| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - air-quality |
| - india |
| - cpcb |
| - pollution |
| - environment |
| - aqi |
| - cleaned |
| - machine-learning |
| pretty_name: VAYU — Cleaned & Model-Ready CPCB Air Quality Data (India) |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # VAYU — Cleaned and Model-Ready CPCB Air Quality Data |
|
|
| Cleaned, feature-engineered, and split datasets ready for ML training. |
| Produced by the VAYU data preparation pipeline from raw CPCB sensor data. |
|
|
| ## Contents |
|
|
| | Folder | File | Use With | |
| |---|---|---| |
| | `05_shared/` | `master_cleaned.parquet` | All models — primary cleaned file | |
| | `05_shared/` | `master_cleaned.csv` | Same, human-readable backup | |
| | `01_regression/` | `regression_train.csv` | Linear Regression, Multiple Regression | |
| | `01_regression/` | `regression_test.csv` | Linear Regression, Multiple Regression | |
| | `02_classification/` | `clf_train_scaled.csv` | Logistic Regression, KNN, SVM | |
| | `02_classification/` | `clf_train_unscaled.csv` | Decision Trees, Random Forest | |
| | `02_classification/` | `clf_test_scaled.csv` | Logistic Regression, KNN, SVM | |
| | `02_classification/` | `clf_test_unscaled.csv` | Decision Trees, Random Forest | |
| | `02_classification/` | `label_map.json` | Decode integer predictions to category names | |
| | `03_clustering/` | `city_profiles_scaled.csv` | K-Means clustering | |
| | `03_clustering/` | `city_clusters.csv` | City cluster assignments + labels | |
| | `04_dimensionality/` | `pollutant_matrix_scaled.csv` | PCA, t-SNE, SVD | |
| | `04_dimensionality/` | `pca_components.csv` | Pre-computed PCA coordinates | |
| | `04_dimensionality/` | `pca_loadings.csv` | Component loadings + variance explained | |
| | `04_dimensionality/` | `tsne_sample.csv` | t-SNE 2D coordinates (15k sample) | |
|
|
| ## Cleaning Operations Applied |
|
|
| 1. Sentinel value `999` → `NaN` (CPCB sensor error code) |
| 2. Physical range validation per pollutant (unit-aware — CO stored as µg/m³) |
| 3. Forward fill short gaps ≤ 3 hours within each city |
| 4. Drop rows where all pollutants are NaN (extended outages) |
| 5. Deduplicate on city + datetime |
| 6. Parse datetime, extract month / hour / day_of_week / season |
| 7. Derive AQI_category from numeric AQI using CPCB breakpoints |
| |
| ## Target Variables |
| |
| | Task | Target | Range | |
| |---|---|---| |
| | Regression | AQI (numeric) | 0 – 500 | |
| | Classification | AQI category (integer encoded) | 0 = Good → 5 = Severe | |
| | Clustering | None (unsupervised) | — | |
| |
| ## Related Repository |
| |
| Raw source data: |
| [rachitgoyell/vayu-raw](https://huggingface.co/datasets/rachitgoyell/vayu-raw) |
