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Credit Card Fraud
💳· Target: 83% accuracy
Data Preview
Features
Scaling
Train
Dataset Overview
284,807 rows20
TOTAL COLUMNS
18
NUMERIC FEATURES
0
MISSING COLUMNS
17
OUTLIER COLUMNS
Data Quality Issues Detected
• Amount: contains outliers
• V1 (PCA): contains outliers
• V2 (PCA): contains outliers
• V3–V10 (PCA): contains outliers
• V11–V28 (PCA): contains outliers
• V4 (PCA): contains outliers
• V5 (PCA): contains outliers
• V6 (PCA): contains outliers
• V7 (PCA): contains outliers
• V8 (PCA): contains outliers
• V9 (PCA): contains outliers
• V10 (PCA): contains outliers
• V11 (PCA): contains outliers
• V12 (PCA): contains outliers
• V13 (PCA): contains outliers
• V14 (PCA): contains outliers
• V15 (PCA): contains outliers
| Column | Type | Sample Values | Distribution | Missing | Outliers | Importance |
|---|---|---|---|---|---|---|
Class TARGETTarget: 0 = Normal, 1 = Fraud (0.17% fraud) | Target | 000 | — | None | No | |
Amount Transaction amount in USD | Numeric | 149.622.69378.66 | μ=88.35 σ=250.12 | None | Yes | 75% |
Time Seconds since first transaction | Numeric | 011 | μ=94813 σ=47488 | None | No | 18% |
V1 (PCA) PCA-transformed feature 1 (anonymized) | Numeric | -1.361.19-1.36 | μ=0 σ=1.96 | None | Yes | 81% |
V2 (PCA) PCA-transformed feature 2 (anonymized) | Numeric | -0.070.270.16 | μ=0 σ=1.65 | None | Yes | 62% |
V3–V10 (PCA) PCA-transformed features 3–10 | Numeric | 2.54-0.341.77 | μ=0 σ=1.4 | None | Yes | 55% |
V11–V28 (PCA) PCA-transformed features 11–28 (lower importance) | Numeric | 0.11-0.090.25 | μ=0 σ=0.9 | None | Yes | 33% |
V4 (PCA) PCA component | Numeric | 0.440.120.33 | μ=0 σ=1.4 | None | Yes | 58% |
V5 (PCA) PCA component | Numeric | -0.340.55-0.11 | μ=0 σ=1.3 | None | Yes | 45% |
V6 (PCA) PCA component | Numeric | 0.15-0.080.45 | μ=0 σ=1.3 | None | Yes | 21% |
V7 (PCA) PCA component | Numeric | 0.230.140.88 | μ=0 σ=1.2 | None | Yes | 52% |
V8 (PCA) PCA component | Numeric | 0.09-0.110.05 | μ=0 σ=1.1 | None | Yes | 18% |
V9 (PCA) PCA component | Numeric | -0.550.22-0.18 | μ=0 σ=1 | None | Yes | 35% |
V10 (PCA) PCA component | Numeric | 0.22-0.330.15 | μ=0 σ=1 | None | Yes | 61% |
V11 (PCA) PCA component | Numeric | -0.110.45-0.22 | μ=0 σ=1 | None | Yes | 48% |
V12 (PCA) PCA component | Numeric | 0.33-0.180.42 | μ=0 σ=0.9 | None | Yes | 55% |
V13 (PCA) PCA component | Numeric | -0.220.15-0.38 | μ=0 σ=0.9 | None | Yes | 12% |
V14 (PCA) PCA component | Numeric | 0.18-0.420.25 | μ=0 σ=0.9 | None | Yes | 65% |
V15 (PCA) PCA component | Numeric | -0.140.22-0.11 | μ=0 σ=0.9 | None | Yes | 8% |
Is Overnight Transaction occurred between 12am-6am | Binary | 01 | — | None | No | 11% |
💡 Review the data carefully — understanding your features helps you make better preprocessing choices.
── PIPELINE SCORE ────
64/100
Accuracy modifier: ×1.01
Features
78
Scaling
65
Outliers
30
Architect
75
⚡ Remove low-importance features (<25%) to reduce noise.
⚡ Some features are highly skewed — try Log or Sqrt normalization.
⚡ You have outlier columns — consider clipping or imputing them.
Step 1 of 3
Score: 64/100