This project is an automated data analysis pipeline built with Python. It processes a raw dataset, performs data cleaning, generates insights, creates visualizations, and produces an analysis report automatically.
The goal of the project is to demonstrate how data analysts and Python developers can build automated systems that transform raw data into useful insights.
- Automated data cleaning
- Business insights generation
- Data visualization
- Automatic report generation
- Modular Python project structure
- Python
- Pandas
- Matplotlib
- Seaborn
python-data-analysis-pipeline
│
├── src
│ ├── main.py
│ ├── cleaner.py
│ ├── analyzer.py
│ ├── visualizer.py
│ └── reporter.py
│
├── data
│ └── raw
│
├── output
│ └── charts
│
├── requirements.txt
├── .gitignore
└── README.md
The pipeline follows these steps:
- Load the dataset
- Clean the data
- Perform analysis
- Generate charts
- Produce a summary report
Workflow:
Raw Dataset
↓
Data Cleaning
↓
Data Analysis
↓
Visualization
↓
Report Generation
The pipeline generates:
- Cleaned dataset
- Charts (sales by category, profit by region)
- Automated analysis report
Outputs are stored in the output folder.
The project uses a sales dataset inspired by the Global Superstore dataset commonly used in data analysis practice.
Place the dataset inside:
data/raw/
Example file:
sales_data.csv
Clone the repository:
git clone https://github.com/HothoLina/python-data-analysis-pipeline.git
Navigate to the project folder:
cd python-data-analysis-pipeline
Create a virtual environment:
python -m venv venv
Activate it:
Windows:
venv\Scripts\activate
Install dependencies:
pip install -r requirements.txt
Run the pipeline:
python src/main.py
- Add automated data validation
- Export reports as PDF or HTML
- Add interactive dashboards
- Integrate with databases
HothoLina Aspiring Python Developer | Data Analyst | Automation Enthusiast