Hello 👩‍💻!

Fancy seeing you here 😌 !

I am Maria Balos, a data scientist and user-centric designer based in Cambridge, UK. You can find me most of the time behind a screen or next to a coffee. Welcome to this small corner of my work!

Right now I am involved into:

• Joined the playground competition "Natural Language Processing with Disaster Tweets" on Kaggle.
• Preparing to join the Master in Deep Learning and Generative AI by DATAMECUM - October 2024 - April 2025
• Finishing the last 16/100 projects of the course 100 Days Of Code in Python by Angela Yu on Udemy.
• Working on my career development, please check my LinkedIn for more details.

Last acchivements:

• 28th of May: Completed the first part of the "Practical Deep Learning" course by fast.ai
• 17th of May: Completed the "Advanced Learning Algorithm Course" by Andrew Ng in Coursera
• 9th of May: Winner of the DATAMECUM Datathon 3rd promotion competition.

Last Medium post:

• Machine learning applied to the design industry: K-Means for image palette generation

Skills 🌟

Please check out the next sections to see these skills applied in projects.

• Exploratory Data Analysis: understanding the data, identifying missing values, approach duplicated values, handling ambiguous values,identifying outliers and anomalies, and correlation detection.

• Unsupervised Machine Learning

  • K-Means: Weever Watermark project
  • DBSCAN
  • PCA
  • Outliers and anomaly detection
  • SOM - Self-Organizing Maps

• Supervised Machine Learning

  • Generalised Linear Models
  • Support Vector Machines
  • K-Nearest Neighbors
  • Decision Stumps: Kaggle notebook
  • Decision Trees:
  • Random Forest: Datamecum Datathon
  • XGBoost: Datamecum Datathon
  • Ensemble models: Winner of the Datamecum Datathon capston project competition with an ensemble of the Random Forest and XGBoost predictions.

• Python libraries for data science

  • Data processing: Pandas, NumPy
  • ML & stats: Scikit-Learn, Statsmodels
  • Data visualisation: Matplotlib, Seaborn, Plotly

Projects 📜

Exploratory Data Analysis

• Space Mission Analysis is a data exploration and data visualisation project where I applied most of the data visualisation libraries.
• Mohs Hardness Exploratory Data Analysis: Decision Stump (one layer decision tree) for a Kaggle competition, this placed me in position 598/1632 at the end of the competition. A decision stump presentation has been created to introduce Datamecum students to decision stumps.
• Datamecum Dataton - Capstone project competition between the third promotion students of the Intensive Program in Data Science by DATAMECUM consisting of building a supervised model to predict a binary class. The exploratory data analisys consisted of:
  • checking for missing values.
  • handling duplicated values and ambiguous data.
  • exploring the relation between missing values and the target variable.
  • Self Organizing Maps and correlation matrix for correlation checks.

Machine Learning

Unsupervised

• Weever Watermark: Applying K-Means and arrays transformation to group the colours of a provided image and use the generated centroids to create a 10-colour palette into a GUI API created with Flask. Please have a look at the Weever Watermark DEMO or at the Machine learning applied to the design industry: K-Means for image palette generation article where I explain the project.

Supervised

• Datamecum Dataton - Capstone project competition between the third promotion students of the Intensive Program in Data Science by DATAMECUM consisting of building a supervised model to predict a binary class.

Web / App Development

• Typing Thunder: a speed-typing GUI app created to measure how fast the user type in one minute. Typing Thunder DEMO
• Morse Code: a command-line program where the dictionary loops, strings and functions in Python are applied. Morse Converter DEMO
• MochaMaps: a website that displays coffee shops and their facilities from a database by using: SQLite, SQLAlchemy, Jinja2, REST API, Flask API and Bootstrap-Flask. MockaMaps DEMO

Automation

• LinkedIn Toggler: Using Selenium for Python to automate recurrent LinkedIn tasks. Please have a look at the LinkedIn Toggler repository if you want to know more about this project.

Final Notes & Contact ☎️

Thank you for visiting my GitHub! Feel free to have a deeper look in my repositories to find more specific projects. Please share any feedback, suggestions, or tips that you believe could help me grow and improve!

I am always happy for a coffee, a chit-chat or a discussion of any possible collaboration. Please drop me an email at mariabalos16@gmail.com or send me a message through my LinkedIn if you fancy any of those.