saurov565/data-science

1. Repo shape

- This is an R-based data science workspace, not an application with a runtime service.

- The repository is split into two clear zones: a main project for supermarket sales analysis and a Lab area with standalone learning exercises.

2. Primary business problem

- The main analysis centers on a Supermarket Sales dataset.

- The goal is to understand customer behavior, spending patterns, and sales drivers, then prepare data for predictive and exploratory modeling.

3. Main pipeline boundary

- The repository’s core flow is: raw transactional data → cleaning → exploratory analysis → feature engineering → preprocessing → model-ready dataset.

- This boundary is important because the project appears focused on analysis and preparation, not deployment or operational APIs.

4. Mid-project script as the preparation layer

- H-G08 mid-project.r is likely the central data preparation / EDA script.

- Based on the README, this layer handles:

- missing values

- outliers

- distribution checks

- relationship exploration

- feature creation such as Total_Sales

- encoding and normalization

5. Final project modeling branches

- The Final Project folder contains three separate analysis scripts:

- H-G08 final-Regression.R

- H-G08 final-classification.R

- H-G08 final-clustering.R

- These form three model-specific branches that likely consume the cleaned/preprocessed dataset and explore different analytical tasks:

- regression for numeric prediction

- classification for category prediction

- clustering for customer segmentation

6. Shared data foundation across models

- All three final scripts likely operate on the same supermarket sales feature space, but with different targets or objectives.

- This means the architecture is not a monolithic pipeline; it is a shared preprocessing foundation with multiple downstream modeling paths.

7. Feature engineering and encoding layer

- The README identifies several important transformations that sit between raw data and modeling:

- Total_Sales = unit_price × Quantity

- categorical encoding for Branch, Gender, Customer_type, Product_line, Payment

- normalization and log transforms to reduce skew

- This is the main schema-conversion boundary in the project, turning business-facing transaction data into numeric model inputs.

8. Exploration and visualization subsystem

- The project uses ggplot2 and GGally for EDA.

- This subsystem likely produces the plots used to inspect distributions, correlations, and relationships before modeling.

- In an architecture diagram, this should be shown as a side path off the cleaned data, feeding insight back into preprocessing choices.

9. Tooling and execution model

- The stack is primarily R with dplyr, ggplot2, and GGally.

- The scripts are likely run interactively or sequentially in an R environment, with no evidence of orchestration, backend services, or external infrastructure.

- The repo therefore behaves like a script-based analytics project.

10. Lab folder role

- The Lab directory contains many independent .R exercises on basics, matrices, arrays, descriptive statistics, and data handling.

- These are best treated as learning/reference modules, not production dependencies of the final project.

- In an architecture view, they are separate from the main pipeline and should not be connected into the supermarket-sales flow.

11. What the architecture diagram should emphasize

- Show one main path from the dataset into cleaning/EDA/feature engineering, then branch into regression, classification, and clustering.

- Keep the Lab scripts outside the core flow as supporting educational material.

- There is no evident persistence layer, API boundary, or deployment target; the important architecture is analytical and script-centric.