📊 🔍 ⚠️ 📈

Detecting Outliers & Anomalies

Find What Doesn't Belong

💡 Hover over any tip or practice to see practical examples
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Z-Score Method

  • Calculate mean and standard deviation
  • Z-score = (value - mean) / std dev
  • Flag values with |Z-score| > 3
  • Works best with normally distributed data
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IQR Method

  • Find Q1 (25th percentile) and Q3 (75th percentile)
  • Calculate IQR = Q3 - Q1
  • Lower bound: Q1 - 1.5 × IQR
  • Upper bound: Q3 + 1.5 × IQR
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Box Plots

  • Visualize distribution and spread
  • Points beyond whiskers are outliers
  • Easy to compare multiple groups
  • Quick identification of data range
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Scatter Plots

  • Plot relationships between variables
  • Spot points far from clusters
  • Identify patterns and trends
  • Useful for multivariate analysis
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Isolation Forest

  • Isolates anomalies using random forests
  • Anomalies are easier to isolate
  • Works well with high-dimensional data
  • No assumptions about data distribution
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Distance-Based Methods

  • Calculate distance to nearest neighbors
  • Points far from neighbors are outliers
  • Use k-NN or DBSCAN algorithms
  • Effective for spatial data analysis

Best Practices for Anomaly Detection

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Define Normal First
Understand what "normal" looks like in your data before identifying anomalies. Context matters significantly.
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Use Multiple Methods
Combine different detection techniques to reduce false positives and increase confidence in findings.
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Clean Data First
Remove duplicates and handle missing values before detection to avoid false anomalies from data quality issues.
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Consider Domain Knowledge
Statistical outliers aren't always true anomalies. Apply business context to validate findings.
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Tune Thresholds
Adjust sensitivity parameters based on your specific use case and acceptable false positive rates.
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Investigate Root Causes
Don't just flag anomalies—understand why they occurred. They may reveal important insights or errors.