https://github.com/prathamp25/simplehousepricepredictor_
A regression-based Machine Learning project predicting house prices using multiple models: Linear Regression, Random Forest, XGBoost, Gradient Boosting, SVR, and KNN. Includes feature engineering, visualization, and model comparison.
https://github.com/prathamp25/simplehousepricepredictor_
house-price-prediction linear-regression machine-learning-algorithms random-forest xgboost-algorithm
Last synced: 7 months ago
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A regression-based Machine Learning project predicting house prices using multiple models: Linear Regression, Random Forest, XGBoost, Gradient Boosting, SVR, and KNN. Includes feature engineering, visualization, and model comparison.
- Host: GitHub
- URL: https://github.com/prathamp25/simplehousepricepredictor_
- Owner: prathamp25
- Created: 2025-02-15T16:21:39.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-03-02T19:11:59.000Z (7 months ago)
- Last Synced: 2025-03-02T20:23:29.765Z (7 months ago)
- Topics: house-price-prediction, linear-regression, machine-learning-algorithms, random-forest, xgboost-algorithm
- Language: Jupyter Notebook
- Homepage:
- Size: 1.07 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# SimpleHousePricePredictor_
## Features Used:
Square Feet,
Bedrooms,
Bathrooms,
Age of House,
Distance from City Center.
## Dataset: California Housing Prices dataset from Scikit-Learn.
Trained a Linear Regression model and Decision Trees, Random Forests, and XGBoost.
## Algorithms Used
The following machine learning algorithms were implemented and compared:
Linear Regression,
Random Forest Regressor,
XGBoost Regressor.
## Performance Metrics
The models were evaluated using:
Mean Squared Error (MSE),
R-Squared Score (R² Score).