This is my learning journey into data modeling.
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Linear Models: Simple and multiple linear regression. Modeling relationships between dependent and independent variables.
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Logistic Regression: Used for classification tasks where the response variable is categorical (e.g., yes/no).
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Generalized Linear Models (GLMs): Extensions of linear models that handle non-normal response distributions (e.g., Poisson regression for count data).
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Time Series Modeling: For analyzing temporal data, models like ARIMA, exponential smoothing, etc.
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Decision Trees & Random Forests: Non-linear models useful for both classification and regression tasks.
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Model Evaluation: Techniques such as cross-validation, residual analysis, and metrics like RMSE, MAE, and AUC to assess model performance.
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Regularization Techniques: Lasso, Ridge, and Elastic Net to handle overfitting and improve model generalization.
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Clustering and Dimensionality Reduction: Techniques like K-means clustering and PCA to group data and reduce complexity.