https://github.com/roydevashish/algo8.ai-data-manipulation-assignment

This assignment performs transaction-level sales data analysis and generates reports using Pandas / SQL / Spark inside a containerized environment. The dataset contains sales transaction records and is used to analyze SKUs, customers, and sales representative performance.
https://github.com/roydevashish/algo8.ai-data-manipulation-assignment

data-analysis duckdb python3 sql uv

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This assignment performs transaction-level sales data analysis and generates reports using Pandas / SQL / Spark inside a containerized environment. The dataset contains sales transaction records and is used to analyze SKUs, customers, and sales representative performance.

• Host: GitHub
• URL: https://github.com/roydevashish/algo8.ai-data-manipulation-assignment
• Owner: roydevashish
• Created: 2025-09-28T06:23:40.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-09-28T06:34:52.000Z (9 months ago)
• Last Synced: 2025-10-07T10:01:21.730Z (8 months ago)
• Topics: data-analysis, duckdb, python3, sql, uv
• Language: Python
• Homepage:
• Size: 2.52 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 📝 Algo8.ai: Data Manipulation Assignment (Using Pandas / SQL / Spark)

This assignment performs **transaction-level sales data analysis** and generates reports using **Pandas / SQL / Spark** inside a containerized environment.  

The dataset contains sales transaction records and is used to analyze **SKUs, customers, and sales representative performance**.  

The project also generates **Excel reports for the top-performing sales representatives**.  

---

## 📂 Project Structure

```

.

├── Dockerfile              # Container setup with Ubuntu, Python, uv

├── output/                 # Stores all generated reports

├── resource/               # Contains dataset and assignment instructions

├── src/                    # Source code and scripts for data analysis

└── README.md               # Project documentation

```

---

## 📊 Dataset Description

The dataset contains **transaction-level sales information**. Each row represents a sales entry recorded by a sales representative during a customer visit.  

### Columns:

- `ENTRY_ID` → Unique entry/transaction identifier  

- `SALES_REP_ID` → Unique ID for each sales representative  

- `SALES_REP` → Name of the sales representative  

- `CUSTOMER_ID` → Unique ID for each customer  

- `CUSTOMER_CODE` → Business/system-specific customer code  

- `CUSTOMER_NAME` → Full name of the customer  

- `SKU_NAME` → Product (Stock Keeping Unit) sold  

- `UNIT_SOLD` → Quantity of SKU sold  

- `TOTAL_VALUE_SOLD` → Total value of SKU sold  

- `CHECKIN_TIME` → Start time of customer visit  

- `CHECKOUT_TIME` → End time of customer visit  

---

## 📝 Assignment Tasks

### 1. Top 10 SKUs Analysis

- Top 10 most-selling SKUs  

  - By **Quantity Sold**  

  - By **Value Sold**  

- Top 10 least-selling SKUs  

  - By **Quantity Sold**  

  - By **Value Sold**  

### 2. Customer Analysis

- Top 10 Customers by **Total Value Purchased**  

### 3. Sales Representative Performance

- Top 10 Sales Representatives  

  - By **Value Sold**  

  - By **Time Spent**  

- For Top 10 Sales Reps (by Value):  

  - **Day-wise Average Value Sold**  

  - **Day-wise Average Time Spent**  

### 4. Reports for Top 3 Sales Representatives

- **Day-wise Transactions Sheets** with:  

  - SKU Sold  

  - Price per SKU  

  - Quantity Sold  

  - Value Sold  

- **Summary Report Sheet** with:  

  - Date  

  - Total Quantity Sold  

  - Total Value Sold  

  - Unique SKUs Sold  

  - Unique Customers Served  

  - Number of Visits  

  - Conversion %  

  - Total Time Spent  

---

## 🐳 Running with Docker

This project is containerized using **Docker** with `ubuntu:latest` and **Python3**.  

Dependencies are managed using **uv**.  

### 🔧 Build the Docker Image

```bash

docker build -t sales-analysis .

```

### 🚀 Run the Container

```bash

docker run -it -v /path/to/workspace/assignment:/workspace sales-analysis

```

Here:  

- `-v` maps your **local workspace** to `/workspace` inside the container.  

- All reports will be available in the `/workspace/output` directory.  

---

## ▶️ Usage

Once inside the container, run the following command to generate all reports:

```bash

uv run src/report.py

ur run src/generate_excle.py

```

This will process the dataset from `resource/`, perform the analysis, and save the results in the `output/` folder.  

---

## 📑 Output

The `output/` directory contains:  

1. **CSV Reports**  

   - **Top 10 SKUs Analysis**  

     - Top 10 most-selling SKUs  

       - By Quantity Sold  

       - By Value Sold  

     - Top 10 least-selling SKUs  

       - By Quantity Sold  

       - By Value Sold  

   - **Customer Analysis**  

     - Top 10 Customers by Total Value Purchased  

   - **Sales Representative Performance**  

     - Top 10 Sales Representatives  

       - By Value Sold  

       - By Time Spent  

     - For Top 10 Sales Reps (by Value):  

       - Day-wise Average Value Sold  

       - Day-wise Average Time Spent  

2. **Excel Reports** (for Top 3 Sales Representatives)  

   - **Day-wise transactions**  

   - **Summary report**  

---

## 👨‍💻 Tech Stack

- **Python 3** (Data Processing)  

- **Pandas / SQL / Spark** (Data Analysis)  

- **OpenPyXL** (Excel Report Generation)  

- **Docker** (Environment Management)  

- **uv** (Python dependency manager)  

---

## 📬 Submitted By

**Devashish Roy - 2024PGCSIS08**  

Master of Technology, Information Systems Security Engineering  

NIT Jamshedpur