📊 Data Visualization and Analytics

Project Summary:

In this comprehensive project, I explored the entire data science pipeline—from raw data to actionable insights—using R as the primary tool. The project was designed to build a strong foundation in data handling, visualization, and predictive modeling, gradually moving into more complex, real-world datasets and analytical challenges.

🔹 Data Acquisition & Processing

• Imported and cleaned data from CSV
• Handled missing values, outliers, and inconsistent data formats
• Applied data transformation techniques such as normalization, encoding, and scaling

🔹 Exploratory Data Analysis (EDA)

• Used summary statistics and visual tools (e.g., boxplots, histograms, scatter plots)
• Identified meaningful correlations and trends between features

🔹 Data Visualization

• Developed dynamic and static visualizations using ggplot2 and plotly
• Created dashboards and storytelling visuals with RMarkdown and Shiny

🔹 Predictive Modeling

• Applied regression models (Linear, Ridge, Lasso)
• Built classification models (Logistic Regression, Decision Trees, KNN)
• Evaluated models with accuracy, precision, recall, F1 score, ROC-AUC

🔹 Capstone Challenge with Real-World Data

• Worked on open datasets in public health, e-commerce, and finance
• Answered domain-specific questions through structured analysis
• Delivered findings via visual reports and presentations

✅ Outcome

This project solidified my understanding of data analytics workflows and enhanced my practical skills in turning raw data into valuable insights. It taught me how to ask the right questions, clean and interpret data meaningfully, and present outcomes clearly for both technical and non-technical audiences.