xgboost-regressor

House price estimation from visual and textual features using both machine learning and deep learning models

This project predicts house prices using machine learning with XGBoost on the Kaggle House Prices dataset. It applies data preprocessing, feature engineering, and log transformation to achieve strong predictive performance and a competitive RMSLE score.

Worked on AFLW2000-3D dataset which is a dataset of 2000 images. The regression model of predicting the 3 angles (pitch - yaw - roll) of head pose estimation was XGboost Regressor.

Notebooks for Kaggle competition

Real-time household energy consumption forecasting system powered by XGBoost and FastAPI. Includes MLOps automation with DVC, MLflow, Prefect, and Streamlit dashboard.

A machine learning web app that predicts house prices across 5 major cities of Pakistan. It uses features like location, property type, area, bedrooms, and bathrooms to give an estimated price. The model achieves an impressive R² score of 99.9%, showing how accurate the predictions are.

A regression model to predict calories burnt using values from multiple sensors.

Built an ATM cash forecasting system using Python and machine learning to predict daily cash demand from historical transaction data. The project includes data preprocessing, model training, and a Flask web application that allows users to input parameters and generate real-time cash demand predictions to support efficient ATM cash management.

This repo hosts an end-to-end machine learning project designed to cover the full lifecycle of a data science initiative. The project encompasses a comprehensive approach including data Ingestion, preprocessing, exploratory data analysis (EDA), feature engineering, model training and evaluation, hyperparameter tuning, and cloud deployment.

Student Performance Predictor is an end-to-end machine learning project that implements a complete predictive modeling pipeline. It analyzes the impact of demographic, socioeconomic, and academic factors on student mathematics performance, performing data preprocessing, feature engineering, machine learning model & deployment using Flask & Render.

Doing Analysis of the sales of video games across the globe and predicting the sales using various Machine Learning Algorithms

Evaluate the robustness and performance between ML and DL models in predicting the CPC concentration under various image capturing devices, types of input image datasets, and lighting conditions. The findings in our current study can overcome the bottleneck by eliminating the need for laborious manual extraction processes and reducing the time and

Quick cheatsheet about XGBoost, a Gradient Boosted regularized technique published in 2014

🎬 Predictive Analytics: Forecasting Netflix Title Performance using a Random Forest Model

A person’s creditworthiness is often associated (conversely) with the likelihood they may default on loans.

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

Prediciting the Prices of House using the Boston House Price Dataset by applying the XGBoost Regressor Model

This project predicts house prices using machine learning algorithms based on housing features. It includes data preprocessing, exploratory analysis, model training, and regression techniques to generate accurate price predictions...

2022-01 데이터마이닝이론및응용 프로젝트 <장애인 이동권 제고를 위한 콜택시 이용편의 증진 방안 : 서울특별시를 중심으로>

A machine learning-based web app to predict the price of used cars in India based on various features like brand, model, location, fuel type, and more. Built with Streamlit for an interactive user interface and powered by an XGBoost (multiple-non-linear-regression) model for accurate predictions.