Project with @DimitrisPapachrysanthou

Exploratory Data Analysis (EDA) Projects

Learning Projects - Part of Big Blue Data Academy Data Science Program
Hands-on exercises to learn and practice Exploratory Data Analysis techniques

Two comprehensive EDA projects analyzing real-world datasets, completed as part of my Data Science training.

🎯 Learning Objectives

• Apply EDA techniques to real datasets
• Practice data cleaning and preprocessing
• Create meaningful visualizations
• Derive actionable insights from data
• Use Python data science libraries (Pandas, NumPy, Matplotlib, Seaborn, Plotly)

📊 Projects

1. Netflix Users Analysis

• Dataset: 25,000 Netflix user records
• Analysis: Watch time patterns, subscription preferences, genre popularity by age group
• Key Insight: Gen Z and Millennials drive 75% of platform engagement
• Skills practiced: Data grouping, aggregation, categorical analysis, pie/bar charts

2. Superstore Sales Analysis

• Dataset: 9,994 sales records (2014-2017)
• Analysis: Product performance, customer segmentation (RFM), sales trends, Pareto analysis
• Key Insight: Top 6 customer segments generate 81% of revenue
• Skills practiced: RFM analysis, time series, Pareto charts, customer segmentation

🛠️ Technologies Used

• Python 3.9+
• Pandas, NumPy (data manipulation)
• Matplotlib, Seaborn, Plotly (visualization)

📈 Key Findings Summary

Project	Top Performer	Key Business Insight
Netflix	Premium Plan	Drama most popular genre across all ages
Superstore	Technology Category	$836K in sales, $145K profit

🔗 Data Sources

• Netflix Users Database
• Superstore Dataset