🏠 House Price Prediction — Random Forest ML App An end-to-end machine learning web app that predicts house prices using a Random Forest Regressor, built with Python and deployed via an interactive Streamlit UI.
🎯 Objective Predict house prices based on 12 property features including bedrooms, bathrooms, sqft, location, and renovation year — with real-time prediction on user-defined input data.
🚀 Key Features
🤖 Random Forest Regressor with 500 estimators for high accuracy 🏷️ Label Encoding for categorical city data 📊 Model evaluation using R² Score and MAE 🎛️ Interactive Streamlit UI for real-time custom predictions 📈 Dual-mode Chart Dashboard:
Predefined Charts — Bedroom count, Price vs Sqft, Monthly trend, Top 10 cities by avg price Custom Chart Builder — Bar, Horizontal Bar, Line, Scatter, Histogram with dynamic aggregation controls
🛠️ Tech Stack ToolPurposePythonCore programming languageScikit-learnRandom Forest model & preprocessingPandas & NumPyData manipulationMatplotlibData visualizationStreamlitInteractive web app UIKaggleDataset source
🧠 ML Model Details ParameterValueAlgorithmRandom Forest RegressorEstimators500Test Size30%Train Size70%Evaluation MetricsR² Score, MAEEncodingLabel Encoding (city column)
📁 Features Used for Prediction bedrooms, bathrooms, sqft_living, sqft_lot, floors, view, condition, sqft_above, sqft_basement, yr_built, yr_renovated, city
📁 Project Structure house-price-prediction-rf/ │ ├── rf_mini_project.py # Main Streamlit app ├── requirements.txt # Dependencies └── README.md # Project documentation
Clone the repository
bashgit clone https://github.com/Shiva-keerth/house-price-prediction-rf.git cd house-price-prediction-rf
Install dependencies
bashpip install -r requirements.txt
Download the dataset from Kaggle and place it in the project folder Run the Streamlit app
bashstreamlit run rf_mini_project.py
📊 Outcome
Built a fully interactive ML web app with real-time price prediction Custom Chart Builder allows dynamic exploration of the dataset Demonstrates end-to-end ML pipeline: Data → Preprocessing → Model → Deployment
Shiva Keerth G
📧 gantishivakeerth@gmail.com
🔗 GitHub | LinkedIn