This project aims to predict house prices using advanced regression techniques. By leveraging various features and data points, we strive to build a robust model for accurate price predictions.
- Data Preprocessing: Handling missing values and outliers
- Feature Scaling: Applying normalization and standardization for improved performance
- Model Selection: Testing various algorithms to identify the best-performing model
- Data Visualization: Creating insightful visualizations for better understanding
- Model Evaluation: Assessing model performance using various metrics
The dataset comprises various features related to residential properties, including size, location, amenities, and other relevant factors. It encompasses a diverse range of housing prices and related attributes.
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Data Exploration and Preprocessing:
- Identifying and handling missing values and outliers
- Feature engineering for enhanced data representation
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Model Building and Evaluation:
- Trying out different regression techniques
- Evaluating model performance and selecting the best model
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Data Visualization:
- Creating insightful visualizations to understand the data better
- Analyzing trends and patterns in the dataset
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Clone the repository
git clone https://github.com/ThecoderPinar/House-Price-Prediction-Project.git
- Install required packages
pip install -r requirements.txt
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
Fork the Project
- Create your Feature Branch (git checkout -b feature/House-Price-Prediction-Projec)
- Commit your Changes (git commit -m 'Add some House-Price-Prediction-Project')
- Push to the Branch (git push origin feature/House-Price-Prediction-Project)
- Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
Your Name - Your Email
- PΔ±nar Topuz
- piinartp@gmail.com
Project Link: https://github.com/ThecoderPinar/House-Price-Prediction-Project