This project focuses on analyzing and predicting house prices using machine learning techniques. The goal is to provide insights into the factors that influence house prices and build a model that can accurately predict the price of a house based on its features.

• Analyze the dataset: Explore and visualize the dataset to understand the distribution of house prices and the relationships between different features.
• Data preprocessing: Clean and prepare the data for machine learning models.
• Feature engineering: Create new features and transform existing features to improve model performance.
• Model selection: Experiment with various machine learning algorithms to find the best model for predicting house prices.
• Model evaluation: Evaluate the performance of the chosen model and fine-tune it for better predictions.

• data/: Contains the dataset used for analysis and prediction.
• main.py: Site content with visual analytics.
• requirements.txt: List of project dependencies for easy setup.
• README.md: Project overview, instructions, and details.

1. Clone the repository:

   git clone https://github.com/Kotyga/homework_start_ds

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the web-application:

   streamlit run main.py

The aim of the project is to provide housing price forecasts based on various characteristics.

• Explore features that could improve prediction accuracy.
• Experiment with advanced machine learning techniques or ensemble models for better results.
• Deploy the model as a web application for real-time house price predictions.

Feel free to contribute to the project by opening issues, proposing new features, or submitting pull requests.

This project is inspired by the ML course homework. Special thanks to the mentors.

By [Maiia] - [https://www.linkedin.com/in/maiia-kotyga-358791245/] Date: [23.03.2024]