As a freelancer passionate about data science, my journey has always been driven by curiosity and the desire to delve into the vast sea of data. With each project, I strive not only to apply my technical knowledge but also to deeply dive into the stories that data can tell.
Passion for Data Science
My passion for data science is fueled by the fascination of how data, when properly interpreted, can reveal patterns, predict trends, and unravel mysteries. This field, balancing on the frontier between technical skill and creativity, allows me not just to solve complex problems but also to transform raw data into valuable insights and engaging stories.
Collaborating on Projects
A vital part of my freelance experience is collaborating on other people’s projects. For me, each project is a new adventure and an opportunity to learn. Working together with other professionals and clients not only expands my horizons but also allows me to make a significant contribution to the success of the projects. I firmly believe that through collaboration, we can achieve results that are much greater than the sum of individual parts.
Featured Projects
Below, I share some of the projects I had the pleasure to collaborate on, demonstrating my versatility and commitment to excellence in data science:
This project involves analyzing and enhancing an initially weak convolutional neural network, provided in the FinalProj23.ipynb file. The goal is to improve the network's performance within 15 training epochs, using a GPU.
- Baseline Performance Analysis: The initial code is run to establish a baseline for performance.
- Implementation and Improvements: Modifications are made to the network architecture and training methods. The changes aim to exceed a baseline accuracy of 44% on the CIFAR-100 dataset, which contains 32x32 colored images of 100 different classes.
- Validation and Testing: 10% of the dataset is reserved for validation. The project focuses on improving accuracy for both the best and worst-performing classes.
- Snapshot Ensemble: Implementation of a snapshot ensemble with at least 5 snapshots to enhance classification.
- Architecture Optimizations: Addition or removal of layers to optimize performance for the 100 classes.
- Detailed Analysis: Detailed discussion on the strategies attempted, what worked and what didn’t, along with justification for each change made.
- Enhanced Performance: Report on the achieved accuracy, highlighting the accuracy of the best and worst class.
- Credits and References: All external sources used are duly credited.
This project not only challenges technical skills in data science and machine learning but also emphasizes the importance of experimentation and critical analysis in the process of optimizing machine learning models.
Link Machine Learning Optimization
This project focuses on analyzing the global and UK-specific trends in renewable energy, primarily wind and solar power, using the UNSD Energy Statistics Database and the UNSD Annual Questionnaire on Energy Statistics. The goal is to inform the development of blockchain-based technical assistance projects under the Blockchain Climate Institute (BCI).
- Global Trends Analysis: Utilize statistical analysis and data visualization techniques to identify trends in wind and solar energy production worldwide.
- Country-Specific Energy Market Analysis: Conduct in-depth analysis of the UK's energy market, focusing on shifts in energy categories and flow patterns.
- Future Energy Production Forecasting: Employ time series analysis and predictive modeling to project future trends in wind and solar energy production up to 2030.
- Energy Clustering Analysis: Implement machine learning techniques, like K-means clustering, to group countries based on their energy mix.
- Data Quality Assessment: Perform a comprehensive assessment of the dataset to ensure accuracy and reliability of the analysis.
- Statistical Analysis and Data Visualization: To observe trends and make the data easily understandable.
- Machine Learning Clustering: K-means clustering for grouping countries based on their energy production metrics.
- Predictive Modeling: To forecast future energy production trends, using models best suited for time series data.
- Detailed Analysis: A thorough discussion of the trends observed globally and in the UK, including leading countries in renewable energy adoption.
- Future Trends Prediction: Insights into projected energy production patterns up to 2030.
- Clustering Insights: Analysis of how countries cluster based on their energy mix.
- Data Quality Insights: Assessment of the dataset's quality and potential biases.
- Stakeholder-Specific Insights: Identification of trends and opportunities relevant to energy companies and real estate managers.
- Briefing Notes: Drafting of comprehensive briefing notes for the DRS1 and DG meetings, focusing on sustainable finance project concepts.
- Interactive Dashboard: Development of an interactive dashboard to visually represent energy trends and predictions.
- Strategic Recommendations: Providing actionable recommendations for blockchain-based project development.
- All external sources and methodologies used in this analysis are duly credited.
- Data sourced from the UNSD Energy Statistics Database and the Annual Questionnaire on Energy Statistics.
This project blends technical expertise in data analysis and machine learning with a strategic focus on renewable energy trends, aiming to support the development of sustainable and impactful blockchain-based projects in the energy sector.
Link Renewable Energy Analytics Project
Each of these projects represents a milestone in my journey, where I was able to apply my skills, learn new techniques, and above all, contribute to transforming data into well-founded decisions and strategies.