Cleaned and explored the UCI Online Retail dataset to reveal sales trends, identify top products, and highlight data quality issues, showcasing key marketing insights with R.

This project analyzes a telecom customer dataset to understand the factors driving customer churn and builds a logistic regression model to predict which customers are likely to leave. The report includes detailed exploratory data analysis (EDA), data cleaning, feature engineering, and model evaluation. Key findings highlight the impact of contract type, tenure, payment method, and monthly charges on churn risk. The model’s insights are presented with clear visualizations and actionable business recommendations to help stakeholders improve customer retention strategies. The final cleaned dataset is also exported for use in interactive Tableau dashboards. This comprehensive project demonstrates practical skills in R programming, data analysis, statistical modeling, and business storytelling—ideal for junior data analyst portfolios.

This report explores Cyclistic bike-share data for Q1 2019 and Q1 2020 to uncover usage patterns between casual riders and annual members. The analysis includes data cleaning, descriptive statistics, and visualizations to support data-driven recommendations for increasing annual memberships.

This project demonstrates data cleaning techniques applied to the Nashville Housing dataset (2013–2016). Using R and packages like tidyverse, lubridate, and janitor, I standardized column names, handled missing values, formatted dates, cleaned text fields, and identified outliers to prepare the dataset for analysis and modeling. This cleaned dataset can now support exploratory data analysis, predictive modeling, and insights into regional housing trends. The project highlights essential data preparation skills required for real-world analytics work.