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Welcome to EarthMark

EarthMark is a real estate price prediction website built to provide accurate property price estimates based on various factors like area size, number of bedrooms, number of bathrooms, etc.

EARTHMARK-DEMO-VIDEO

Key Features

  • Property Price Prediction
  • User-Friendly Interface
  • Market Trends

Tech Stack

Category Toolkit Used
Frontend HTML5 CSS3 JavaScript
Backend Python Flask
Machine Learning Scikit Learn
Design Tool Figma

Setup and Usage

To set up a complete flask environment for the EarthMark project, follow these steps:

Step 1: Clone the Repository

git clone https://github.com/iamRabia-N/EarthMark.git

Step 2: Navigate to the Project Directory

Once the cloning process is complete, navigate to the project directory using the cd command:

cd EarthMark

Step 3: Setup Virtual Environment

First, install the virtualenv package:

pip install virtualenv

Then, create a new virtual environment named "env" (you can choose any name) using the following command:

virtualenv env

Activate the virtual environment by running the activation script. Note that the command might differ depending on your operating system:

For Windows (PowerShell):

.\env\Scripts\Activate.ps1

For macOS/Linux:

source env/bin/activate

Step 4: Install Flask

With the virtual environment activated, you can now install flask and its dependencies using pip:

pip install flask

Step 5: Start Deploying in Browser

python main.py

Credits

  • The images and videos used in this project are taken from Pexels.
  • The dataset used in this project is taken from Kaggle.

Future Enhancements

Enhancement 01: Logo Integration

A logo will be added to the top-left corner of each page. This addition aims to enhance the project's visual identity and professionalism. This addition is depicted in the following high-fidelity wireframe images.

Landing Page Price Prediction Page
About Page Market Trends & Latest News

Enhancement 02: Recommendation Slider

Addition of a recommendation slider based on user input. For example, it will display a list of properties in the same price range as the user's inputed based predicted price. This slider will appear directly below the output section. This addition is depicted in the following high-fidelity wireframe images.

Current Design Future Enhancement Work

Important NOTE

In this project, an API named "NewsAPI" is used on the website's "Market Trends and Latest News" page. To ensure this page works properly, you need to set up an API key. Follow these steps:

  • Go to NewsAPI website and sign up for a free account.
  • After signing up and logging in, look for the option to generate a new API key. Copy the API key provided.
  • Open the project's repository on your local machine. Create a file named .env in the root directory. Inside the .env file, add the following line:
API_KEY=your_api_key_here
  • Replace your_api_key_here with the API key you obtained from NewsAPI. Save the .env file.

Furthermore, when running the repository, ensure the dataset file path in the main.py is correctly configured to match its location on your system. By default, the dataset is expected to be located at the following address:

df = pd.read_csv(r"E:\EarthMark\House_Price_dataset.csv")

If you fork this repository to a different location on your machine, you will need to update the file path accordingly in your code.

Bug Reporting

Feel free to open an issue on GitHub if you find any bug.

Feature Requests

Feel free to open an issue on GitHub to request additional features that would benefit your use case.