# Sweetviz 1 Use Target Analysis For Ml Projects

> Sub-skill of sweetviz: 1. Use Target Analysis for ML Projects (+4).

- Skill: `vamseeachanta/sweetviz-1-use-target-analysis-for-ml-projects` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/sweetviz-1-use-target-analysis-for-ml-projects`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/sweetviz-1-use-target-analysis-for-ml-projects/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/sweetviz-1-use-target-analysis-for-ml-projects

---


# 1. Use Target Analysis for ML Projects (+4)

## 1. Use Target Analysis for ML Projects


```python
# GOOD: Always specify target for ML datasets
report = sv.analyze(df, target_feat="target")

# AVOID: Missing target analysis
report = sv.analyze(df)  # No target relationship shown
```


## 2. Sample Large Datasets


```python
# GOOD: Sample for large datasets
if len(df) > 100000:
    df_sample = df.sample(n=100000, random_state=42)
    report = sv.analyze(df_sample)
else:
    report = sv.analyze(df)

# AVOID: Analyzing huge datasets directly
# report = sv.analyze(df_with_millions_of_rows)  # Very slow
```


## 3. Configure Feature Types Properly


```python
# GOOD: Force categorical for ID-like numeric columns
config = sv.FeatureConfig(
    skip=["customer_id", "transaction_id"],
    force_cat=["zip_code", "area_code", "rating"]
)
report = sv.analyze(df, feat_cfg=config)

# AVOID: Letting Sweetviz treat zip codes as numeric
```


## 4. Use Comparison for Validation


```python
# GOOD: Compare train/test for data leakage detection
comparison = sv.compare(
    [train_df, "Train"],
    [test_df, "Test"],
    target_feat="target"
)

# AVOID: Only analyzing training data
```


## 5. Control Pairwise Analysis


```python
# GOOD: Disable for speed on many features
report = sv.analyze(df, pairwise_analysis="off")  # Fast

# GOOD: Enable when feature correlations matter
report = sv.analyze(df, pairwise_analysis="on")   # Full correlations
```