📅 2023-04-19 — Session: Analyzed World Bank project data with Random Forest
🕒 20:05–20:25
🏷️ Labels: Data Visualization, Random Forest, World Bank, Python, Matplotlib
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to analyze World Bank project data to predict job creation relevance using a Random Forest Classifier and to visualize data distributions with histograms.
Key Activities
- Utilized Pandas
groupby()to create histograms for grouped data. - Iterated through DataFrame columns to plot histograms using Matplotlib.
- Created subplots for better data visualization.
- Addressed and resolved a KeyError in a DataFrame loop by filtering non-string values and renaming loop variables.
- Updated histogram plotting code to fix variable reference issues.
- Conducted a comprehensive analysis of World Bank project scores using a Random Forest Classifier, including data loading, diagnostics, preprocessing, model training, and score analysis.
Achievements
- Successfully visualized data distributions with histograms and subplots.
- Resolved KeyErrors in data processing loops.
- Completed an insightful analysis of World Bank project data, enhancing understanding of job creation relevance.
Pending Tasks
- Further refine the Random Forest model parameters for improved accuracy.
- Explore additional visualization techniques to better present findings.