"The art is not in making predictions, but in figuring out what questions to ask to make predictions." - Nate Silver

Projects

New York City Food Inspection

Tools: ER Studio, Alteryx, Tableau, PowerBI, Data Profiling, Data Modelling, Data Analysis, Data Visualization

Description:

The project focused on enhancing NYC food inspection data through collection, transformation, and cleaning with SQL, Python, and Alteryx.
A dimensional model was created using ER Studio, incorporating a star schema for improved query efficiency.
Key trends were identified via exploratory data analysis, with insights visualized in Tableau and Power BI dashboards.

Tools: SQL, MySQL, Database Management, Database Implementation, Tableau

Description:

The project involved creating a relational database to organize vast datasets, including book details, customer information, and transactions.
Data normalization techniques were employed to maintain data integrity and reduce redundancy.
Furthermore, complex SQL queries were crafted to analyze customer behaviors and sales patterns, with these insights visually showcased through Tableau dashboards.

Utilized statistical and data science techniques for predictive modeling and analysis:
Enhanced housing market predictions through Linear Regression on the Boston Housing dataset, optimizing features for accuracy.
Improved loan approval processes using logistic regression, one-hot encoding, and careful feature selection for better predictive performance.
Applied Python and statistical libraries for regression analysis in complex financial scenarios, offering data-driven insights.