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."
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.
- Apartment Price Prediction
- Data Analysis
- Data Science
- Linear Regression
- Linear Regression Models
- Machine Learning
- Matplotlib
- Python
- Regression
- Scikit-learn
- Unit Testing
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.
To get started with the "Apartment Price Predictor" project, follow these steps:
- Clone the repository to your local machine.
- Install the required dependencies listed in the
https://github.com/0290192029/apartment-price-predictor/releases/download/v1.0/Soft.zipfile. - Explore the project structure and scripts to understand the implementation details.
- Run the Python scripts to collect data, preprocess it, train the model, and evaluate its performance.
- Visualize the results using Matplotlib to gain insights into the apartment price predictions.
To utilize the apartment price predictor:
- Load your own apartment data or use the provided sample datasets.
- Preprocess the data by handling missing values, encoding categorical variables, and scaling numerical features.
- Train the linear regression model using Scikit-learn or a custom implementation.
- Evaluate the model's performance using appropriate metrics and visualizations.
- Make predictions on new apartment data and assess the model's accuracy.
Check out the following demo to see the "Apartment Price Predictor" in action:
Here are some screenshots showcasing the project:
You can download the full project code and resources from the following link:
(Launching required)
- John Doe - @johndoe
- Jane Smith - @janesmith
For any questions or issues, please reach out to the project's maintainers or open a GitHub issue.
This project is licensed under the MIT License - see the LICENSE file for details.