A data analysis project designed to empower the selection process of residential apartments from an HDB dataset. The primary objective is to meticulously filter and analyze apartments, considering crucial factors such as floor, area, and their orientation with respect to the sea wind direction. The project aims to provide valuable insights for making informed decisions in apartment selection, maximizing comfort and livability 🏡 🌊 🌬️
The code in this repository performs the following tasks:
- Reads and filters data from the HDB dataset.
- Selects apartments corresponding to a reference apartment on the sea wind direction for each building.
- Assign priorities to apartments based on client's criteria
- Sorts and organizes the selected apartments based on their cell numbers.
- Implements a conditional formatting function to highlight and color-code apartments based on priority.
- Saves the formatted data to an Excel file for further analysis and visualization. 💾 📊
To run this project on your local machine, follow these steps:
- Clone this repository to your local machine using the command:
git clone <repository-url>. - Install the required libraries by running
pip install -r requirements.txt. - Make sure you have the necessary dataset file (
all.xlsx) and the selection criteria file (selections.xlsx) in the project directory. - Open
client.pyand execute the code to perform the data analysis and generate the formatted Excel file. - The resulting Excel file (
best.xlsx) will contain the selected apartments, color-coded based on priority.
This project relies on the following libraries:
- pandas: to handle data manipulation and analysis.
- xlsxwriter: to save data to Excel files.
Make sure you have these libraries installed before running the code.
The project provides a visual representation of the selected apartments in an Excel file (best.xlsx). The apartments are color-coded based on their assigned priorities, allowing for a quick and intuitive understanding, vizualization, and furthher analysis of the data.
This project is part of a data analysis application and utilizes powerful Python libraries to efficiently filter and analyze the HDB dataset. The code is well-documented and organized to ensure easy understanding and reproducibility.
Contributions to this project are welcome! If you have any suggestions or improvements, feel free to create a pull request. Together, we can enhance the functionality and visualization of the apartment selection process.
If you have any questions or inquiries, please feel free to reach out via email at [eng.omar.al.sharif@gmail.com]. I would be happy to assist you!
Thank you for visiting this repository! 🚀 I hope you find the project insightful and the code useful in your data analysis endeavors. Happy apartment selection! 🏠