I built Customer_analysis as a customer behavior data analyst portfolio project on my GitHub. It is intended to give me practical experience with an industry-style, end-to-end analytics workflow, converting raw customer shopping data into meaningful insights. I also list it among my repositories on github profile.
My Customer_analysis project is a Jupyter Notebook project; started 2026-01; last updated 2026-01.
- I use Python for data preparation, modeling, and exploratory data analysis: I clean, transform, and explore the dataset to make it analysis-ready.
git clone https://github.com/tejasnaikj/Customer_analysis.git
cd Customer_analysis
# Open Customer_Shopping_Behavior_Analysis.ipynb for data import,
# initial exploration, cleaning, and connecting Python to SQL.
# Create a database in MySQL, PostgreSQL, or MS SQL Server,
# then use the notebook to insert the dataset.
# Open customer_behavior_sql_queries.sql to work through business questions.
# Open customer_behavior_dashboard.pbix to explore the Power BI dashboard.
The README presents the project as simulating real-world data analyst tasks in business environments. The available project description does not report customer behavior findings, dataset dimensions or source, database schema or connection details, specific SQL questions, or dashboard measures.
- I use structured SQL queries to answer business questions about customer behavior, loyalty, and purchase trends.
- I use Power BI to create an interactive dashboard highlighting customer metrics and trends.
- I developed the project to strengthen my skills in Python data cleaning and preparation, EDA, SQL querying and business insights, Power BI dashboard creation, and understanding customer trends and purchase patterns.