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 candidates more likely to have the visa certified.

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.

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

Improving a Machine Learning Model

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

A churn bank model that classify the customers if they stop dealing with the bank or not

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.

A powerful stacked ensemble model for income prediction, combining GradientBoosting, AdaBoost, Bagging, Linear Regression, and Decision Trees. Achieves an impressive R² of 0.8761 on the RoS_sample_submission dataset.

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

Customer Churn Prediction

This notebook demonstrates an end-to-end, reproducible ML workflow with business-oriented communication: clear EDA, rigorous CV & hyperparameter tuning, interpretable feature importances, visual diagnostics, and an exported pipeline ready for production validation.

👩🏻‍🍳🍽️Restaurant Success Prediction using ML

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 document clustering, which is followed by a dimensionality reduction technique to visualize the document clusters in 2D space.

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

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

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

Marketing Data Analysis Project

Text Mining Competition

Applied Machine Learning ( End to End ) Classification Models

Machine Learning with Python

A simple implementation of the Logistic Regression Classifier on the Breast Cancer Dataset with L1 regularization and GridSearch for hyperparameter tuning.

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.

A regression model to predict housing prices based on various features.

Bu projedeki amaç bir şirketin kısa vadeli borçlarını ödeyebilme kapasitesini gösteren metriğin aşağıda açıklanmış belirli bağımsız değişkenlere göre bir doğrusal regresyon modeliyle tahmin edilmesidir.