🏡 House Price Prediction App

Welcome to the House Price Prediction App, a user-friendly Streamlit application that predicts house prices based on various features. With an intuitive interface and an AI-powered backend, this app makes it easy to estimate property prices for better decision-making in real estate investments.

🌟 Features

• Interactive Interface: Easily input property features through an elegant sidebar.
• Real-Time Predictions: Get instant price predictions powered by a pre-trained machine learning model.
• Insightful Visuals: Understand the impact of different features on house prices.
• Responsive Design: Optimized for both desktop and mobile devices.

🚀 Getting Started

Prerequisites

Ensure you have Python 3.7 or later installed on your machine. You will also need pip for package management.

Installation

1. Clone the Repository

   git clone https://github.com/MrBroma/ImmoEliza_ML_app.git
   cd immoeliza_ml_app

2. Create and Activate a Virtual Environment

    python -m venv env

3. Install Dependencies

   pip install --upgrade pip
   pip install -r requirements.txt

4. Running the app

    streamlit run streamlit/app.py

Visit http://localhost:8501 in your web browser to view the app.

immoeliza_ml_app/
│
├── models/
│   ├── preprocessor.pkl       # Pre-trained data preprocessor
│   └── random_forest.pkl      # Pre-trained Random Forest model
│
├── data/
│   └── dataset_sales_cleaned.csv   # Dataset for predictions
│
├── streamlit/
│   ├── app.py                 # Main app script
│   └── style.css              # Custom CSS styles
│
├── requirements.txt           # Python dependencies
└── README.md                  # Project documentation

🏠 Streamlit Deployment

This app has been deployed on Streamlit.
https://immoeliza-ml-app-broma.streamlit.app

✨ Usage

Input Features

To get a prediction, input the following features in the sidebar:

• Number of Bedrooms
• Number of Bathrooms
• Construction Year
• District
• Garden: Yes/No
• Garden Area (in m²)
• Kitchen Type
• Living Area (in m²)
• Locality
• Number of Facades
• Province
• Region
• Number of Rooms
• Number of Showers
• Subtype of Property
• Surface of Plot (in m²)
• Swimming Pool: Yes/No
• Terrace: Yes/No
• PEB (Performance énergétique des bâtiments)
• State of Building
• Flooding Zone
• Furnished: Yes/No

Predicting House Prices

Click the "Predict Price" button in the sidebar after inputting the features. The predicted price will be displayed instantly.

🤖 Model Details

The prediction model used is XGBoost. It leverages a comprehensive dataset to provide accurate price predictions based on the input features.

📈 Dataset

The dataset used for training the model is dataset_sales_cleaned.csv, which includes cleaned and processed real estate data.

📚 Additional Information

Version: 1.0.0
Author: Loic Rouaud
https://www.linkedin.com/in/loic-rouaud
License: MIT License

👥 Contributing

We welcome contributions! If you'd like to contribute, please follow these steps:

Fork the repository. Create a new branch (git checkout -b feature-branch). Commit your changes (git commit -am 'Add new feature'). Push to the branch (git push origin feature-branch). Open a Pull Request.

⭐️ Acknowledgments

I would like to thank the initiative of the project during my trainee.