📅 2023-01-13 — Session: Enhanced Matplotlib Visualizations and Feature Importance
🕒 14:30–16:00
🏷️ Labels: Matplotlib, Data Visualization, Feature Importance, Python, Pandas
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance data visualization techniques using Matplotlib and explore methods for determining feature importance in machine learning models.
Key Activities
- Explored methods to modify x-tick labels in Matplotlib, including setting, customizing, and rotating labels to prevent overlap.
- Implemented techniques for setting multi-index in Pandas DataFrames and renaming index axes.
- Examined methods for determining feature importance in classifiers, focusing on Permutation Importance and RandomForestClassifier.
- Generated scatter plots and dual bar charts using Matplotlib and Pandas, emphasizing feature importance and correlation visualization.
Achievements
- Successfully modified x-tick labels in Matplotlib, enhancing readability and presentation of data visualizations.
- Applied Pandas techniques for multi-indexing and index manipulation, improving data handling capabilities.
- Clarified multiple methods for assessing feature importance, providing practical coding examples for implementation.
Pending Tasks
- Further exploration of advanced visualization techniques and feature importance methods in different machine learning models.