https://github.com/mike014/house-price-predictor-application
he House Price Predictor is a Python application that predicts house prices based on their size in square meters. It uses a linear regression model to make predictions and provides a graphical user interface (GUI) for user interaction. The application also includes logging, database management, and concurrency features.
https://github.com/mike014/house-price-predictor-application
Last synced: 7 months ago
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he House Price Predictor is a Python application that predicts house prices based on their size in square meters. It uses a linear regression model to make predictions and provides a graphical user interface (GUI) for user interaction. The application also includes logging, database management, and concurrency features.
- Host: GitHub
- URL: https://github.com/mike014/house-price-predictor-application
- Owner: Mike014
- License: gpl-3.0
- Created: 2024-09-14T16:19:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-14T17:05:19.000Z (over 1 year ago)
- Last Synced: 2025-05-30T22:43:42.306Z (about 1 year ago)
- Language: Python
- Size: 26.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# House-Price-Predictor-Application
he House Price Predictor is a Python application that predicts house prices based on their size in square meters. It uses a linear regression model to make predictions and provides a graphical user interface (GUI) for user interaction. The application also includes logging, database management, and concurrency features.
## How to Use
1. **Run the Application**: Execute `main.py` to start the GUI.
2. **Add Data**: Enter house sizes and corresponding prices, then click "Add Data" to store them in the database.
3. **Train the Model**: Click "Train Model" to train the linear regression model with the stored data.
4. **Make Predictions**: Enter a house size and click "Predict" to get the predicted price.
- **Clone the Repository from GitHub**: Open the integrated terminal in Visual Studio and clone the repository using the git clone command followed by the GitHub repository URL.
```bash
git clone https://github.com/username/House-Price-Predictor-Application.git
```
- **Navigate to the Project Directory**: Change to the project directory that you just cloned.
```bash
cd House-Price-Predictor-Application
```
- **Install Dependencies**: Install all the necessary dependencies listed in the requirements.txt file.
```bash
pip install -r requirements.txt
```
- **Run the Application**: Start the application by running the main.py file.
```bash
python main.py
```
## Concepts Used
- **Linear Regression**: A statistical method to model the relationship between a dependent variable and one or more independent variables. In this application, it is used to predict house prices based on their sizes. The model finds the best-fit line that minimizes the difference between the actual and predicted values.
- **Logging**: Recording events that happen during the execution of the program.
- **Threading**: Running multiple threads (smaller units of a process) concurrently.
- **SQLite Database**: A lightweight, disk-based database to store and manage data.
- **Tkinter**: A standard Python library for creating graphical user interfaces.
This application combines machine learning, GUI development, database management, logging, and concurrency to provide a comprehensive tool for predicting house prices.
