Gemini for Data Analysis: CSV to Insights Pipeline

Analyze this dataset and provide actionable insights. Follow this analysis pipeline:

**Phase 1 — Data Understanding**:
- Describe each column: data type, meaning, and potential quality issues
- Show basic statistics: count, mean, median, std dev, min/max for numeric columns
- Show frequency counts for categorical columns
- Identify missing values and suggest handling strategies
- Flag potential data quality issues (outliers, inconsistencies, duplicates)

**Phase 2 — Exploratory Analysis**:
- Identify the top 3 most interesting patterns or correlations
- Describe trends over time if temporal data exists
- Segment data by key categories and compare metrics
- Calculate relevant derived metrics
- Find anomalies worth investigating

**Phase 3 — Visualization Recommendations**:
- For each key insight, suggest the best chart type and why
- Provide Python (matplotlib/seaborn) or JavaScript (Chart.js) code for each visualization
- Include proper labels, titles, and formatting

**Phase 4 — Actionable Recommendations**:
- Top 5 data-driven recommendations ranked by potential impact
- Each recommendation should include: the finding, the evidence, and the suggested action
- Risks or caveats for each recommendation

**Phase 5 — Further Investigation**:
- Questions the data raises but cannot answer alone
- Additional data sources that would enrich the analysis