/Real-Estate-Price-Prediction

Primary LanguagePython

Real Estate Price Prediction Website

This data science project series walks through the step-by-step process of building a real estate price prediction website. We will start by building a machine learning model using scikit-learn and linear regression, utilizing the Bangalore home prices dataset from Kaggle.com. The project will then progress through three main components:

  1. Model Building with Scikit-Learn:

    • We will leverage scikit-learn to build a predictive model for estimating home prices in Bangalore.
    • Key concepts covered include data loading and cleaning, outlier detection and removal, feature engineering, dimensionality reduction, hyperparameter tuning using GridSearchCV, and K-fold cross-validation.
    • Technologies and tools used: Python, NumPy, Pandas for data manipulation, Matplotlib for data visualization, scikit-learn for model building, and Jupyter Notebook for experimentation.

  2. Django Server Implementation:

    • Instead of Flask, we will use Django to develop the HTTP server that serves the machine learning model.
    • Django provides a robust framework for building web applications with features such as URL routing, template rendering, and ORM (Object-Relational Mapping) for database interaction.
    • We will implement views to handle incoming requests for predicting home prices based on user input.
    • Technologies used: Python Django for HTTP server implementation.

  3. Website Development:

    • The third component involves building a user interface (UI) using HTML, CSS, and JavaScript.
    • Users will be able to input details such as home square footage and number of bedrooms into the website.
    • The website will then interact with the Django server to retrieve predicted home prices based on the provided inputs.

Technology and Tools Used:

  • Python: Programming language used for data manipulation, model building, and server development.
  • NumPy and Pandas: Python libraries used for data cleaning and manipulation.
  • Matplotlib: Python library used for data visualization.
  • scikit-learn: Python library used for machine learning model building.
  • Jupyter Notebook: Interactive development environment used for experimentation and documentation.
  • Visual Studio Code and PyCharm: Integrated Development Environments (IDEs) used for code development.
  • Django: High-level Python web framework used for implementing the HTTP server.
  • HTML/CSS/JavaScript: Web development technologies used for building the user interface.

Directory Structure:

  • data: Contains the dataset files.
  • notebooks: Contains Jupyter Notebooks for data preprocessing, model building, and experimentation.
  • django_server: Contains the Django server implementation.
  • website: Contains the HTML, CSS, and JavaScript files for the user interface.
  • README.md: This file, providing an overview of the project.

Getting Started:

  1. Clone this repository to your local machine.
  2. Navigate to the project directory.
  3. Follow the instructions in each component's directory (notebooks, django_server, website) to set up and run the respective parts of the project.

Acknowledgements:

  • The Bangalore home prices dataset used in this project is sourced from Kaggle.com.

Contributors:

  • [Pankaj shah]

License:

This project is licensed under the [License Name] License - see the LICENSE file for details.