https://github.com/dms-codes/pagu-realisasi-dana-desa-2023

Indonesia Village Fund Analysis - Analyzing Budget vs. Realization
https://github.com/dms-codes/pagu-realisasi-dana-desa-2023

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Indonesia Village Fund Analysis - Analyzing Budget vs. Realization

• Host: GitHub
• URL: https://github.com/dms-codes/pagu-realisasi-dana-desa-2023
• Owner: dms-codes
• Created: 2024-11-30T13:37:24.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-30T13:38:39.000Z (about 2 months ago)
• Last Synced: 2024-11-30T14:34:58.895Z (about 2 months ago)
• Language: Python
• Homepage: https://github.com/dms-codes/pagu-realisasi-dana-desa-2023/
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
##  Indonesia Village Fund Analysis - Analyzing Budget vs. Realization

This is a Python script for analyzing the budget (pagu) vs. realization (realisasi) of village funds in Indonesia. It utilizes pandas for data manipulation and seaborn for visualizations.

**Functionality:**

1. **Data Loading:** Reads data from CSV files containing pagu and realisasi information (assuming they reside in a folder named "data").

2. **Data Cleaning:** Merges the dataframes based on common columns and drops rows with missing values.

3. **Feature Engineering:** Calculates the utilization rate (realisasi divided by pagu).

4. **Visualization:**

    * Creates a scatter plot to visualize the relationship between pagu and utilization.

    * Generates a normal distribution plot to analyze the distribution of the utilization rate.

**How to Use:**

1. Ensure you have pandas and seaborn libraries installed (`pip install pandas seaborn`).

2. Place your pagu and realisasi data in a folder named "data" within the same directory as this script.

3. Run the script (`python village_fund_analysis.py`).

**Expected Output:**

* Two visualizations will be displayed:

    * A scatter plot showing pagu on the x-axis and utilization rate on the y-axis.

    * A normal distribution plot of the utilization rate.

**Further Enhancements:**

* Implement additional data cleaning techniques (e.g., handling outliers).

* Explore other visualizations like box plots or heatmaps for deeper insights.

* Integrate machine learning models for more advanced analysis (e.g., predicting future utilization).

**Disclaimer:**

This script provides a basic framework for analyzing village fund data. It may require adjustments based on the specific data format and desired analysis goals.