Smart property valuation system using XGBoost machine learning for accurate house price predictions based on 13 real estate features

A comprehensive R-based data analysis project that examines housing rental patterns across multiple cities, utilizing statistical methods and visualization techniques to analyze 4,746 properties' data points including rent prices, locations, and amenities. The project employs various R libraries to clean, process, and visualize rental market trends

UrbanSphere is a powerful tool designed to assist potential buyers and residents by evaluating and ranking sub-districts of cities. It uses diverse parameters such as air quality, water quality, crime rate, and proximity to essential facilities to highlight the best areas for your needs.

Collection of interactive Tableau dashboards showcasing data visualization expertise across entertainment, real estate, and aviation industries. Features Netflix content analysis, housing market trends, and British Airways customer satisfaction metrics with advanced filtering and drill-down capabilities.

This project predicts house prices in Bengaluru using multiple regression techniques. The goal is to build a machine learning model that takes in various features like location, size, number of bedrooms, and area, and outputs an estimated price of the property. 🔧 Models

real estate python pipeline with clean data automation

real estate property data extractor

Data-driven analysis of the Ames Housing Dataset, combining advanced feature engineering and Stochastic Gradient Descent (SGD) regression model tuning. This repository showcases predictive modeling, hyperparameter optimization, and actionable insights for real estate analytics.

House Price Prediction is a machine learning project that analyzes real estate data to predict house prices based on various features like location, size, and amenities. It involves data preprocessing, exploratory data analysis (EDA), feature engineering, and model training using regression algorithms to provide accurate price estimates. 🚀📊🏡

livabl real-estate data scraper

Download unlimited Zillow listings for free, with no watermark and no registration required using our easy-to-use tool.

AirbnbNewYorkCityAnalysis is a comprehensive data analysis and visualization project exploring short-term Airbnb rental trends across New York City (2008–2022). Using open source Airbnb data, the project combines data cleaning, statistical summaries, and Tableau dashboards to uncover pricing patterns, borough level distribution, and insights.

End-to-end data science project on King County house sales. Covers data cleaning, EDA, visualization, feature engineering, and regression modeling to predict housing prices. Showcases core skills in Python, ML, and insight communication

UK property listings and agent data extractor

UK energy certificate crawler

🏙️ 2buyornot2buy: The Parker Luxury Condo Buy Signal Machine Learning and In-Depth Financial Analysis. Turning a rainy day from angst to analytical joy. This is what happens when you approach real-world life decisions analytically, you know, just for fun.

This project explores neighbourhood dynamics, pricing behaviour & host performance of Airbnb in Brisbane