This app helps you predict house prices in Azerbaijan. It uses a simple math method called Multiple Linear Regression. The program looks at factors like house size, location, and features to give you a price estimate. You do not need any programming skills to use this.
The tool is designed for people interested in real estate, data analysis, or those who want a straightforward way to estimate property values. It runs on Windows and comes with clear steps to get started, no coding required.
Before installing, make sure your computer runs Windows 10 or later. You need at least:
- 4 GB of RAM
- 1 GHz or faster processor
- 500 MB of free disk space
- An internet connection to download the app
These requirements help the program work smoothly when loading data and making predictions.
You will get the app files from the main GitHub page linked below. Follow these detailed instructions to open and use the app on your Windows PC.
Click the big button above or go directly to this link:
Download Multiple-Linear-Regression
This page contains all files and instructions for the app.
On the GitHub page, look for a section named Releases or a folder named Downloads. Inside, find the latest setup file. It should have a name like Multiple-Linear-Regression-Setup.exe or similar.
Click to download this file to your PC. Save it somewhere easy to find, like your desktop or the Downloads folder.
- Find the downloaded file on your PC.
- Double-click the
.exefile to start the installer. - Follow the steps shown on your screen. The process usually involves clicking “Next,” accepting the license terms, and choosing where to install the app.
No options need to be changed during installation. Just accept the defaults if you are unsure.
Once installed, open the app by:
- Clicking the shortcut on your desktop, or
- Searching for "Multiple-Linear-Regression" in your Start menu.
The app window will appear with a simple interface to input house data and get price predictions.
Using this tool does not require technical knowledge. Here is how to proceed:
-
Input House Details
Fill in fields like house size (in square meters), number of rooms, and neighborhood quality. The app might also ask for other details like year built or proximity to amenities. -
Start the Prediction
Click the “Predict” button. The program calculates the likely price based on all data entered. -
View Results
The predicted price shows up clearly on the screen. You can use this number to understand the current market or compare properties. -
Explore Data Visualizations
The app includes graphs and charts that display data trends. For example, you might see how price changes with house size or neighborhood.
This program uses several trusted tools and techniques:
- Python: The main programming language behind the app.
- Pandas and NumPy: Libraries that help handle data tables easily.
- Scikit-learn: A popular library to perform linear regression.
- Matplotlib and Seaborn: Tools that create easy-to-understand graphs.
- Simple Interface: Designed so users can quickly get results without programming.
The app processes your inputs with a trained regression model. It looks at many factors at once and finds the best fit for house prices.
- Check your Windows version. Update if needed.
- Restart your computer and try again.
- Make sure you have the latest version. Go back to the download page to verify.
- Ensure you have enough disk space.
- Close other apps while installing.
- Run the installer as Administrator (right-click the file and choose "Run as Administrator").
- Double-check you entered all values correctly.
- Remember, this is a prediction based on available data, not an exact price.
You can visit the main GitHub page any time to get the latest version of the app or see updates:
Multiple-Linear-Regression Repository
Look at the Releases section for new versions or bug fixes.
- Save your inputs and results as a file for later review.
- View detailed reports showing how each factor affects price.
- Export charts as images to share or use in presentations.
These features help you use the app more effectively and keep track of your analysis.
If you run into any problems, you can open an Issue on the GitHub page. Describe your problem clearly and include screenshots if possible. The development team or community can then respond to help fix it.