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.

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

Kaggle Competetion problem solution. https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/code

It is the detailed collection of house listings from various cities and regions in Bangladesh, with a specific focus on Dhaka and Chittagong. It encompasses essential details such as location, property type, size, amenities, and pricing.

Healthcare data

Code of Kaggle Projects

Predicts the fare amount of Uber rides based on various factors such as pickup/drop-off coordinates, passenger count, and trip distance.

Honors Thesis Research

Perform an analysis on a film database to guide PProductions on which type of film should be developed next by predicting IMDb ratings.

Purchase prediction for Google Merchandise Store visitors

Exploratory data analysis and supervised learning analysis on the Ames Housing Prices dataset.

Predicting the number of calories burned based on individual biometric and activity features using advanced ensemble machine learning techniques.

The model aimed at prediction of employee's salary

Zomato restoran veri setini kullanarak restoran yemek puanlarını tahmin etmeyi amaçlayan bir makine öğrenmesi çalışmasıdır.