Hello World! It's Caroline :)

About Me

I'm an MBCS Python programmer with a strong focus on quality assurance, data analysis, and security-minded problem-solving. I enjoy finding hidden issues, fine-tuning secure workflows, and bridging communication gaps. My background spans cryptography, GUI development, NLP, data visualisation, and hands-on troubleshooting across real-world systems, supported by strong administrative and communication skills.

I love clean code, clear documentation, and that satisfying moment when I solve the problem, no matter what time it is. If you'd like to work with me, feel free to get in touch. :)

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Project Showcase

Here are some of the projects I've enjoyed building, ranging from technical coursework to personal development. Each one reflects my approach to analysis, clarity, structure, and a detective-mindset.

• AES-256 Cipher Generator

  Python // Cryptography // PBKDF2 // AES-CBC

  A PEP8-compliant encryption tool developed as my Open University capstone project. It uses AES-256 in CBC mode with PBKDF2 key derivation, full input validation, and structured error handling.

  • Implementation || Implements AES-256 encryption in CBC mode with PBKDF2 key derivation.
  • Detective-Mindset || Integrated robust input validation and custom exception handling (EmptyStringError, OneCharBirthMonthError) to prevent system crashes.
  • Defensive Design || One million hash iterations to make the derived key computationally expensive to hack!
  • Professional Standards || Fully documented and PEP8-compliant, adhering to ISO/IEC 18033-3 and NIST FIP197 standards.
  • Security Focus || Demonstrates security-focused programming and defensive coding.

  except WordTooLongError: # catch trolling; words are <45-chars print{f'The longest English word is '+ f'pneumonoultramicroscopicsilicovolcanoconiosis.')

  >> View on GitHub

• Supermarket Sales Analysis

  Python // Pandas // Matplotlib // Seaborn // Data Cleaning // Visualisation

  An end-to-end data analysis project developed as my Edge Hill University capstone project. It explores customer behaviour, sales trends, and product performance. It includes data wrangling, feature extraction, and a wide range of visual charts.

  • Data Integrity || Cleaned and normalized a 3-month real-world retail dataset using Pandas.
  • Visualisation || Built clear, readable charts using Matplotlib and Seaborn to communicate complex performance data.
  • Analytical Insight || Located anomalous gems in the homogenous swill to identify true performance outliers.
  • Operational Value || Identified Saturday at 7 pm as the peak transaction window, providing actionable insights for staff redeployment.
  • Critical Thinking || Evaluated dataset limitations, noting the downward trend and the risks of short-term data modeling.

  day_list = [] # var to hold days for i in range(df.shape[0]): # iterate over all rows # extract day from mm/dd/yyyy day = int(str(df['Date'][i]).split('/')[1]) day_list.append(str(day)) # append to list df['Day'] = day_list # assign new column

  >> View on GitHub

• Sentiment Analysis of Game Reviews

  Python // NLTK // TextBlob // NER // NLP

  A natural language processing (NLP) project analysing sentiment in video game reviews, with additional named entity recognition (NER) to extract key entities.

  • Sentence-level sentiment scoring and aggregation.
  • Custom classification logic for positive/negative reviews.
  • Named Entity Recognition using NLTK.
  • Demonstrates text processing, algorithm design, and data interpretation.

  for sentence in blob.sentences: review_polarity += sentence.sentiment.polarity if review_polarity > 0.27: # avg dataset polarity sentiment_label_for_current_review = 1 # positive else: sentiment_label_for_current_review = 0 # negative

  >> View on GitHub

• GUI Games

  Python // Tkinter // Event-Driven Programming

  Two small but polished GUI games; Tic-Tac-Toe and Rock-Paper-Scissors. They demonstrate state management, event handling, and UI design.

  

  Tic-Tac-Toe feature highlights:

  • Dynamic 3x3 grid.
  • Win-condition algorithm.
  • Clean, readable game logic.

  Rock-Paper-Scissors feature highlights:

  • Event-driven UI.
  • Randomised computer choices.
  • Score tracking and visual feedback.

  # player colour in tic-tac-toe style.configure('played.TButton', foreground='blue') # computer colour style.configure('ai.TButton', foreground='red')

  >> View Tic-Tac-Toe on GitHub

  >> View Rock-Paper-Scissors on GitHub

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Skills

Here's a quick overview of some tools and techniques I've worked with:

• Programming || Python
• Data || Pandas, Matplotlib, Seaborn
• NLP || NLTK // TextBlob
• GUI Development || Tkinter
• Security || AES-256 // PBKDF2 // secure coding practices
• Web || HTML // CSS
• Tools || Git (GitHub)
• QA || Test case design // defect isolation // data validation // UAT
• Soft Skills || Clear documentation // problem-solving // communication

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Contact me

Email || caroline.campbell@bcs.org

GitHub || github.com/tehcara

LinkedIn || linkedin.com/in/care-campbell

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