abdulmumeen-abdullahi/Nigeria-House-Price-Predictive-Model

I trained a Random Forest Regressor model to predict house prices in Nigeria, focusing on properties like Detached Duplex, Terraced Duplex, Semi-Detached Duplex, and Detached Bungalow. # 🏡 Nigeria House Price Predictive Model This project develops a predictive model to accurately estimate the prices of luxury homes in Nigeria. The final product is a Random Forest Regressor integrated into an interactive ipywidgets dashboard within a Jupyter Notebook, allowing users to get real-time price predictions by adjusting property features. ## 🚩 Problem Statement The Nigerian luxury real estate market is booming, but accurately pricing properties remains a major challenge for realtors and investors. Setting a price too high can deter potential buyers, while pricing too low results in lost revenue. This project aims to solve this by building a data-driven tool that provides reliable price estimates based on key property attributes, helping stakeholders make more informed and profitable decisions. ## 🛠️ Tech Stack & Features ### Core Technologies: * `Python` * `Pandas` & `NumPy` for data cleaning and manipulation. * `Scikit-learn` for machine learning (RandomForestRegressor, OneHotEncoder). * `Matplotlib` & `Seaborn` for data visualization. ### Interactivity: * `ipywidgets` for creating the interactive prediction dashboard in Jupyter. ### Key Features: * **Accurate Price Prediction:** A fine-tuned Random Forest model that predicts house prices in millions of Nigerian Naira (NGN). * **Interactive Widget:** A user-friendly dashboard with dropdowns and sliders to explore how different features affect house prices. * **Feature Importance Analysis:** Identifies the most significant drivers of property value. ## 🚀 Installation & Usage To run the interactive notebook locally, please follow these steps: 1. **Clone the repository:** ```bash git clone github.com cd Nigeria-House-Price-Predictive-Model ``` 2. **Run the Jupyter Notebook:** Launch the notebook `Nigeria House Price Predictive Model.ipynb` and run the cells. The interactive prediction widget will be displayed at the end …