01 / The brief
The question
Analyze email communication patterns, message volume, important senders, recipients, and common topics from a real-world email dataset.
This GitHub project analyzes the Enron email dataset to understand communication patterns, email volume, top senders, top recipients, recipient types, and common discussion topics. The project is relevant for the portfolio because it combines database querying, data cleaning, exploratory analysis, visualization, and basic NLP techniques on a real-world email dataset.
Results & observations
- Produced insights about email volume, senders, recipients, recipient types, and common topics.
- The project demonstrates data cleaning, SQL querying, visualization, and NLP fundamentals.
Challenges
- Large email communication dataset
- Multiple related database tables
- Text preprocessing requirements
- Need to convert emails into clear communication insights
Approach
- Connected to the SQLite email database.
- Cleaned and prepared message and recipient data.
- Used SQL and Pandas to extract communication patterns.
- Created visualizations and text-based insights using Python libraries.
Implementation details
Database Connection
Connected to the Enron SQLite database.
Data Extraction
Used SQL queries to extract employees, messages, and recipients.
Data Cleaning
Prepared email records for analysis.
Exploratory Analysis
Analyzed communication volume, senders, recipients, and recipient types.
Text Analysis
Used NLP tools to explore common words and discussion topics.
Explore the report
Select a screenshot for a larger view.