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https://github.com/mateusoliveira30/house-prices
This project was developed for the Kaggle competition "House Prices - Advanced Regression Techniques." The goal is to predict house sale prices using advanced regression techniques, including feature engineering, Random Forests, and Gradient Boosting.
https://github.com/mateusoliveira30/house-prices
kaggle-competition machine-learning scikit-learn
Last synced: 14 days ago
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This project was developed for the Kaggle competition "House Prices - Advanced Regression Techniques." The goal is to predict house sale prices using advanced regression techniques, including feature engineering, Random Forests, and Gradient Boosting.
- Host: GitHub
- URL: https://github.com/mateusoliveira30/house-prices
- Owner: MateusOliveira30
- Created: 2024-07-09T11:39:45.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-08-15T15:08:00.000Z (5 months ago)
- Last Synced: 2024-11-08T21:12:17.218Z (2 months ago)
- Topics: kaggle-competition, machine-learning, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 70.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# House Prices - Advanced Regression Techniques
This project was developed for the Kaggle competition "House Prices - Advanced Regression Techniques," where the goal is to predict house sale prices using advanced regression techniques.
**Description**
In this competition, participants are required to predict house sale prices based on various property features. The task involves practicing feature engineering, Random Forests (RFs), and Gradient Boosting.
**Results**
My model achieved a Root Mean Squared Error (RMSE) of 0.25476, demonstrating strong predictive capability.
**Contents**
**Jupyter Notebook:** Used for exploratory data analysis, feature engineering, and model development.
**README.md:** This file.
**Dependencies**
To run this project, you will need to install the following libraries:
#### 1. Python 3.8+
#### 2. pandas
#### 3. numpy
#### 4. scikit-learn
#### 5. matplotlib
You can install the dependencies using the following command:
```pip install pandas numpy scikit-learn matplotlib```
**Project Structure**
**1. Exploratory Data Analysis (EDA):** Initial analysis to understand the distribution and correlations of variables.
**2. Feature Engineering:** Transformation and creation of new relevant features to improve model performance.
**3. Modeling:** Training and validation of various regression models, including Random Forests and Gradient Boosting.
**4. Evaluation:** Comparison of models using appropriate metrics and selection of the best-performing model.
**How to Run**
1. Clone this repository:
```git clone https://github.com/seu_usuario/house-prices-advanced-regression-techniques.git```
2. Navigate to the project directory:
```cd house-prices-advanced-regression-techniques```
3. Install the dependencies:
```pip install -r requirements.txt```
4. Run the notebook to reproduce the analyses and trainings:
```jupyter notebook notebooks/House_Prices_Advanced_Regression_Techniques.ipynb```
**Contribution**
Contributions are welcome! Feel free to open issues or submit pull requests.
**References**
[Kaggle Competition: House Prices - Advanced Regression Techniques](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/overview)