How amnanaeem123-coder/smog-prediction-backend works
This repository is presented as an ML backend for smog-risk prediction, represented by a collection of notebooks rather than sampled application source. Notebook names indicate activities spanning data preparation, analysis, model training, forecasting, risk classification, and mapping. The README does not describe how these activities are wired together, and source excerpts are unavailable, so the graph shows the evidenced components without asserting runtime calls or data flows. The API-test notebook is excluded as test activity.
Components 9
Data Preparation
- Preprocessing
FYP_Week2_Preprocessing.ipynb - Exploratory Analysis
FYP_Week3_EDA.ipynb - Feature Engineering
FYP_Week4_FeatureEngineering.ipynb
Modeling
- Model Training
FYP_Week5_ModelTraining.ipynb - Forecast Fix
FYP_Week5b_ForecastFix.ipynb - Merge and Retrain
FYP_Pollution_Merge.ipynb - Merged Retraining
FYP_Week10_PollutionMerge_Retrain.ipynb
Prediction Outputs
- Risk Classification
FYP_Week7_RiskClassification.ipynb - Folium Map
FYP_Week8_FoliumMap.ipynb