Portfolio - Andreea- Alina

Andreea- Alina

Jr Data Scientist | ArcGIS Analyst

1

Python Project

About Me

Hello! I am Andreea, a Data Scientist and GIS Analyst 🌍📈⚛.
For the past four years, I have dedicated myself to studying this fascinating world of data, and I must say it is an incredible journey! 🌟
My experience encompasses a variety of tools and techniques, including Python 🐍, Data visualization 📊, Machine Learning 🤖, and Data Analysis 🔍. Additionally, during my participation in the Data Science Bootcamp(Spain), I acquired advanced skills in areas such as Data Analysis 📈, Machine Learning 🧠, and Recommendation Systems 📚.
I am committed to continuing to grow professionally and exploring new opportunities in the world of data. My passion for this field drives me to constantly seek challenges that allow me to expand my knowledge and contribute significantly.
I am excited about what the future holds and eager to keep learning and growing in this exciting industry. 🎯

Key Skills

Python

ArcGIS Pro

ArcGIS Online

Machine Learning

Data Visualization

My Projects

Smart City with GIS

Domain/Function: Spatial Science

Optimizing Crop Rotation for Small-scale Farmers in Rwanda

Domain/Function: Genetic Algorithms and Recommender Systems

Awards & Certificate

Get Job Ready: Power BI Data Analytics for All Levels 3.0

SQL Beginner to Advanced For Data Professionals

Data Scientist

Spatial Data Science

Artificial Intelligence Fundamentals

Let's Connect

Feel free to get in touch with me. I am always open to discussing new projects, creative ideas or opportunities to be part of your visions.

https://www.linkedin.com/in/andreea-alina/

GitHub

https://github.com/andreeaacotos

Smart City with GIS

Domain/Function: Spatial Science

The SmartTech project is based on the use of ArcGIS technology to create a smart city model that benefits both municipalities and citizens. The project used data from the city of Santiago de Compostela as an example, but it can be applied to any city with a population exceeding 50,000 inhabitants.

View Project
on GitHUB

View Project
on YouTube

Other Tools

ArcGIS Pro

The description of the project.

Future actions.

Project Details

The website of the project presents all the details about the users and its benefits. Citizen applications include intuitive tools for sustainability, like a viewer with heat maps showing CO concentrations related to traffic flow. A mobility app provides traffic info, showing congested (red) and free-flowing (green) zones, public park availability, and bus schedules.
A tourism app lets tourists find accommodations by cost and view detailed info, including availability. They can also map key tourist sites based on their available time.
A survey allows citizens to participate in municipal tasks by submitting complaints with images, notifying relevant municipal employees. A dashboard shows public fund allocations on a map. A general viewer offers tools like waste container locations and fill percentages, and a tool for finding the best parks for exercise based on air quality.
Employees have a personal page with applications for daily tasks, including citizen participation feedback.

Python Project

Optimizing Crop Rotation for Small-scale Farmers in Rwanda

Domain/Function: Genetic Algorithms and Recommender Systems

View Project
on GitHUB

View Project
on https://www.omdena.com/chapter-challenges/optimizing-crop-rotation-for-small-scale-farmers-in-rwanda

Crop recommendation-made with RandomForestClassifier that requires user input: environmental and soil features.

Genetic Algorithm for Crop Rotation - The algorithm optimizes rotations for 2-5 years, balancing crop yields, and soil health.

Project Details

This project focuses on developing an optimized crop rotation system for small-scale farmers in Rwanda, using a combination of data-driven analysis and machine learning models to make informed crop recommendations and finally to create a crop rotation plan. This approach is intended to improve soil health, increase crop yield, and support sustainable agricultural practices.

Objectives
- Recommend crop rotations tailored to soil and climate conditions.
- Enhance long-term soil quality while supporting higher yields and profitability.
- Use genetic algorithms to design robust, sustainable crop rotation strategies.
- App development for practical use by stakeholders.