https://github.com/0290192029/apartment-price-predictor

Python-проект по прогнозированию стоимости аренды квартир с помощью линейной регрессии. Практическая работа по теме: "Основы машинного обучения" дисциплины "МДК 13.01: Основы применения методов искусственного интеллекта в программировании".
https://github.com/0290192029/apartment-price-predictor

apartment-price-prediction apartments-for-rent api correios-api data-analysis feature-engineering feature-enginering linear-regression linear-regression-models mlops numpy prediction-model r seaborn

Last synced: 4 months ago
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Python-проект по прогнозированию стоимости аренды квартир с помощью линейной регрессии. Практическая работа по теме: "Основы машинного обучения" дисциплины "МДК 13.01: Основы применения методов искусственного интеллекта в программировании".

• Host: GitHub
• URL: https://github.com/0290192029/apartment-price-predictor
• Owner: 0290192029
• Created: 2025-01-19T23:39:43.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-08-20T16:32:41.000Z (about 1 year ago)
• Last Synced: 2025-08-20T18:40:23.497Z (about 1 year ago)
• Topics: apartment-price-prediction, apartments-for-rent, api, correios-api, data-analysis, feature-engineering, feature-enginering, linear-regression, linear-regression-models, mlops, numpy, prediction-model, r, seaborn
• Size: 1.95 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🏠📊 Apartment Price Predictor 🏠📊

![Apartment Price Predictor](https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zip)

### Overview

Welcome to the "Apartment Price Predictor" repository! This Python project focuses on predicting apartment rental prices using linear regression. It serves as a practical assignment for the "Fundamentals of Machine Learning" course under the subject "MDK 13.01: Basics of applying artificial intelligence methods in programming." 

### Short Description

This repository contains a comprehensive Python project that implements a linear regression model to predict apartment rental prices. The project is designed to help users understand the basics of data analysis, data science, and machine learning. By studying this project, users can gain valuable insights into linear regression models, explore machine learning techniques, and enhance their Python programming skills.

### Topics

- Apartment Price Prediction

- Data Analysis

- Data Science

- Linear Regression

- Linear Regression Models

- Machine Learning

- Matplotlib

- Python

- Regression

- Scikit-learn

- Unit Testing

### Project Structure

The project is organized into the following key components:

- **Data Collection**: The project includes scripts to collect real-world apartment rental data from various sources.

- **Data Preprocessing**: The data undergoes preprocessing steps to clean, transform, and prepare it for modeling.

- **Model Training**: A linear regression model is trained on the preprocessed data to predict apartment prices accurately.

- **Evaluation**: The model's performance is evaluated using metrics such as Mean Squared Error and R-squared.

- **Visualization**: Utilizing Matplotlib, the project generates visualizations to help understand the data distribution and model outcomes.

### Getting Started

To get started with the "Apartment Price Predictor" project, follow these steps:

1. Clone the repository to your local machine.

2. Install the required dependencies listed in the `https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zip` file.

3. Explore the project structure and scripts to understand the implementation details.

4. Run the Python scripts to collect data, preprocess it, train the model, and evaluate its performance.

5. Visualize the results using Matplotlib to gain insights into the apartment price predictions.

### How to Use

To utilize the apartment price predictor:

1. Load your own apartment data or use the provided sample datasets.

2. Preprocess the data by handling missing values, encoding categorical variables, and scaling numerical features.

3. Train the linear regression model using Scikit-learn or a custom implementation.

4. Evaluate the model's performance using appropriate metrics and visualizations.

5. Make predictions on new apartment data and assess the model's accuracy.

### Demo

Check out the following demo to see the "Apartment Price Predictor" in action:

- [Demo Video](https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zip)

### Screenshots

Here are some screenshots showcasing the project:

![Screenshot 1](https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zip)

![Screenshot 2](https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zip)

### Download

You can download the full project code and resources from the following link:

[![Download Project](https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zip%https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zip)](https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zip)

(Launching required)

### Contributors

- John Doe - [@johndoe](https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zip)

- Jane Smith - [@janesmith](https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zip)

### Support

For any questions or issues, please reach out to the project's maintainers or open a GitHub issue.

### Related Projects

- [House Price Predictor](https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zip)

- [Stock Price Prediction](https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zip)

### License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.