Model Selection Tool
Project description
📦 Models Class – Regression Models Playground
This class helps you quickly test different regression algorithms (OLS, SGD, BGD) on any DataFrame and target.
✂️ split_data(ratio=0.8, randomState=42)
Splits data into train/test based on ratio.
Uses self.target_column to separate X and y.
📈 Linear_Regression_OLS(get_equation=False, plot=False, accuracy=True)
Trains simple (1D) Linear Regression using Ordinary Least Squares.
get_equation: print learned lineplot: visualizeaccuracy: print score
📊 MLinear_Regression_OLS(get_equation=False, plot=False, accuracy=True)
Multi-feature version of OLS Linear Regression.
⚡ MLinear_Regression_SGD(epochs=100, learning_rate=0.01, plot=False, accuracy=True, get_equation=False)
Multi-feature SGD Linear Regression. Trains with Stochastic Gradient Descent.
🌀 Linear_Regression_BGD(epochs=100, learning_rate=0.01, plot=False, accuracy=True, get_equation=False)
Simple (1D) Linear Regression using Batch Gradient Descent.
💪 MLinear_Regression_BGD(epochs=100, learning_rate=0.01, plot=False, accuracy=True, get_equation=False)
Multi-feature BGD-based Linear Regression.
🧼 Standard_Scale(features=None)
Standardizes features (z-score normalization). Applies to entire DataFrame if no features are specified. Re-splits data after scaling.
🚀 Linear_Regression_SGD(epochs=100, learning_rate=0.01, plot=False, accuracy=True, get_equation=False)
Simple (1D) Linear Regression using SGD.
📤 Extract_Data()
Returns (X_train, X_test, y_train, y_test) — useful for external use.
🆕 Set_DF(newDF, target_column)
Reset the class with a new DataFrame and target column.
🔀 Select_Model(model=None, batch_size=None, method=None, epochs=100, learning_rate=0.01, plot=False, accuracy=True, get_equation=False)
Unified interface to select and run any regression model or method.
model: Choose'linear_regression'('lr') or'multi_linear_regression'('mlr')method: Specify algorithm ('ols','bgd','sgd','mgbd')batch_size: For mini-batch gradient descent ('mgbd')- Other arguments are passed to the underlying method
Raises ValueError if model or method is unknown.
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