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Machine Learning Project to Predict House Prices in Bangalore.
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.
livabl real-estate data scraper
A simple tool for investors to find cash-flowing properties in Canada. Scans target cities, underwrites with a conservative cash-flow model, and ranks deals by margin of safety.
Institutional-grade liquidity and capital efficiency model for Dubai real estate using DLD transaction data. Detects rotation phases, exit velocity shifts, and structural risk clusters to support data-driven capital allocation decisions via advanced Power BI analytics.
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
real estate python pipeline with clean data automation
perth property data
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
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 multiple linear regression project to predict house prices using numerical property and neighborhood features, with model evaluation using R², MAE, and RMSE.
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. 🚀📊🏡
Automated Commercial Property Analytics Pipeline: Python ETL, SQL Modelling, and Power BI Dashboard
Real estate market data MCP server — connect Claude, Cursor, or any AI assistant to 895 US markets
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.
Zillow price history data
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