Web Server Log Analysis - Python
📊 Web Server Log Analysis - Python Take-Home Assessment
Submitted by: Shubham Bama📊 Web Server Log Analysis - Python Take-Home Assessment
Submitted by: Shubham Bamane
Assessment Title: Web Server Log Analysis
Organization: MapUp AI (Take-Home Assignment)
In this video, I walk through my end-to-end solution for analyzing the Calgary HTTP web server log dataset using Python and Pandas.
🔍 Topics covered in this project:
Data download and decompression using urllib and gzip
Parsing Apache HTTP logs with regex
Timestamp formatting and datetime filtering
Data cleaning and validation
Exploratory analysis using Pandas
Answering 10 real-world server log analysis questions:
1. Total log records
2. Unique hosts
3. Unique filenames per day
4. Count of 404 errors
5. Top 15 filenames causing 404s
6. Top 15 file extensions with 404s
7. Daily bandwidth in July 1995
8. Hourly request distribution
9. Top 10 requested files
10. HTTP status code distribution
📁 Files included in submission:
`analysis.ipynb`: Jupyter notebook with code and output
`analysis.html`: Exported HTML version of notebook
`transcript.txt`: Auto-generated video transcript
`README.md`: (optional notes and assumptions)
🎥 Recorded and explained by me as part of the take-home evaluation.
🔐 This video is set to *Unlisted* and only accessible via this link.
Thank you for reviewing my submission!
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Assessment Title: Web Server Log Analysis
Organization: MapUp AI (Take-Home Assignment)
In this video, I walk through my end-to-end solution for analyzing the Calgary HTTP web server log dataset using Python and Pandas.
🔍 Topics covered in this project:
Data download and decompression using urllib and gzip
Parsing Apache HTTP logs with regex
Timestamp formatting and datetime filtering
Data cleaning and validation
Exploratory analysis using Pandas
Answering 10 real-world server log analysis questions:
1. Total log records
2. Unique hosts
3. Unique filenames per day
4. Count of 404 errors
5. Top 15 filenames causing 404s
6. Top 15 file extensions with 404s
7. Daily bandwidth in July 1995
8. Hourly request distribution
9. Top 10 requested files
10. HTTP status code distribution
📁 Files included in submission:
`analysis.ipynb`: Jupyter notebook with code and output
`analysis.html`: Exported HTML version of notebook
`transcript.txt`: Auto-generated video transcript
`README.md`: (optional notes and assumptions)
🎥 Recorded and explained by me as part of the take-home evaluation.
🔐 This video is set to *Unlisted* and only accessible via this link.
Thank you for reviewing my submission!
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