Automation Projects

This repository contains a set of Python automation exercises covering file handling, CSV processing, backups, machine learning, and API workflows.

Setup

1. Create and activate a virtual environment from the repository root.
2. Install the common dependencies:

pip install pandas scikit-learn joblib fastapi uvicorn requests prefect

3. Run commands from the automation_projects/ root so the relative paths in the scripts resolve correctly.

Project List

• project1_rename/ - rename files in the downloads/ folder by normalizing file names
• project2_mover/ - move mixed file types into organized subfolders
• project3_api/ - stores raw country data pulled from an API
• project3_csv/ - combine, clean, and analyze sales CSV data
• project4_backup/ - create timestamped zip backups with rotation and logging
• project5_ml/ - train and use a revenue prediction model
• project6_api/ - FastAPI app that serves the trained model
• project7_prefect/ - Prefect flow that fetches country data and retrains the model

Run Instructions

project1_rename

Normalize the file names inside project1_rename/downloads/:

python project1_rename/renamefiles.py

project2_mover

Organize the files inside project2_mover/mixed_files/ into type-based folders:

python project2_mover/file_organizer.py

project3_csv

Combine the monthly sales files, then clean and inspect the data:

python project3_csv/combine_sales.py
python project3_csv/cleaner.py
python project3_csv/sales_exercises.py

combine_sales.py writes combined_sales.csv and summary_report.json. cleaner.py reads messy_sales.csv and produces cleaned_sales.csv plus cleaning_report.json.

project4_backup

Create a timestamped zip backup of the automation_projects/ folder and update the backup log:

python project4_backup/backup_system.py

project5_ml

Train the revenue model on the cleaned sales data:

python project5_ml/train.py

Run a prediction from the saved model:

python project5_ml/predict.py --units 500 --price 250 --item "Cosmetics" --channel Online --priority H --month 6

You can also run python project5_ml/predict.py with no arguments to see sample predictions.

project6_api

Start the FastAPI app with Uvicorn:

uvicorn project6_api.api:app --reload

Then open:

• http://127.0.0.1:8000/ for the API root
• http://127.0.0.1:8000/docs for interactive API docs

project7_prefect

Run the Prefect flow:

python project7_prefect/pipeline.py

The flow fetches country data, retrains the model, and writes a run report under project7_prefect/reports/.

Notes

• Generated caches and local virtual environments are ignored via .gitignore.
• The ML and API projects expect the model files in project5_ml/models/.
• If you change any input CSVs, rerun the CSV pipeline before retraining the model.