# sreejabethu/sales-data-analysis-forecasting architecture

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

- Interactive diagram: https://gitdiagram.com/sreejabethu/sales-data-analysis-forecasting
- Repository: https://github.com/sreejabethu/sales-data-analysis-forecasting

## Overview

This is a small, single-script sales-data analysis project. The workflow starts with a user running `main.py`, which loads the product dataset, cleans and filters it, then produces rating-distribution and category-level visualizations. The README also describes interpreting rating and sales-related trends. The source shows the implemented cleaning and plots; the exact sales-trend analysis described in the README is not evident in the excerpt, so it is represented as a documented capability without an asserted implementation link.

Main entry points: [`main.py`](https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py) (Analysis script), [`amazon.csv`](https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/amazon.csv) (Product data)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data["Data preparation"]
  node_runner["Analysis script<br/>[main.py]"]
  node_dataset["Product data<br/>[amazon.csv]"]
  node_cleaning["Clean and filter<br/>[main.py]"]
end

subgraph group_analysis["Exploratory analysis"]
  node_rating_analysis["Rating distribution<br/>[main.py]"]
  node_category_analysis["Category metrics<br/>[main.py]"]
  node_sales_trends["Sales and ratings trends<br/>[README.md]"]
end

subgraph group_presentation["Visualizations"]
  node_rating_plot["Rating histogram<br/>[main.py]"]
  node_discount_plot["Discount chart<br/>[main.py]"]
  node_rating_count_plot["Rating-count chart<br/>[main.py]"]
end

node_user(("Analyst"))

node_user -->|"runs"| node_runner
node_runner -->|"loads"| node_dataset
node_runner -->|"cleans"| node_cleaning
node_cleaning -->|"provides data"| node_rating_analysis
node_cleaning -->|"provides data"| node_category_analysis
node_rating_analysis -->|"plots"| node_rating_plot
node_category_analysis -->|"plots discounts"| node_discount_plot
node_category_analysis -->|"plots counts"| node_rating_count_plot

click node_runner "https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py"
click node_dataset "https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/amazon.csv"
click node_cleaning "https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py"
click node_rating_analysis "https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py"
click node_category_analysis "https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py"
click node_sales_trends "https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/README.md"
click node_rating_plot "https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py"
click node_discount_plot "https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py"
click node_rating_count_plot "https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.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_dataset,node_cleaning toneBlue
class node_rating_analysis,node_category_analysis,node_sales_trends toneAmber
class node_rating_plot,node_discount_plot,node_rating_count_plot toneMint
class node_user toneIndigo
```

## Components

### Data preparation

- [Analysis script](https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py): `main.py`
- [Product data](https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/amazon.csv): `amazon.csv`
- [Clean and filter](https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py): `main.py`

### Exploratory analysis

- [Rating distribution](https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py): `main.py`
- [Category metrics](https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py): `main.py`
- [Sales and ratings trends](https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/README.md): `README.md`

### Visualizations

- [Rating histogram](https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py): `main.py`
- [Discount chart](https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py): `main.py`
- [Rating-count chart](https://github.com/sreejabethu/sales-data-analysis-forecasting/blob/main/main.py): `main.py`

### Other components

- **Analyst**

## Connections

- Analyst → Analysis script: runs (evidence: `README.md`)
- Analysis script → Product data: loads (evidence: `main.py`)
- Analysis script → Clean and filter: cleans (evidence: `main.py`)
- Clean and filter → Rating distribution: provides data (evidence: `main.py`)
- Clean and filter → Category metrics: provides data (evidence: `main.py`)
- Rating distribution → Rating histogram: plots (evidence: `main.py`)
- Category metrics → Discount chart: plots discounts (evidence: `main.py`)
- Category metrics → Rating-count chart: plots counts (evidence: `main.py`)