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

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.

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

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.

Data Pre Processing in Python using a House Rent Project. Run in Jupyter Notebook. In this Project I do Data Preprocessing in that I clean Data & shows it in relevant manner

Automated Commercial Property Analytics Pipeline: Python ETL, SQL Modelling, and Power BI Dashboard

Enterprise-grade real estate price intelligence platform implementing an end-to-end ML pipeline with advanced feature engineering, Gradient Boosting ensembles, cross-validated model evaluation, hyperparameter optimization, serialized model artifacts, automated inference, and Kaggle-grade batch prediction for production-ready valuation analytics.

A multiple linear regression project to predict house prices using numerical property and neighborhood features, with model evaluation using R², MAE, and RMSE.

real estate python pipeline with clean data automation

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

A dual-mode Real Estate Intelligence Dashboard for Zimbabwe's top developers. Features automated Board Reporting (PDF), Leasing Feasibility Simulations, and Revenue Risk Detection using Python & Streamlit.

real estate property data extractor

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

UK energy certificate crawler

Zillow price history data

Multimodal property valuation system combining satellite imagery and structured housing data. Uses CNN-based visual embeddings + tabular features to predict real estate prices, with Grad-CAM explainability for spatial insights.

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

livabl real-estate data scraper

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.

Interactive Tableau dashboard analyzing King County real estate trends. Visualizes daily average sales prices, property condition heatmaps, and distribution metrics for bedrooms and bathrooms.