Data Overview
What you'll see here
Dataset shape, data types, duplicate and missing-value checks, statistical summary, and the class imbalance that drives every later decision.
Training set
20,000 × 41
40 predictors + Target
Test set
5,000 × 41
held out until the end
Failure rate (train)
5.55%
1,110 failures
Missing values
36
V1: 18, V2: 18 (train) · 11 in test
Target balance (training set)
Class imbalance (5.55% failures) drives every modelling decision that follows.
Data quality checks
| Check | Result |
|---|---|
| Data types | All 40 predictors float64; Target int64 |
| Duplicate rows (train / test) | 0 / 0 |
| Missing cells (train / test) | 36 / 11 |
| Value range across predictors | -20.37 to 23.63 |
| Mean IQR-outlier share | 1.25% of values |
Statistical summary
First 8 of 40 predictors. All are continuous
float64
, roughly centred, and on comparable but not identical scales — so standardisation is mandatory.| Variable | Mean | Std | Min | Median | Max |
|---|---|---|---|---|---|
| V1 | -0.27 | 3.44 | -11.88 | -0.75 | 15.49 |
| V2 | 0.44 | 3.15 | -12.32 | 0.47 | 13.09 |
| V3 | 2.48 | 3.39 | -10.71 | 2.26 | 17.09 |
| V4 | -0.08 | 3.43 | -15.08 | -0.14 | 13.24 |
| V5 | -0.05 | 2.1 | -8.6 | -0.1 | 8.13 |
| V6 | -1 | 2.04 | -10.23 | -1 | 6.98 |
| V7 | -0.88 | 1.76 | -7.95 | -0.92 | 8.01 |
| V8 | -0.55 | 3.3 | -15.66 | -0.39 | 11.68 |
- Means fall between -3.61 and 2.48; standard deviations between 1.65 and 5.5.
- No categorical variables, so no encoding is required — only scaling.
- Predictor values span -20.37 to 23.63.
AI: Data quality & class balance
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