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🏠 California Housing Price Prediction - ML model with Streamlit web app for predicting median house values using Random Forest regression

Interactive Streamlit app predicting California house prices using the 1990 U.S. Census dataset. Features real-time price predictions with Linear Regression, based on median income, house age, and average rooms, plus insightful visualizations of key housing data.

Linear Regression project to predict California house prices using gradient descent and Streamlit app for live predictions.

🏠 California House Price Prediction — ML model + Streamlit web app that predicts house prices using HistGradientBoosting. Features EDA, feature engineering, model comparison and live deployment.

🏠 ML-powered house price prediction app using Random Forest and Streamlit. Predict California housing prices based on income, location, room stats, and more.

House Price Prediction web app using Linear Regression. Trained on the California Housing dataset with scaled features. Built with Python, scikit-learn, and Streamlit. Allows users to input property details and instantly get predicted house prices.

This project is a Machine learning regression pipeline to predict California house prices using the California Housing Dataset. It includes complete preprocessing, model tuning, evaluation, and a deployed Streamlit web app for live predictions.

This project aims to build a machine learning model to predict house prices using the California Housing dataset. The model is deployed using a Streamlit app to provide an interactive interface for users to input house features and get price predictions.

California House Price Prediction is a Streamlit-powered web app that uses an XGBoost model to provide instant and accurate house price predictions based on key housing features. With a sleek UI and real-time predictions, it's fully deployable and easy to use! 🏡✨

This project predicts California house prices using key features like median income and house age. It includes data exploration, Linear Regression modeling, evaluation, and visualization. A Streamlit app lets users input values to get instant price predictions, making it practical and user-friendly.

A Machine Learning web app that predicts house prices using XGBoost and Streamlit. Built with a simple interface for easy use, it provides real-time price predictions in ₹ and visualizes key factors affecting prices. Trained on the California Housing dataset using Python and Scikit-learn.

Predict California house prices using a fully optimized regression pipeline. Includes preprocessing, feature scaling, hyperparameter-tuned XGBoost model, evaluation with RMSE, MAE, R², and an interactive Streamlit app for deployment-ready price predictions on user-provided inputs.

A regression project that predicts house prices in California using the California Housing dataset. The model is trained using Scikit-learn pipelines with preprocessing steps such as feature scaling and encoding. It also includes a deployed Streamlit web app for live predictions.

This project is a machine learning application that predicts California median house prices using the California Housing Dataset. A Random Forest Regressor was trained after data cleaning, encoding, and scaling. The predictions are displayed through an interactive Streamlit web app.