HousePriceEstimationProject

Overview

The HousePriceEstimationProject aims to estimate house prices using various machine learning techniques. This project involves data collection, cleaning, model training, and deployment through a FastAPI service.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Project Structure
• Contributing
• License

Features

• Data crawling and collection
• Data cleaning and preprocessing
• Various machine learning models for price estimation
• API service for predictions using FastAPI

Installation

1. Clone the repository:

   git clone https://github.com/javad787/HousePriceEstimationProject.git
   cd HousePriceEstimationProject

2. Install the required packages:

   pip install -r requirements.txt

Usage

1. Prepare the data:

   python data_cleaning/clean_data.py

2. Train the models:

   python models/train_model.py

3. Run the FastAPI server:

   uvicorn fastapi.main:app --reload

4. Access the API documentation at http://127.0.0.1:8000/docs

Project Structure

HousePriceEstimationProject/
│
├── data/                # Raw and processed data
├── data_cleaning/       # Scripts for cleaning data
├── data_crawling/       # Scripts for collecting data
├── fastapi/             # FastAPI application
├── models/              # Machine learning models
├── .gitignore           # Git ignore file
├── README.md            # Project README
├── requirements.txt     # Python dependencies

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes. Ensure that your code adheres to the project's coding standards.

License

This project is licensed under the MIT License. See the LICENSE file for more details.