📥 Data Sourcing and Cleaning

This project demonstrates real-world data sourcing and cleaning techniques using structured and unstructured data. It includes profiling, validation, transformation, and storage across different formats such as CSV, TXT, and SQL.

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📁 Contents

✅ Final Project

• Sourced and cleaned raw text into structured CSV
• Analyzed and visualized using charts and summaries
• Stored cleaned data in .db and .csv formats

✅ Assignment 15

• Performed cleaning and transformation on books.csv
• Included outlier detection, column standardization, and data checks

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🧰 Tools Used

• Python + Jupyter Notebooks
• pandas, matplotlib
• SQL for data storage and retrieval

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🚀 How to Run

1. Clone the repo:

   git clone https://github.com/arun-data-analyst/Data-Sourcing-and-Cleaning.git
   cd Data-Sourcing-and-Cleaning

2. Install the Python packages:

   pip install -r requirements.txt

3. Open the notebooks in Jupyter Lab or any Python IDE

🧠 Author

Arun Acharya
Data Analyst in training | Willis College