Dashboard Overview
Summary metrics and dataset health audit
Dataset Overview
Data structure, memory requirements, and target definition
| Property | Details |
|---|---|
| Dataset Shape | 1015 Rows × 7 Columns |
| Memory Footprint | 145.74 KB |
| Total Data Points | cells |
| Target Column | Is_Promoted |
| Data Types Breakdown | Numeric, Categorical |
Variables Analysis
Per-feature metrics, distribution charts, and summary statistics
Missing Values Analysis
Missingness distributions, percentages, and imputation needs
| Column Name | Missing Count | Missing Percentage | Status |
|---|---|---|---|
| Salary | 121 | 11.92% | Moderate |
| Age | 50 | 4.93% | Moderate |
| Education | 31 | 3.05% | Moderate |
| Experience | 0 | 0.0% | Complete |
| Department | 0 | 0.0% | Complete |
| Is_Promoted | 0 | 0.0% | Complete |
| Constant_Feature | 0 | 0.0% | Complete |
Duplicate & Redundancy Analysis
Row-level duplicates, zero-variance columns, and constant features
Constant Columns Detected
These features contain only a single unique value across all rows:
Constant_Feature
Correlation Analysis
Inter-feature correlation heatmaps and multicollinearity audits
Feature Distributions
Histograms, box plots, and value frequency distributions
Numerical Feature Distributions
Categorical Frequency Distributions
Outlier Analysis
Interquartile Range (IQR) and Z-score anomaly detection
| Column | IQR Outlier Count | IQR Outlier % | IQR Bounds [Lower, Upper] | Z-Score Outliers (>3σ) |
|---|---|---|---|---|
| Salary | 49 | 5.48% | [-69319.9242, 153360.3019] |
4 |
| Age | 8 | 0.83% | [4.7984, 65.7268] |
3 |
| Experience | 0 | 0.0% | [-12.5, 31.5] |
0 |
| Constant_Feature | 0 | 0.0% | [100.0, 100.0] |
0 |
Target Analysis & Feature Importance
Target class balance, feature correlations, and Machine Learning insights
Actionable Preprocessing Recommendations
Automated recommendations engine suggestions
11.92% missing values & skewed distribution (skew=12.2372).
Use Median Imputation (`df['col'].fillna(df['col'].median())`) to avoid outlier distortion.
4.93% missing values with symmetric distribution.
Use Mean Imputation (`df['col'].fillna(df['col'].mean())`) or KNN Imputer.
3.05% missing values in categorical feature.
Use Mode Imputation or introduce a distinct `'Missing'` category tag.
Right-skewed feature distribution (skew = 12.2372).
Apply Log Transformation (`np.log1p(df['col'])`) to normalize distribution.
49 outliers (5.48%) detected via IQR method.
Apply Winsorization or Quantile Clipping between -69319.9242 and 153360.3019.
Constant column with single value.
Drop column `Constant_Feature` as it carries zero variance and no predictive signal.