# mohammed-abu-oudah/predication-of-product-sales architecture

> Architecture diagram of the GitHub repository mohammed-abu-oudah/predication-of-product-sales, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-27). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/mohammed-abu-oudah/predication-of-product-sales
- Repository: https://github.com/mohammed-abu-oudah/predication-of-product-sales

## Overview

This repository is a notebook-based product-sales prediction project. Its documented workflow inspects and cleans data, uses plots to explore distributions and relationships, then trains and compares linear regression and random-forest regression; GridSearchCV is used to tune the random forest. The README reports overall weak performance and suggests feature removal or outlier processing as possible improvements. The tree contains only the notebook and README, so the graph uses the notebook as the supported path for each workflow stage; its internal implementation and exact data flow could not be verified from source excerpts.

Main entry points: [`Prediction_of_Product_Sales.ipynb`](https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb) (Inspect and clean)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_preparation["Data preparation"]
  node_inspect_clean["Inspect and clean"]
end

subgraph group_exploration["Data exploration"]
  node_visualize["Explore with plots"]
end

subgraph group_modeling["Modeling"]
  node_linear_regression["Linear regression"]
  node_random_forest["Random forest"]
  node_grid_search["GridSearchCV tuning"]
  node_model_results["Model results"]
end

node_analyst(("Analyst"))
node_sales_data["Sales data"]

node_analyst -->|"runs"| node_inspect_clean
node_sales_data -->|"input"| node_inspect_clean
node_inspect_clean -->|"explores"| node_visualize
node_inspect_clean -->|"trains on"| node_linear_regression
node_inspect_clean -->|"trains on"| node_random_forest
node_random_forest -->|"tunes with"| node_grid_search
node_linear_regression -->|"reports"| node_model_results
node_random_forest -->|"reports"| node_model_results
node_grid_search -->|"reports"| node_model_results
node_model_results -->|"presents"| node_analyst

click node_inspect_clean "https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb"
click node_visualize "https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb"
click node_linear_regression "https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb"
click node_random_forest "https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb"
click node_grid_search "https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb"
click node_model_results "https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb"

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_inspect_clean toneBlue
class node_visualize toneAmber
class node_linear_regression,node_random_forest,node_grid_search,node_model_results toneMint
class node_analyst,node_sales_data toneIndigo
```

## Components

### Data preparation

- [Inspect and clean](https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb): `Prediction_of_Product_Sales.ipynb`

### Data exploration

- [Explore with plots](https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb): `Prediction_of_Product_Sales.ipynb`

### Modeling

- [Linear regression](https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb): `Prediction_of_Product_Sales.ipynb`
- [Random forest](https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb): `Prediction_of_Product_Sales.ipynb`
- [GridSearchCV tuning](https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb): `Prediction_of_Product_Sales.ipynb`
- [Model results](https://github.com/mohammed-abu-oudah/predication-of-product-sales/blob/main/Prediction_of_Product_Sales.ipynb): `Prediction_of_Product_Sales.ipynb`

### Other components

- **Analyst**
- **Sales data**

## Connections

- Analyst → Inspect and clean: runs
- Sales data → Inspect and clean: input
- Inspect and clean → Explore with plots: explores
- Inspect and clean → Linear regression: trains on
- Inspect and clean → Random forest: trains on
- Random forest → GridSearchCV tuning: tunes with
- Linear regression → Model results: reports
- Random forest → Model results: reports
- GridSearchCV tuning → Model results: reports
- Model results → Analyst: presents