Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/assem-elqersh/kaggle-housing-prices-prediction
House-Prices Advanced Regression Techniques Competition Solution
https://github.com/assem-elqersh/kaggle-housing-prices-prediction
feature-engineering hyperparameter-optimization kaggle-competition lasso-regression-model lgbmregressor support-vector-regression xgboost
Last synced: about 1 month ago
JSON representation
House-Prices Advanced Regression Techniques Competition Solution
- Host: GitHub
- URL: https://github.com/assem-elqersh/kaggle-housing-prices-prediction
- Owner: Assem-ElQersh
- License: gpl-3.0
- Created: 2024-02-11T18:32:06.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-06-21T12:20:19.000Z (6 months ago)
- Last Synced: 2024-06-22T05:04:40.993Z (6 months ago)
- Topics: feature-engineering, hyperparameter-optimization, kaggle-competition, lasso-regression-model, lgbmregressor, support-vector-regression, xgboost
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques
- Size: 1.95 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# House Price Prediction
This repository contains Python code for predicting house prices using advanced regression techniques. The project focuses on preprocessing data, exploring various features, applying machine learning models, and optimizing predictions.
## Project Structure
- **`Data/`**: Directory containing datasets used for training (`train.csv`), and testing (`test.csv`).
- **`requirements.txt`**: File listing all required Python libraries and dependencies.
- **`house_price_prediction.ipynb`**: Jupyter notebook containing the main code for data preprocessing, model building, and evaluation.
## Description
This project aims as a solution for this [problem](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques) to predict house prices using a dataset that includes various features such as lot size, neighborhood, number of rooms, and more. The steps involved in the project are as follows:
1. **Data Preprocessing**:
- Importing and cleaning datasets.
- Handling missing values and outliers.
- Feature engineering to create new meaningful features.
2. **Exploratory Data Analysis**:
- Visualizing relationships between features and target variable (`SalePrice`).
- Analyzing distributions and correlations among variables.
3. **Model Building**:
- Implementing machine learning models including:
- Random Forest, Gradient Boosting, Ridge Regression, Lasso Regression, Support Vector Regression (SVR), XGBoost, LightGBM, CatBoost.
- Hyperparameter tuning using Randomized Search Cross Validation.
4. **Ensemble Learning**:
- Stacking multiple optimized models for improved prediction accuracy.
5. **Evaluation and Submission**:
- Generating predictions using the ensemble model.
- Formatting predictions according to submission requirements.
## Requirements
All Python libraries and dependencies required for running the code are listed in `requirements.txt`. Install them using the following command:
```bash
pip install -r requirements.txt
```
## Usage
1. Clone the repository:
```bash
git clone https://github.com/Assem-ElQersh/Kaggle-Housing-Prices-Prediction
cd Kaggle-Housing-Prices-Prediction
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Run the Jupyter notebook `house_price_prediction.ipynb` to execute the code step-by-step.
4. The final predicted house prices will be saved in `my_prediction_ensemble.csv`.
## Contributors
- [Assem ElQersh](https://github.com/Assem-ElQersh)
## License
This project is licensed under the GNU General Public License v3.0 - see the [LICENSE](https://github.com/Assem-ElQersh/Kaggle-Housing-Prices-Prediction/blob/main/LICENSE) file for details.