🔍 Objective¶
💡 Problem Framing¶
📈 Why Regression?¶
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🧪 Data Setup¶
📥 Load Dataset¶
📊 Visual EDA¶
🛠 Feature Engineering¶
✂️ Train-Test Split¶
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🔄 Preprocessing¶
⚖️ Feature Scaling¶
🧹 Feature Selection (RFE)¶
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✅ Assumption Checks¶
📐 Linearity¶
📉 Homoscedasticity¶
📊 Normality of Residuals¶
🔗 Multicollinearity¶
🔄 Independence¶
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⚙️ Base Model: Linear Regression¶
🏗️ Model Fit¶
📏 Evaluation Metrics¶
🌀 Residual Analysis¶
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🚨 Outlier Detection¶
🔍 Leverage¶
🔥 Cook’s Distance¶
📉 Visual Diagnostics¶
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🧰 Regularized Models¶
🧊 Ridge Regression¶
🔥 Lasso Regression¶
🧃 ElasticNet¶
🎯 Hyperparameter Tuning¶
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📊 Diagnostic Plots¶
🆚 Predicted vs Actual¶
🌀 Residual Plot¶
📈 QQ Plot¶
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🧠 Interpretability¶
🔢 Coefficients¶
🧮 VIF¶
🔬 SHAP / PDP¶
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🧾 Model Comparison Summary¶
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📌 Final Summary¶
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❓ FAQ / Notes¶
📌 When to use regularization¶
📊 What impacts R²¶
🚨 Overfitting indicators¶
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