lgbm-regressor

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.

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

Forecasting % Baseline, a measure of solar panel energy output, using weather and solar irradiance data.

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

This project builds an **end-to-end machine learning regression system** to predict the **total number of bike rentals (`cnt`)** using historical data and multiple influencing factors.

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

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

Flight price prediction using ML — EDA, preprocessing pipelines, and tuned XGBoost/LightGBM/Random Forest regressors in scikit-learn.

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

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

Code of Kaggle Projects

High-frequency IV forecasting for ETH using LightGBM and cross-asset order book features

Honors Thesis Research

Advanced Predictive Analytics for Building Efficiency

Purchase prediction for Google Merchandise Store visitors

Проектная работа по продаже автомобилей

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

🎶Comparative analysis of ensemble regression algorithms for predicting song popularity using Spotify Web API audio features.

The model aimed at prediction of employee's salary