🎨 IMDB Comedy Movies – Data Analysis (Interview Task)

Hi! This project is based on a real interview exercise I received for a Security Analyst role at Arkose Labs. It’s a short but meaningful data challenge that asked me to analyze two IMDb datasets from 2018 and answer some clear questions about comedy films.

The goal wasn't just to extract numbers — it was about thinking critically, approaching real-world data, and drawing clear insights.

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✅ What I Did

• Checked how many comedy movies were released in 2018
• Found how many of them had a rating of 8.0 or higher
• Identified the highest-rated comedy movie
• Analyzed whether users tend to prefer short or long movies (based on ratings)

The analysis was done using simple, readable Python – no overengineering.

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📂 Files in This Repo

📁 Python Analysis Scripts

• analysis_step1_comedy_films.py – Filters 2018 comedy movies
• analysis_step2_high_rated.py – Finds comedy movies rated 8.0+
• analysis_step3_top_rated_titles.py – Identifies the top-rated title
• analysis_step4_length_vs_rating.py – Compares movie length vs. rating

📄 Notes & Explanations

• notes_step1_comedy.md
• notes_step2_high_rated.md
• notes_step3_top_rated.md
• notes_step4_length_vs_rating.md

🗂️ Data Files

• title_basics_2018.csv – IMDb base metadata
• title_ratings.csv – IMDb ratings

📄 Assignment Brief

• Arkose Labs SOC - Security Analyst Interview Exercise.docx

📘 This README

• README.md

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🤛️ Why This Is Here

I decided to share this publicly because it shows:

• How I approach small analytical tasks
• How I work with real data
• And how I communicate insights clearly and simply

I don’t come from a traditional data background — but as a cybersecurity analyst, I constantly investigate, connect dots, and find meaning in raw information.

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🧑‍💻 About Me

Eliran Loai Deeb GitHub: tinkerlev LinkedIn: Loai Deeb