Obananob/Nigeria-House-Price-Prediction

# Nigerian House Price Predictor ## Project Overview This project applies Machine Learning to predict real estate prices in Nigeria. Using a dataset of Nigerian housing listings, I built a **Gradient Boosting Regressor** model that estimates prices based on location, house type, and amenities (bedrooms, parking, etc.). The project includes a **Streamlit Web App** for easy interaction. ## Features - **Data Cleaning:** Removed extreme outliers (e.g., erroneous Trillion Naira listings). - **EDA:** Visualized market trends, price distribution, and location impact. - **Model:** Trained a Gradient Boosting model with **~60% Accuracy**. - **App:** User-friendly interface to get instant price predictions. ## File Structure - `app.py`: The Streamlit application code. - `notebook.ipynb`: The Jupyter Notebook containing all Data Cleaning, EDA, and Training steps. - `requirements.txt`: List of dependencies. - `*.pkl`: Saved model files. ## How to Run 1. Clone the repository. 2. Install dependencies: `pip install -r requirements.txt` 3. Run the app: `streamlit run app.py` ## 📊 Key Insights - **Town** is the #1 driver of price in Nigeria. - The market is heavily **Right-Skewed**, with a vast difference between standard and luxury homes.