Linear-Regression-Model-for-House-Price-Prediction and machine-learning-regression
These are ecosystem siblings—both are educational implementations of standard regression algorithms (linear regression, KNN, ridge/lasso) applied to the same housing price prediction problem, likely serving as reference implementations or tutorials rather than competing production tools.
About Linear-Regression-Model-for-House-Price-Prediction
huzaifsayed/Linear-Regression-Model-for-House-Price-Prediction
Linear Regression Model for Real State House Price Prediction
Implements full ML pipeline including exploratory data analysis, train-test splitting, and model evaluation on a 5000-row dataset with seven features (income, age, rooms, bedrooms, population, price, address). Uses scikit-learn's linear regression to estimate house prices based on aggregated neighborhood demographics and property characteristics. Demonstrates end-to-end workflow from data preprocessing through predictions on unseen test data.
About machine-learning-regression
agrawal-priyank/machine-learning-regression
Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python
Implements polynomial regression alongside linear variants and uses GraphLab Create for direct algorithm application, complementing hand-coded gradient descent implementations. Feature selection via L1/L2 regularization directly addresses multicollinearity and overfitting across simple, multiple, and polynomial regression architectures. Jupyter notebooks provide modular, reproducible workflows organized by algorithmic approach rather than a single integrated pipeline.
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