Data Scientist Student
Current
During my training, I gained practical mastery of Python, Pandas, PostgreSQL, and industry best practices under the guidance of a senior data scientist mentor. I also developed and presented projects involving exploratory data analysis, data cleaning, machine learning, and statistical modeling. Below are some of the most interesting and exciting projects I worked on:1) Supervised Learning: Analysis of Financial Dataset for Fraud Detection - Designed an end-to-end machine learning model for fraud detection. - Cleaned and engineered data; built, tuned, and compared several models; tuned the best model to specifications. - Synthesized findings and suggestions into a standalone repository and delivered a business-oriented presentation.Technology: Python, Scikit-learn, Plotly, Statsmodels, Jupyter, Pandas, Numpy, Seaborn.2) Statistical Analysis of Heart Disease Factors - Conducted a statistical analysis comparing different factors associated with a positive diagnosis of heart disease.- Thoroughly cleaned and prepared a heart disease dataset for rigorous statistical analysis. - Employed advanced statistical techniques to explore the impact of heart disease factors, effectively visualizing the differences with meaningful effect sizes in a clear and engaging manner.Technology: Python, Statsmodels, Jupyter, Pandas, Numpy, Seaborn.
