This project focuses on understanding and uncovering patterns in the dataset through detailed exploration. I used various techniques to summarize the main characteristics of the data and gain insights before moving to any modeling phase.
- Analyzed distributions, correlations, and relationships between variables.
- Used visualizations to uncover patterns, trends, and outliers in the data.
- Identified features that may be useful for prediction or further analysis.
- Created summary statistics and grouped insights to understand segment behavior.
EDA helps in asking the right questions before building models. This project shows how I approach data with curiosity and structure—focusing on what the data is trying to say, not just what we want to ask of it.
- Python (Pandas, NumPy)
- Matplotlib, Seaborn, Plotly for visualizations
- Jupyter Notebook for documenting the analysis