Apartment Rental Price Prediction System

Welcome to the Apartment Rental Price Prediction System repository. This project is designed to predict the rental prices of apartments using a range of apartment attributes. Utilizing the UCI Machine Learning Repository's Apartment for Rent dataset, this system offers an innovative approach to understanding rental market dynamics.

Usage video

AppUsage.webm

Features

• Rental Price Prediction: Estimate apartment rental prices based on various attributes.
• Data-Driven Insights: Gain insights into factors affecting rental prices.
• MLOps Integration: Leveraging modern MLOps tools for enhanced performance and reliability.

Tech Stack

• Docker: For containerizing the different components of the system, ensuring consistent environments and easy deployment.
• Python: The primary programming language used for data processing and model development.
• MLFlow: For tracking experiments, model management, and registry.
• Scikit-learn: A robust library for machine learning model development.
• FastAPI: A modern, fast web framework for building APIs.
• Streamlit: For creating interactive web applications for user predictions.
• Pandas: For data manipulation and analysis.
• Other data analysis and processing tools.

Quick Start

Setup: Ensure Docker is installed on your system.

docker-compose up

Access the Interface: Open your browser and navigate to http://localhost:8501 for the Streamlit UI. Making Predictions: Use the interface to input apartment attributes and receive price predictions. MLflow Tracking: Visit http://localhost:5000 to view tracked experiments and model versions.

Usage

The workflow involves:

Data Acquisition: Automatically downloading the dataset. Data Processing: Cleaning and preparing data for training. Model Training and Tracking: Using MLflow server for training and tracking models. Frontend and Backend Integration: Frontend: Streamlit UI for users to input data and receive predictions. Backend: FastAPI for serving the ML model, using MLFlow registry to load and predict.

Directory Structure

├── application/
│   ├── app/ # API
│   ├── front/ # Front-End

├── data/
│   ├── external/
│   ├── interim/
│   ├── processed/
│   ├── raw/
│   └── ...
├── mlflow-db/ - MLFlow database
│   ├── Dockerfile
│   ├── mlflow.db
│   └── ...
├── notebooks/
│   └── exploration.ipynb
└── src/ - Train and Data modules
    ├── data-pipeline/
    ├── train/
    ├── .gitignore
    ├── README.md
    └── requirements.txt

Development Model Development: Python and Scikit-learn are used for developing the prediction model.

API Development with FastAPI: Serving the model for predictions.

Frontend Development with Streamlit: Interactive UI for making predictions.

Docker Integration: Ensuring seamless operation of all components.

Contributing

Contributions are welcome! Feel free to fork this repository, open issues, or submit PRs. For significant changes, please open an issue first to discuss the proposed change.