trained for document classification and they all have been evaluated using performance metrics followed by tuning of the model hyper-parameters to reach the most accurate classification. Additionally, a model has been trained for doc…

Applied SVC classifier and Logistic Regression classifier algorithm onto credit card transaction dataset to detect any fraud.

Classification of fetal cardiotocography to determine whether pattern is normal, suspect or pathalogical.

Marketing Data Analysis Project

Customer Churn Prediction

Retail data analysis using machine learning techniques in Python and Sklearn packages to help Ugly Christmas Party determine how many sweaters to order for each ugly Christmas sweater design created.

GridSearchCV For Model optimization

Machine Learning with Python