https://github.com/asherk7/house-price-prediction
House Prices - Advanced Regression Techniques - Predict sales prices and practice feature engineering, RFs, and gradient boosting
https://github.com/asherk7/house-price-prediction
data-science numpy pandas regression scikit-learn
Last synced: 2 months ago
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House Prices - Advanced Regression Techniques - Predict sales prices and practice feature engineering, RFs, and gradient boosting
- Host: GitHub
- URL: https://github.com/asherk7/house-price-prediction
- Owner: asherk7
- License: mit
- Created: 2025-02-01T18:35:13.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-17T21:10:00.000Z (over 1 year ago)
- Last Synced: 2025-02-17T21:33:15.203Z (over 1 year ago)
- Topics: data-science, numpy, pandas, regression, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 2.65 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# House Prices - Kaggle Competition
This is my attempt at the **House Prices - Advanced Regression Techniques** Kaggle competition. The goal is to predict home sale prices using regression models.
## Steps
1. **EDA**: Explored data distributions and missing values.
2. **Feature Engineering**: Handled missing data, encoded categorical variables, transformed skewed feeatures, and created new features.
3. **Modeling**: Tested Linear Regression, Random Forest Regression, XGBoost, Gradient Boosting and LightGBM. Implemented Ensemble model stacking and weighted averaging to improve accuracy.
4. **Submission**: Generated predictions and submitted to Kaggle.
## Results
My best score achieved on the Kaggle Leaderboard: `0.13295`
Came in the top 25% of the Kaggle Rolling Leaderboard.