# bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth architecture

> Architecture diagram of the GitHub repository bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-08-10). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth
- Repository: https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth

## Overview

#### Purpose and Scope
This is a Ghana-focused data science project evaluating whether social-media engagement predicts customer value, loyalty, purchasing, churn, and agribusiness profitability. It is an offline analytical workflow rather than a deployed application. The primary deliverable is evidence, predictive results, and generated analysis artifacts for agribusiness decision-making.

#### Entry Points and Execution Model
The interactive analytical entry point is the Jupyter notebook at `notebooks/Enhancing_Digital_Engagement_for_Agribusiness_Growth.ipynb`. Reusable data-access logic begins in `src/data/load_data.py`. Python dependencies are declared in `requirements.txt`, while `environment.yml` supports a reproducible Conda-based environment.

#### Data Ingestion and Privacy Boundary
Raw, cleaned, and engineered datasets are intentionally absent from the tracked tree; the README identifies `data/` as ignored. The ingestion boundary is therefore `src/data/load_data.py`, which should isolate dataset paths, schema loading, and any sensitive-data handling from notebook analysis. The project works from anonymized customer data, making local data storage and preprocessing the main privacy boundary.

#### Preprocessing and Validation
The first pipeline stage cleans the raw customer records into the analytical population through missing-value imputation, outlier winsorization, scaling, and integrity validation. Its generated artifacts are organized under `outputs/01-data-preprocessing-and-validation/`, with stage documentation at `outputs/01-data-preprocessing-and-validation/README.md`. This stage establishes the trusted feature set consumed by downstream EDA and models.

#### Exploration, Segmentation, and Feature Engineering
The notebook performs exploratory analysis across engagement, trust, purchases, platform use, and demographics, including composite engagement scoring and customer segmentation. Outputs are materialized under `outputs/02-exploratory-data-analysis/` and described by `outputs/02-exploratory-data-analysis/README.md`. Derived business features—such as Loyalty Resilience Score, Brand Relation Score, and customer-value metrics—form the boundary between descriptive analysis and supervised prediction.

#### Modeling and Evaluation
Predictive work uses regression and classification models: linear/logistic regression, Random Forest, XGBoost, and LightGBM, with tuning and explainability. The control flow is engineered features → model fitting → validation metrics and diagnostic plots, including R², accuracy, ROC-AUC, confusion matrices, and SHAP. Results and visual evaluations belong in `outputs/03-modeling-and-predictions/`, documented by `outputs/03-modeling-and-predictions/README.md`.

#### Outputs and External Dependencies
The repository produces static analytical artifacts rather than serving APIs or using cloud infrastructure. Core runtimes are Python plus Jupyter, managed through `requirements.txt` and `environment.yml`. No external service integrations are present in the tree; social-media platforms are analytical variables, not connected runtime dependencies. The report asset at `docs/Enhancing_Digital_Engagement_for_Agribusiness_Growth.pdf` is a stakeholder-facing presentation of the pipeline’s findings.

Main entry points: [`notebooks/Enhancing_Digital_Engagement_for_Agribusiness_Growth.ipynb`](https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/notebooks/Enhancing_Digital_Engagement_for_Agribusiness_Growth.ipynb) (Analysis notebook), [`src/data/load_data.py`](https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/src/data/load_data.py) (Data loading boundary), [`docs/Enhancing_Digital_Engagement_for_Agribusiness_Growth.pdf`](https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/docs/Enhancing_Digital_Engagement_for_Agribusiness_Growth.pdf) (Stakeholder report), [`requirements.txt`](https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/requirements.txt) (Python dependencies)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_runtime["Analytical runtime"]
  node_requirements["Python dependencies<br/>dependency manifest<br/>[requirements.txt]"]
  node_conda_env["Conda environment<br/>environment manifest<br/>[environment.yml]"]
  node_notebook["Analysis notebook<br/>Jupyter entry point"]
end

subgraph group_pipeline["Offline analysis pipeline"]
  node_local_data[("Anonymized local data<br/>ignored dataset")]
  node_loader["Data loading boundary<br/>Python data access<br/>[load_data.py]"]
  node_preprocessing["Preprocessing &amp; validation<br/>data preparation stage"]
  node_trusted_features[("Trusted feature set<br/>validated dataset")]
  node_eda["EDA &amp; segmentation<br/>descriptive analysis stage"]
  node_feature_engineering["Business feature engineering<br/>feature derivation stage"]
  node_predictive_models["Predictive modeling<br/>supervised modeling stage"]
  node_evaluation["Validation &amp; explainability<br/>model evaluation stage"]
end

subgraph group_artifacts["Generated evidence"]
  node_preprocess_outputs["Preprocessing artifacts<br/>output directory"]
  node_eda_outputs["EDA artifacts<br/>output directory"]
  node_model_outputs["Modeling artifacts<br/>output directory"]
  node_report["Stakeholder report<br/>PDF report"]
  node_preprocess_readme["Preprocessing stage notes<br/>stage documentation<br/>[README.md]"]
  node_eda_readme["EDA stage notes<br/>stage documentation<br/>[README.md]"]
  node_model_readme["Modeling stage notes<br/>stage documentation<br/>[README.md]"]
end

node_requirements -->|"provides packages"| node_notebook
node_conda_env -->|"reproduces runtime"| node_notebook
node_local_data -->|"loads anonymized records"| node_loader
node_loader -->|"supplies customer records"| node_preprocessing
node_notebook -->|"invokes"| node_loader
node_notebook -->|"orchestrates"| node_preprocessing
node_preprocessing -->|"cleans and validates"| node_trusted_features
node_trusted_features -->|"supports analysis"| node_eda
node_eda -->|"informs derived features"| node_feature_engineering
node_feature_engineering -->|"engineered features"| node_predictive_models
node_predictive_models -->|"predictions and fits"| node_evaluation
node_preprocessing -->|"writes artifacts"| node_preprocess_outputs
node_eda -->|"writes artifacts"| node_eda_outputs
node_evaluation -->|"writes metrics and plots"| node_model_outputs
node_preprocess_readme -.->|"documents"| node_preprocess_outputs
node_eda_readme -.->|"documents"| node_eda_outputs
node_model_readme -.->|"documents"| node_model_outputs
node_preprocess_outputs -.->|"supports findings"| node_report
node_eda_outputs -.->|"supports findings"| node_report
node_model_outputs -.->|"supports findings"| node_report

click node_requirements "https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/requirements.txt"
click node_conda_env "https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/environment.yml"
click node_notebook "https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/notebooks/Enhancing_Digital_Engagement_for_Agribusiness_Growth.ipynb"
click node_loader "https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/src/data/load_data.py"
click node_report "https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/docs/Enhancing_Digital_Engagement_for_Agribusiness_Growth.pdf"
click node_preprocess_readme "https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/outputs/01-data-preprocessing-and-validation/README.md"
click node_eda_readme "https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/outputs/02-exploratory-data-analysis/README.md"
click node_model_readme "https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/outputs/03-modeling-and-predictions/README.md"

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_requirements,node_conda_env,node_notebook toneBlue
class node_local_data,node_loader,node_preprocessing,node_trusted_features,node_eda,node_feature_engineering,node_predictive_models,node_evaluation toneAmber
class node_preprocess_outputs,node_eda_outputs,node_model_outputs,node_report,node_preprocess_readme,node_eda_readme,node_model_readme toneMint
```

## Components

### Analytical runtime

- [Python dependencies](https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/requirements.txt): dependency manifest · `requirements.txt`
- [Conda environment](https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/environment.yml): environment manifest · `environment.yml`
- [Analysis notebook](https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/notebooks/Enhancing_Digital_Engagement_for_Agribusiness_Growth.ipynb): Jupyter entry point · Interactive orchestrator for the end-to-end workflow. · `notebooks/Enhancing_Digital_Engagement_for_Agribusiness_Growth.ipynb`

### Offline analysis pipeline

- **Anonymized local data**: ignored dataset · Raw, cleaned, and engineered customer data remains outside version control.
- [Data loading boundary](https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/src/data/load_data.py): Python data access · Isolates dataset paths, schema loading, and sensitive-data handling. · `src/data/load_data.py`
- **Preprocessing & validation**: data preparation stage · Imputes, winsorizes, scales, and validates the analytical population.
- **Trusted feature set**: validated dataset
- **EDA & segmentation**: descriptive analysis stage · Analyzes engagement, trust, purchases, platforms, and demographics.
- **Business feature engineering**: feature derivation stage · Builds engagement, loyalty, brand-relation, and customer-value features.
- **Predictive modeling**: supervised modeling stage · Fits regression and classification models including RF, XGBoost, and LightGBM.
- **Validation & explainability**: model evaluation stage · Produces metrics, diagnostics, and SHAP-based explanations.

### Generated evidence

- **Preprocessing artifacts**: output directory
- **EDA artifacts**: output directory
- **Modeling artifacts**: output directory · Predictive results and visual evaluations.
- [Stakeholder report](https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/docs/Enhancing_Digital_Engagement_for_Agribusiness_Growth.pdf): PDF report · Presentation of pipeline findings for agribusiness decision-making. · `docs/Enhancing_Digital_Engagement_for_Agribusiness_Growth.pdf`
- [Preprocessing stage notes](https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/outputs/01-data-preprocessing-and-validation/README.md): stage documentation · `outputs/01-data-preprocessing-and-validation/README.md`
- [EDA stage notes](https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/outputs/02-exploratory-data-analysis/README.md): stage documentation · `outputs/02-exploratory-data-analysis/README.md`
- [Modeling stage notes](https://github.com/bhrajo/proj-00-digital-engagement-analytics-for-agribusiness-growth/blob/main/outputs/03-modeling-and-predictions/README.md): stage documentation · `outputs/03-modeling-and-predictions/README.md`

## Connections

- Python dependencies → Analysis notebook: provides packages
- Conda environment → Analysis notebook: reproduces runtime
- Anonymized local data → Data loading boundary: loads anonymized records
- Data loading boundary → Preprocessing & validation: supplies customer records
- Analysis notebook → Data loading boundary: invokes
- Analysis notebook → Preprocessing & validation: orchestrates
- Preprocessing & validation → Trusted feature set: cleans and validates
- Trusted feature set → EDA & segmentation: supports analysis
- EDA & segmentation → Business feature engineering: informs derived features
- Business feature engineering → Predictive modeling: engineered features
- Predictive modeling → Validation & explainability: predictions and fits
- Preprocessing & validation → Preprocessing artifacts: writes artifacts
- EDA & segmentation → EDA artifacts: writes artifacts
- Validation & explainability → Modeling artifacts: writes metrics and plots
- Preprocessing stage notes → Preprocessing artifacts: documents
- EDA stage notes → EDA artifacts: documents
- Modeling stage notes → Modeling artifacts: documents
- Preprocessing artifacts → Stakeholder report: supports findings
- EDA artifacts → Stakeholder report: supports findings
- Modeling artifacts → Stakeholder report: supports findings