Zain The AnalystAvailableCase Study
Natural Language Processing NLP
Enron Emails Dataset Analysis
A Python, SQL, and NLP project that explores email communication patterns from the Enron dataset.
Key Metrics Analyzed
DatasetEnron Emails
DatabaseSQLite
Tables Used3
Analysis TypeNLP + EDA
Overview & Objectives
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.
Project Goal
Analyze email communication patterns, message volume, important senders, recipients, and common topics from a real-world email dataset.
The Business Problem
The Enron email dataset contains large communication records stored across database tables. The challenge was to clean, query, and analyze the data to find meaningful communication patterns and topics.
Methodology
My Approach
- 1Connected to the SQLite email database.
- 2Cleaned and prepared message and recipient data.
- 3Used SQL and Pandas to extract communication patterns.
- 4Created visualizations and text-based insights using Python libraries.
Implementation
Roadmap Execution
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.
Key Features
SQLite querying
Email volume analysis
Top sender and recipient analysis
Recipient type analysis
Text preprocessing
Word cloud and topic exploration
Python visualization
Business Impact
Shows SQL and Python analysis skills
Demonstrates NLP basics on real email data
Finds communication patterns from complex records
Useful for compliance, communication, and text analytics use cases
Challenges Overcome
- Large email communication dataset
- Multiple related database tables
- Text preprocessing requirements
- Need to convert emails into clear communication insights
Outcomes
Final Outcomes & Learnings
- Produced insights about email volume, senders, recipients, recipient types, and common topics.
- The project demonstrates data cleaning, SQL querying, visualization, and NLP fundamentals.
Project Gallery & Screenshots
Technologies Used
PythonSQLitePandasMatplotlibNLTKWordCloudJupyter Notebook
Data Sources
Enron email SQLite databaseEmployee list tableMessage tableRecipient info table
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