Tree Property Price Analyzer Overview This project analyzes property prices based on streets with short and tall trees. Using JSON and CSV files, it calculates the average property price for each street type, providing insights into whether tree density correlates with property value.

Features Dynamic Data Analysis: Processes data from JSON and CSV files to categorize streets and calculate averages. Customizable: Easily replace input files to analyze different datasets. Unit-Tested: Comprehensive tests ensure reliability and accuracy. Project Structure python Copy code pythonProjectTree/ ├── analyze_tree_property.py # Main program file ├── test_analyze_tree_property.py # Unit tests ├── dublin-trees.json # JSON file with street data ├── dublin-property.csv # CSV file with property prices ├── test-dublin-trees.json # Test JSON file for unit tests ├── test-dublin-property.csv # Test CSV file for unit tests ├── requirements.txt # Python dependencies └── README.md # Project documentation Setup Instructions Install Python:

Ensure Python 3.8+ is installed. Verify with: bash Copy code python --version Clone the Repository:

bash Copy code git clone https://github.com/P3st83/Projecttree.git cd Projecttree Create and Activate a Virtual Environment:

bash Copy code python -m venv .venv source .venv/bin/activate # On Windows, use .venv\Scripts\activate Install Dependencies:

bash Copy code pip install -r requirements.txt Running the Program Execute the Analysis:

bash Copy code python analyze_tree_property.py This will calculate and display average property prices for streets with short and tall trees.

Sample Output:

vb net Copy code Average property price on streets with short trees: €488981.66 Average property price on streets with tall trees: €587800.39 Running Unit Tests To validate the functionality, run the unit tests:

bash Copy code python -m unittest test_analyze_tree_property.py Files analyze_tree_property.py: The main script that analyzes property prices. test_analyze_tree_property.py: Unit tests for the script. dublin-trees.json: Input file containing categorized tree data. dublin-property.csv: Input file with property prices and street names. test-dublin-trees.json: Mock JSON data for testing. test-dublin-property.csv: Mock CSV data for testing. Dependencies The project uses the following Python libraries:

json (Built-in) csv (Built-in) unittest (Built-in) pathlib (Built-in) statistics chardet Install them using:

bash Copy code pip install -r requirements.txt Contribution Guidelines Fork the repository. Create a new branch for your feature: bash Copy code git checkout -b feature-name Commit your changes: bash Copy code git commit -m "Add feature-name" Push to your branch: bash Copy code git push origin feature-name Create a pull request. License This project is licensed under the MIT License. See LICENSE for more details.

This updated README ensures clarity, professionalism, and ease of use for developers and users alike. Let me know if you'd like any additional changes!