Analyzed World Bank project data with Random Forest

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Analyzed World Bank project data with Random Forest

  • Day: 2023-04-19
  • Time: 20:05 to 20:25
  • Project: Dev
  • Workspace: WP 1: Strategic / Growth & Development
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Data Visualization, Random Forest, World Bank, Python, Matplotlib

Description

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.

Evidence

  • source_file=2023-04-19.sessions.jsonl, line_number=2, event_count=0, session_id=4a5f7a7965e54052c5b53146a49db8ba1eb810666fa97e9fba3185dec97f69f7
  • event_ids: []

Graph View

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