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

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

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

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.

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.

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

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

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

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

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

Machine learning model for predicting compressive strength of fiber-reinforced concrete with waste rubber.

Machine learning for predicting relevance of scientific abstracts using TF-IDF, SMOTE, regression and classification models

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

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.

Build a Machine Learning model to predict the total count of cabs booked in each hour by the new data. Research on Cyclic features

Predicting house prices in Boston using the XGBoost regressor model.

In this project, a regression-based performance prediction model was developed to estimate building energy consumption based on simplified façade attribute information and weather conditions.