Taiwo11/nigeria-house-price-prediction-model-v2
Domaine:
socioeconomicType de record:
model
Créateur:
Tai
Hôte:
# Nigeria House Price Prediction (ML + Clustering + Streamlit)
> 🚀 This is the advanced rebuild (v2) of my original ML project.
> 📌 View the original version (v1) here: Nigerian House Price Prediction – v1
---
## 📘 Project Overview
The Nigerian real estate market is growing rapidly, yet pricing remains inconsistent due to a lack of standardized valuation models.
This project aims to build a machine learning–based predictive model that estimates house prices using historical property listing data from across Nigeria.
### 🎯 Goal
To develop a data-driven pricing system that can help buyers, sellers, and developers make more informed decisions.
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## 🚀 Key Features
- Predict house prices using Linear Regression and Random Forest
- Compare model outputs
- Segment properties using K-Means clustering
- Deploy interactive prediction interface using Streamlit
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## Project Structure
```
nigeria-house-price-prediction-model/
│
├── data/
│ ├── raw/ # Original dataset
│ └── processed/ # Cleaned data
│
├── notebooks/ # EDA and experimentation
│ ├── 01_eda.ipynb
│ ├── 02_model_training.ipynb
│ └── 03_clustering.ipynb
│
├── src/ # Core ML pipeline
│ ├── preprocess.py
│ ├── train.py
│ ├── predict.py
│ └── clustering.py
│
├── app/ # Streamlit deployment
│ └── streamlit_app.py
│
├── models/ # Saved trained models
│ ├── linear_regression.pkl
│ ├── random_forest.pkl
│ ├── scaler.pkl
│ └── kmeans.pkl
│
├── requirements.txt
├── README.md
└── .gitignore
```
---
## Models Used
### ✅ Regression (Price Prediction)
- **Linear Regression**
- **Random Forest Regressor**
### ✅ Unsupervised Learning (Segmentation)
- **K-Means Clustering**
- Groups properties into similar market segments
- Helps interpret pricing patterns beyond prediction
---
## Streamlit App Features
- Predict house price from user inputs
- Compare predictions from Linear Regression vs Ra …