Bikis-Biset16/ethiopian-housing-price-predictor

Interactive Flask web application for house price estimation using trained machine learning pipelines and real-time form persistence. # 🏡 House Price Prediction Web Application An interactive, end-to-end Machine Learning web application built with **Flask**, **Pandas**, and **Scikit-Learn**. The application estimates property valuations based on key structural features, spatial dimensions, and geographic proximities. --- ## 🛠️ Tech Stack * **Backend:** Python, Flask, Jinja2 * **Machine Learning:** Scikit-Learn, Joblib, Pandas, NumPy * **Frontend:** HTML5, CSS3 --- ## ✨ Key Features * **Multi-Model Inference:** Allows users to select and test predictions against different trained regression models (e.g., Gradient Boosting, Random Forest). * **Stateful Input Retention:** Form inputs dynamically persist after submission via Jinja2 templates, making scenario testing fast and seamless. * **Safe Request Handling:** Graceful error handling and fallback defaults to prevent server crashes on bad inputs. * **Pipeline Integration:** Automated feature transformation and handling of categorical data using Scikit-Learn pipelines. --- ## 📁 Project Structure ```text Hou_price_prediction_Dash/ ├── app.py # Flask backend and route definitions ├── house_price_model (2).pkl # Serialized Scikit-Learn pipeline model ├── templates/ │ └── index.html # HTML form UI with Jinja2 logic ├── .gitignore # Git untracked files specification └── README.md # Project documentation