This project showcases the practical application of SQL skills in exploring, cleaning, and analyzing retail sales data to generate meaningful business insights.
The project involved setting up a retail sales database, performing data cleaning to handle missing or null values, and conducting exploratory data analysis (EDA) to understand the dataset. Using SQL queries, I answered a series of business-focused questions, such as identifying sales trends, calculating total and average sales, analyzing customer demographics, and determining top-performing products and customers.
This end-to-end process demonstrates how SQL can be used to transform raw data into actionable information for strategic decision-making.
This project investigates the relationship between lifestyle factors and prostate cancer risk using a synthetic dataset downloaded from Kaggle. Using exploratory analysis and logistic regression, I evaluated how age, BMI, smoking, alcohol consumption, diet type, physical activity, family history, sleep hours, and mental stress relate to high prostate cancer risk. Key findings show that smoking, low physical activity, higher BMI, older age, and family history are associated with increased odds of being high-risk, while healthy/mixed diets, lower mental stress, and moderate alcohol consumption are associated with reduced odds.
This project showcases the use of Python, specifically the Pandas and Matplotlib libraries, to perform end-to-end data analysis on raw sales data.
The primary goal was to simulate real-world tasks of a data analyst by transforming unstructured data into actionable business insights. The analysis involved importing the dataset into a Pandas DataFrame, performing thorough data cleaning including handling missing values, removing duplicates, fixing formatting inconsistencies, and ensuring proper data types` followed by exploratory analysis and visualization. Using descriptive statistics, aggregation techniques, and trend evaluation, I systematically answered key business questions related to payment preferences, product performance, revenue distribution, and overall sales behavior. This comprehensive approach not only demonstrated proficiency in Python data analysis but also highlighted how data-driven insights can support informed decision-making in a business environment.
The purpose of this project is to evaluate the performance of marketing efforts using key metrics such as ad spend, conversions, clicks, and return on investment (ROI).
The analysis leverages Power BI dashboards to provide a visual representation of performance trends and to identify areas where marketing resources can be optimized.
This project includes time-based insights, product-level comparisons, and detailed ROI calculations.
Below, you'll find a selection of dashboards created using Tableau.