https://github.com/thecoderpinar/house-price-prediction-project

🏠 This project focuses on predicting house prices using advanced regression techniques. It involves comprehensive data preprocessing, feature engineering, and model selection. The aim is to develop an accurate predictive model for real estate prices.
https://github.com/thecoderpinar/house-price-prediction-project

data-analysis data-preprocessing data-visualization deep-learning jupyter-notebook machine-learning neural-networks python regression regression-models

Last synced: 1 day ago
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🏠 This project focuses on predicting house prices using advanced regression techniques. It involves comprehensive data preprocessing, feature engineering, and model selection. The aim is to develop an accurate predictive model for real estate prices.

• Host: GitHub
• URL: https://github.com/thecoderpinar/house-price-prediction-project
• Owner: ThecoderPinar
• License: mit
• Created: 2023-10-31T16:10:44.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-31T16:19:41.000Z (over 1 year ago)
• Last Synced: 2024-12-16T17:55:17.325Z (about 2 months ago)
• Topics: data-analysis, data-preprocessing, data-visualization, deep-learning, jupyter-notebook, machine-learning, neural-networks, python, regression, regression-models
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data
• Size: 6.79 MB
• Stars: 6
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# House Price Prediction Project

## 🏡 About The Project

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.

## 🚀 Project Highlights

- **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

## 📊 Project Details

### Data Set Description

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.

### Project Steps

1. **Data Exploration and Preprocessing:**

   - Identifying and handling missing values and outliers

   - Feature engineering for enhanced data representation

2. **Model Building and Evaluation:**

   - Trying out different regression techniques

   - Evaluating model performance and selecting the best model

3. **Data Visualization:**

   - Creating insightful visualizations to understand the data better

   - Analyzing trends and patterns in the dataset

## 🛠️ Technologies Used

- Python

- Pandas

- NumPy

- Matplotlib

- Seaborn

- Scikit-learn

## 📋 Installation

1. Clone the repository

   ```sh

   git clone https://github.com/ThecoderPinar/House-Price-Prediction-Project.git

2. Install required packages

   ```sh

   pip install -r requirements.txt

## 🤝 Contributing

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

## 📝 License

Distributed under the MIT License. See LICENSE for more information.

## 📞 Contact

Your Name - Your Email

- Pınar Topuz

- [email protected]

Project Link: https://github.com/ThecoderPinar/House-Price-Prediction-Project