Lucas Campos

Dynamic Data Science graduate with a mastery of Python, SQL, R, and machine learning techniques. Focused on leveraging advanced analytics to drive actionable insights and optimize business processes. Strong collaborator with exceptional problem-solving abilities and a passion for staying at the forefront of data science innovation. Ready to contribute effectively to high-impact projects in a challenging data-driven environment.

Big Data Strategy Project for Spotify (ISQS 5341): 

• Led a team of five students in developing a comprehensive big data strategy for Spotify, a leading music streaming platform.
• Conducted analyses using SWOT Analysis, Five Forces Model, and Value Chain Analysis to assess Spotify's business strategy and competitive landscape.
• Explored methods for capturing, measuring, and integrating data within Spotify's operations, emphasizing user interaction metrics and diverse dataset incorporation.
• Aligned Spotify's big data utilization with business objectives and addressed privacy and security concerns associated with data monetization.
• Provided recommendations for optimizing operations, enhancing efficiency, and leveraging big data analytics to maintain Spotify's competitive edge in the music streaming industry.

Multivariate Analysis Project: Global Inflation Trends Analysis (ISQS 6350):

• Lead a team in conducting multivariate analysis on the Global inflation dataset, focusing on economic and demographic variables across countries in 2022.
• Directed data cleaning and visualization processes to ensure dataset integrity and enhance interpretability.
• Utilized dimension reduction techniques, including Principal Component Analysis (PCA), Exploratory Factor Analysis (EFA), and Canonical Correlation Analysis (CCA), to uncover underlying patterns and relationships among variables.
• Implemented cluster analysis methods such as K-Means, Hierarchical, and Model-Based Clustering to group countries based on economic similarities and explore global economic conditions.
• Contributed to insights regarding the impact of inflation on cost of living and population size, identifying correlations and variations in trends.
• Enhanced practical skills in multivariate analysis and gained valuable insights into global economic trends and interdependencies among economic variables.

Machine Learning Analysis Project: Predicting Trailer Views for Movies (ISQS 6347):

• Led a team in conducting a comprehensive machine learning analysis using real-world movie data.
• Implemented various machine learning models including K-NN, Regression Tree, Random Forest, Linear Regression, Logistic Regression, ANN, SVM, and Ensemble methods.
• Collaborated with team members to preprocess the dataset, train models, and evaluate performance metrics such as RMSE.
• Analyzed and compared the effectiveness of different models in predicting trailer views.
• Demonstrated proficiency in data preprocessing, model selection, implementation, and evaluation.
• Presented findings and conclusions in a clear and concise manner, highlighting the best-performing model and its implications for predicting movie trailer views.