# akanksha238/logistics-analysis architecture

> Architecture diagram of the GitHub repository akanksha238/logistics-analysis, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-29). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/akanksha238/logistics-analysis
- Repository: https://github.com/akanksha238/logistics-analysis

## Overview

Logistics Analyzer is a Python analytics project for simulated shipment operations. Its main workflow generates shipment records, analyzes SLA, cost and satisfaction patterns, then produces charts. The repository also contains separate predictive-modeling and mode-allocation optimization scripts, plus cleaning, forecasting and clustering modules. The README describes the core pipeline; links to less-documented subsystems are omitted where sampled source does not establish their runtime wiring.

Main entry points: [`logistics_dataset.csv`](https://github.com/akanksha238/logistics-analysis/blob/main/logistics_dataset.csv) (Shipment dataset), [`run_pipeline.py`](https://github.com/akanksha238/logistics-analysis/blob/main/run_pipeline.py) (Pipeline runner), [`eda.py`](https://github.com/akanksha238/logistics-analysis/blob/main/eda.py) (Exploratory analysis), [`visualize.py`](https://github.com/akanksha238/logistics-analysis/blob/main/visualize.py) (Chart generation)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_pipeline["Core pipeline"]
  node_runner["Pipeline runner<br/>[run_pipeline.py]"]
  node_simulator["Shipment simulator<br/>[simulate_data.py]"]
  node_dataset[("Shipment dataset")]
end

subgraph group_insights["Operational insights"]
  node_eda["Exploratory analysis<br/>[eda.py]"]
  node_summary["EDA summary<br/>[eda_summary.txt]"]
  node_visualizer["Chart generation<br/>[visualize.py]"]
end

subgraph group_decision["Decision support"]
  node_model["Delivery prediction"]
  node_modelcharts["Model visualizations"]
  node_optimizer["Mode allocation"]
end

subgraph group_domain["Additional analysis"]
  node_cleaning["Data cleaning<br/>[data_cleaning.py]"]
  node_forecasting["Demand forecasting"]
  node_clustering["Zone clustering<br/>[zone_clustering.py]"]
end

node_operator(("Analyst"))

node_operator -->|"runs"| node_runner
node_runner -->|"calls"| node_simulator
node_runner -->|"calls"| node_eda
node_runner -->|"calls"| node_visualizer
node_simulator -->|"writes"| node_dataset
node_eda -->|"reads"| node_dataset
node_eda -->|"writes"| node_summary
node_visualizer -->|"reads"| node_dataset
node_visualizer -->|"loads data via"| node_eda
node_model -->|"reads"| node_dataset
node_modelcharts -->|"uses model results"| node_model
node_optimizer -->|"reads"| node_dataset

click node_runner "https://github.com/akanksha238/logistics-analysis/blob/main/run_pipeline.py"
click node_simulator "https://github.com/akanksha238/logistics-analysis/blob/main/simulate_data.py"
click node_dataset "https://github.com/akanksha238/logistics-analysis/blob/main/logistics_dataset.csv"
click node_eda "https://github.com/akanksha238/logistics-analysis/blob/main/eda.py"
click node_summary "https://github.com/akanksha238/logistics-analysis/blob/main/eda_summary.txt"
click node_visualizer "https://github.com/akanksha238/logistics-analysis/blob/main/visualize.py"
click node_model "https://github.com/akanksha238/logistics-analysis/blob/main/logistics_analyzer.py"
click node_modelcharts "https://github.com/akanksha238/logistics-analysis/blob/main/week4_03_viz_modeling.py"
click node_optimizer "https://github.com/akanksha238/logistics-analysis/blob/main/week4_02_optimization.py"
click node_cleaning "https://github.com/akanksha238/logistics-analysis/blob/main/data_cleaning.py"
click node_forecasting "https://github.com/akanksha238/logistics-analysis/blob/main/demand_forecasting.py"
click node_clustering "https://github.com/akanksha238/logistics-analysis/blob/main/zone_clustering.py"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_runner,node_simulator,node_dataset toneBlue
class node_eda,node_summary,node_visualizer toneAmber
class node_model,node_modelcharts,node_optimizer toneMint
class node_cleaning,node_forecasting,node_clustering toneRose
class node_operator toneIndigo
```

## Components

### Core pipeline

- [Pipeline runner](https://github.com/akanksha238/logistics-analysis/blob/main/run_pipeline.py): `run_pipeline.py`
- [Shipment simulator](https://github.com/akanksha238/logistics-analysis/blob/main/simulate_data.py): `simulate_data.py`
- [Shipment dataset](https://github.com/akanksha238/logistics-analysis/blob/main/logistics_dataset.csv): `logistics_dataset.csv`

### Operational insights

- [Exploratory analysis](https://github.com/akanksha238/logistics-analysis/blob/main/eda.py): `eda.py`
- [EDA summary](https://github.com/akanksha238/logistics-analysis/blob/main/eda_summary.txt): `eda_summary.txt`
- [Chart generation](https://github.com/akanksha238/logistics-analysis/blob/main/visualize.py): `visualize.py`

### Decision support

- [Delivery prediction](https://github.com/akanksha238/logistics-analysis/blob/main/logistics_analyzer.py): `logistics_analyzer.py`
- [Model visualizations](https://github.com/akanksha238/logistics-analysis/blob/main/week4_03_viz_modeling.py): `week4_03_viz_modeling.py`
- [Mode allocation](https://github.com/akanksha238/logistics-analysis/blob/main/week4_02_optimization.py): `week4_02_optimization.py`

### Additional analysis

- [Data cleaning](https://github.com/akanksha238/logistics-analysis/blob/main/data_cleaning.py): `data_cleaning.py`
- [Demand forecasting](https://github.com/akanksha238/logistics-analysis/blob/main/demand_forecasting.py): `demand_forecasting.py`
- [Zone clustering](https://github.com/akanksha238/logistics-analysis/blob/main/zone_clustering.py): `zone_clustering.py`

### Other components

- **Analyst**

## Connections

- Analyst → Pipeline runner: runs
- Pipeline runner → Shipment simulator: calls
- Pipeline runner → Exploratory analysis: calls
- Pipeline runner → Chart generation: calls
- Shipment simulator → Shipment dataset: writes
- Exploratory analysis → Shipment dataset: reads
- Exploratory analysis → EDA summary: writes
- Chart generation → Shipment dataset: reads
- Chart generation → Exploratory analysis: loads data via
- Delivery prediction → Shipment dataset: reads
- Model visualizations → Delivery prediction: uses model results
- Mode allocation → Shipment dataset: reads