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A California house price prediction system using Random Forest regression with scikit-learn pipelines for preprocessing, featuring model comparison (Linear Regression, Decision Tree, Random Forest), stratified sampling, and automated training/inference workflows.

Predict California median house prices using ML regression models. Includes data preprocessing, training Linear Regression, Decision Tree, Random Forest, hyperparameter tuning, evaluation metrics, and visualization. Modular code ideal for learning and extending housing price prediction.

House Price Prediction using Multiple Regression Models. This project applies Linear Regression, Ridge Regression, and Decision Tree models on the California Housing dataset with data preprocessing, feature scaling, train-test splitting, and performance evaluation using MSE and R² metrics.

Developed a California housing price prediction model using supervised machine learning. Analyzed the relationship between per capita income and house prices. Implemented Linear Regression and Decision Tree models, achieving ~99% accuracy. Built using Python, Pandas, Scikit-learn, and data visualization tools.

Developed a California housing price prediction model using supervised machine learning. Analyzed the relationship between per capita income and house prices. Implemented Linear Regression and Decision Tree models, achieving ~99% accuracy. Built using Python, Pandas, Scikit-learn, and data visualization tools.

Developed a California housing price prediction model using supervised machine learning. Analyzed the relationship between per capita income and house prices. Implemented Linear Regression and Decision Tree models, achieving 99% accuracy. Built using Python, Pandas, Scikit-learn, and data visualization tools.

Developed a California housing price prediction model using supervised machine learning. Analyzed the relationship between per capita income and house prices. Implemented Linear Regression and Decision Tree models, achieving 99% accuracy. Built using Python, Pandas, Scikit-learn, and data visualization tools.

Predict house prices using California housing data. The project includes data cleaning, feature scaling, and model training with Linear Regression, Decision Tree, and Random Forest. It features a Gradio interface for real-time price prediction based on user inputs. Accurate, fast, and user-friendly.