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User Behavior Data Analysis (SQLite + Python)

This project performs exploratory data analysis (EDA) on user and event data stored in a SQLite database.

The analysis was originally developed as part of a data science technical assessment. The goal was to explore behavioral patterns, identify trends in user activity, and extract insights that could support business decisions.

Dataset

The dataset is stored in a SQLite database:

papcorns.sqlite

It contains tables such as:

  • users
  • user_events

These tables include information about user attributes and interaction events within the platform.

Project Workflow

The analysis follows a typical data science workflow:

  1. Database connection using SQLite
  2. Loading tables into Pandas DataFrames
  3. Data cleaning and preprocessing
  4. Exploratory Data Analysis (EDA)
  5. Visualization of behavioral patterns
  6. Insight extraction

Notebooks

The project contains two main notebooks:

papcorns_analysis_template.ipynb

Main notebook that includes:

  • database exploration
  • feature inspection
  • exploratory analysis
  • visualizations
  • behavioral insights

ChurnProbability.ipynb

Additional notebook exploring potential churn indicators and predictive patterns.

Technologies Used

  • Python
  • Pandas
  • SQLite3
  • Matplotlib
  • Seaborn
  • Jupyter Notebook

Running the Project

Clone the repository:

git clone https://github.com/coderfeye13/user-event-data-analysis.git
cd user-event-data-analysis

Open the notebook:

jupyter notebook papcorns_analysis_template.ipynb

Ensure that the database file papcorns.sqlite is located in the root directory of the project.

Author

Furkan Yilmaz
M.Sc. Computer Science
HAW Kiel – Germany

License

This project is licensed under the MIT License.