# vishwajitm01/amazon-predictive-modelling-project- architecture

> Architecture diagram of the GitHub repository vishwajitm01/amazon-predictive-modelling-project-, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-26). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/vishwajitm01/amazon-predictive-modelling-project-
- Repository: https://github.com/vishwajitm01/amazon-predictive-modelling-project-

## Overview

This is a data-analysis project exploring Amazon electronics sales, not a deployed commerce service. Its documented workflow uses a Kaggle dataset of more than 40,000 products to investigate two outcomes: Best Seller classification, evaluated by sensitivity because of class imbalance, and purchased-volume prediction. The README supports these stages and their purpose, but no source excerpts were available, so internal model choices, preprocessing steps, and execution links beyond the documented workflow are not represented.

Main entry points: [`Amazon_Project.R`](https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R) (Analysis workflow)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data["Data"]
  node_kaggle_data[("Kaggle electronics data")]
  node_product_attributes["Product attributes<br/>[Amazon_Project.R]"]
end

subgraph group_analysis["Predictive analysis"]
  node_analysis_script["Analysis workflow<br/>[Amazon_Project.R]"]
  node_best_seller_model["Best Seller prediction<br/>[Amazon_Project.R]"]
  node_class_imbalance["Class imbalance<br/>[Amazon_Project.R]"]
  node_volume_model["Purchase volume prediction<br/>[Amazon_Project.R]"]
end

subgraph group_outcomes["Research outcomes"]
  node_sensitivity["Sensitivity evaluation<br/>[Amazon_Project.R]"]
  node_seller_decisions["Stocking and promotion insights"]
end

node_kaggle_data -->|"provides data"| node_analysis_script
node_analysis_script -->|"examines features"| node_product_attributes
node_analysis_script -->|"builds models"| node_best_seller_model
node_class_imbalance -->|"motivates metric"| node_sensitivity
node_best_seller_model -->|"evaluated by"| node_sensitivity
node_analysis_script -->|"builds models"| node_volume_model
node_best_seller_model -->|"informs"| node_seller_decisions
node_volume_model -->|"informs"| node_seller_decisions

click node_product_attributes "https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R"
click node_analysis_script "https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R"
click node_best_seller_model "https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R"
click node_class_imbalance "https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R"
click node_sensitivity "https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R"
click node_volume_model "https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R"

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_kaggle_data,node_product_attributes toneBlue
class node_analysis_script,node_best_seller_model,node_class_imbalance,node_volume_model toneAmber
class node_sensitivity,node_seller_decisions toneMint
```

## Components

### Data

- **Kaggle electronics data**
- [Product attributes](https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R): `Amazon_Project.R`

### Predictive analysis

- [Analysis workflow](https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R): `Amazon_Project.R`
- [Best Seller prediction](https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R): `Amazon_Project.R`
- [Class imbalance](https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R): `Amazon_Project.R`
- [Purchase volume prediction](https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R): `Amazon_Project.R`

### Research outcomes

- [Sensitivity evaluation](https://github.com/vishwajitm01/amazon-predictive-modelling-project-/blob/main/Amazon_Project.R): `Amazon_Project.R`
- **Stocking and promotion insights**

## Connections

- Kaggle electronics data → Analysis workflow: provides data
- Analysis workflow → Product attributes: examines features
- Analysis workflow → Best Seller prediction: builds models
- Class imbalance → Sensitivity evaluation: motivates metric
- Best Seller prediction → Sensitivity evaluation: evaluated by
- Analysis workflow → Purchase volume prediction: builds models
- Best Seller prediction → Stocking and promotion insights: informs
- Purchase volume prediction → Stocking and promotion insights: informs