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.
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.
The analysis follows a typical data science workflow:
- Database connection using SQLite
- Loading tables into Pandas DataFrames
- Data cleaning and preprocessing
- Exploratory Data Analysis (EDA)
- Visualization of behavioral patterns
- Insight extraction
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.
- Python
- Pandas
- SQLite3
- Matplotlib
- Seaborn
- Jupyter Notebook
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.
Furkan Yilmaz
M.Sc. Computer Science
HAW Kiel – Germany
This project is licensed under the MIT License.