# mainakadak02/end-to-endsalesforecasting architecture

> Architecture diagram of the GitHub repository mainakadak02/end-to-endsalesforecasting, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-11-12). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/mainakadak02/end-to-endsalesforecasting
- Repository: https://github.com/mainakadak02/end-to-endsalesforecasting

## Overview

This is a Streamlit retail-sales analytics dashboard. Users select an overview, forecast, anomaly report, or product-segmentation view; the app loads and prepares the sales CSV, then computes and visualizes the selected analysis. The distinct analysis stages and data-loading layer are all implemented in app.py. The repository provides no README and has only one substantive source file, so the graph stays compact; all shown relationships are evidenced by that excerpt.

Main entry points: [`app.py`](https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py) (Streamlit Dashboard), [`train.csv`](https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/train.csv) (Training Data)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_interface["Dashboard"]
  node_app["Streamlit Dashboard<br/>[app.py]"]
end

subgraph group_analytics["Sales Analytics"]
  node_overview["Sales Overview<br/>[app.py]"]
  node_forecast["Sales Forecast<br/>[app.py]"]
  node_anomalies["Anomaly Detection<br/>[app.py]"]
  node_segments["Product Clustering<br/>[app.py]"]
end

subgraph group_data["Data"]
  node_loader["Data Loader<br/>[app.py]"]
  node_sales_data["Training Data<br/>[train.csv]"]
end

node_user(("Dashboard User"))

node_user -->|"selects view"| node_app
node_app -->|"loads data"| node_loader
node_loader -->|"reads CSV"| node_sales_data
node_app -->|"renders selected"| node_overview
node_app -->|"renders selected"| node_forecast
node_app -->|"renders selected"| node_anomalies
node_app -->|"renders selected"| node_segments
node_overview -->|"uses loaded data"| node_loader
node_forecast -->|"uses loaded data"| node_loader
node_anomalies -->|"uses loaded data"| node_loader
node_segments -->|"uses loaded data"| node_loader

click node_app "https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py"
click node_overview "https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py"
click node_forecast "https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py"
click node_anomalies "https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py"
click node_segments "https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py"
click node_loader "https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py"
click node_sales_data "https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/train.csv"

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_app,node_user toneBlue
class node_overview,node_forecast,node_anomalies,node_segments toneAmber
class node_loader,node_sales_data toneMint
```

## Components

### Dashboard

- [Streamlit Dashboard](https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py): `app.py`

### Sales Analytics

- [Sales Overview](https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py): `app.py`
- [Sales Forecast](https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py): `app.py`
- [Anomaly Detection](https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py): `app.py`
- [Product Clustering](https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py): `app.py`

### Data

- [Data Loader](https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/app.py): `app.py`
- [Training Data](https://github.com/mainakadak02/end-to-endsalesforecasting/blob/main/train.csv): `train.csv`

### Other components

- **Dashboard User**

## Connections

- Dashboard User → Streamlit Dashboard: selects view (evidence: `app.py`)
- Streamlit Dashboard → Data Loader: loads data (evidence: `app.py`)
- Data Loader → Training Data: reads CSV (evidence: `app.py`)
- Streamlit Dashboard → Sales Overview: renders selected (evidence: `app.py`)
- Streamlit Dashboard → Sales Forecast: renders selected (evidence: `app.py`)
- Streamlit Dashboard → Anomaly Detection: renders selected (evidence: `app.py`)
- Streamlit Dashboard → Product Clustering: renders selected (evidence: `app.py`)
- Sales Overview → Data Loader: uses loaded data (evidence: `app.py`)
- Sales Forecast → Data Loader: uses loaded data (evidence: `app.py`)
- Anomaly Detection → Data Loader: uses loaded data (evidence: `app.py`)
- Product Clustering → Data Loader: uses loaded data (evidence: `app.py`)