## Implementation Guide

### Step 1: Prepare Data for Analysis

Set up the dataset for anomaly detection:

1. Load dataset using Read tool
2. Inspect data structure and identify relevant features
3. Clean data by handling missing values and inconsistencies
4. Normalize or scale features as appropriate for algorithm
5. Split temporal data if time-series analysis is needed

### Step 2: Select Detection Algorithm

Choose appropriate anomaly detection method based on data characteristics:

- **Isolation Forest**: For high-dimensional data with complex anomalies
- **One-Class SVM**: For clearly defined normal behavior patterns
- **Local Outlier Factor (LOF)**: For density-based anomaly detection
- **Statistical Methods**: For simple univariate or multivariate analysis
- **Autoencoders**: For complex patterns in large datasets

### Step 3: Configure Detection Parameters

Set algorithm parameters to balance sensitivity:

- Define contamination rate (expected proportion of anomalies)
- Set distance metrics appropriate for feature types
- Configure threshold values for anomaly scoring
- Establish validation strategy for parameter tuning

### Step 4: Execute Anomaly Detection

Run the detection algorithm on prepared data:

1. Apply selected algorithm using Bash tool
2. Generate anomaly scores for each data point
3. Classify points as normal or anomalous based on threshold
4. Extract characteristics of identified anomalies

### Step 5: Analyze and Report Results

Interpret detection results and provide insights:

- Summarize number and distribution of anomalies
- Highlight most significant outliers with context
- Identify patterns or clusters among anomalies
- Generate visualizations showing anomaly distribution
- Provide recommendations for further investigation

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