Overfitting
Data Patterns
Data Trends
Statistical Methods
Data Mining Techniques for Sales Forecasting
Trend Analysis
Interactions
Trend Analysis
Limitations 
Analytical Model Limitations Analytical models are essential tools in data analysis, but they also come with limitations:
Overfitting: Models that are too complex may fit the training data too closely, leading to poor performance on new data
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Data Mining Challenges and Solutions It can lead to
overfitting, where models perform well on training data but poorly on unseen data
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Data Patterns Overfitting: In predictive modeling, there is a risk of overfitting, where a model captures noise instead of the underlying pattern
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Data Trends Overfitting: In predictive modeling, overfitting occurs when a model is too complex and captures noise instead of the underlying trend
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Statistical Methods Overfitting: In predictive modeling, overfitting occurs when a model is too complex and captures noise rather than the underlying pattern
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Data Mining Techniques for Sales Forecasting Advantages Limitations Easy to understand and interpret Prone to
overfitting Handles both numerical and categorical data May require pruning for better accuracy 4
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Trend Analysis Overfitting: Focusing too much on historical data can lead to overfitting, where models become too complex and fail to generalize to new data
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Interactions Overfitting: Including too many interaction terms in a model can lead to overfitting, where the model performs well on training data but poorly on unseen data
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Trend Analysis Overfitting: In complex models, there is a risk of overfitting, where the model becomes too tailored to historical data and fails to predict future trends accurately
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Trend Analysis Overfitting: Relying too heavily on historical trends can lead to overfitting, where models perform well on past data but poorly on new data
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