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.

Multi-Class Obesity Risk Prediction Project | Prediction of obesity risk in individuals using various factors, which is related to cardiovascular disease.

Mohs Hardness Prediction Project | Ensemble Models with Neural Networks, LGBM, CAT, XGB using a Voting Mechanism. 🚀💎

My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.

Machine Learning model for price prediction using an ensemble of four different regression methods.

Customers knowledge, supply chain movement and sales forecasting, Customer Lifetime value, churn and survival analysis

Project: What factors impact the accuracy of airfare prediction?

l train and evaluate multiple time-series forecasting models using the Store Item Demand Forecasting Challenge dataset from Kaggle. This dataset has 10 different stores and each store has 50 items, i.e. total of 500 daily level time series data for five years (2013–2017).

ASHRAE - Great Energy Predictor III: How much energy will a building consume?

Data science project on Housing Prices Dataset regression analysis

Prediction of the sale price of a vehicle using predictive models using gradient boosting

Primary aim of this project is to build machine learning model that should be able to predict the solar power output of the 12 different location of the Northern Hemisphere according to the provided dataset.

Kaggle Competition : Predicting house prices using a collection of advanced regression techniques and data visualization with plotly

Honors Thesis Research

Rusty Bargain is a used car buying and selling company that is developing an app to attract new buyers. My job as data science is to create a model that can determine the market value of a car.

Creating a ML-model that forecasts the price of a car (historic data)

Compare ticket price and distance between car, train and bus + Price prediction + dynamic visualization