Welcome to Basics of Predictive Analysis, an interactive project designed to predict rental prices and classify luxury apartments in Berlin using synthetic data. This project utilizes regression and classification analyses, integrated into a user-friendly GUI for easy selection and visualization of different tasks.
This project includes three main components:
| Number | Task | Description | Code |
|---|---|---|---|
| 1. | 🏢 Regression Analysis | Predict rental prices of Berlin apartments based on various features. | Code |
| 2. | 🏠 Classification Analysis | Classify apartments as luxury or non-luxury based on rental prices and other attributes. | Code |
| 3. | 🖥️ Main GUI Script | Launch the GUI to choose between regression and classification analyses. | Code |
To try out the project, you can run it directly on Replit:
-
Run the Replit Project: Run Project on Replit
-
Choose Analysis Type:
- Select "Regression" to predict rental prices.
- Select "Classification" to classify apartments as luxury or non-luxury.
- Python: The primary programming language used for this project.
- Scikit-learn: For machine learning models.
- Matplotlib: For data visualization.
- Tkinter: For the graphical user interface.
- Scripts:
regression_analysis.py: Script to run regression analysis.classification_analysis.py: Script to run classification analysis.main.py: Main script to launch the GUI and choose between regression and classification.
Interested in contributing? Fork the repository, create a new branch, and submit a pull request.
This project is open-source. Feel free to use and modify the code as needed.