Equalize360 - Gender Equality Index Analysis

Dataset Overview

This repository contains the analysis of the Gender Equality Index dataset sourced from the European Union Open Data Portal. The dataset provides insights into various aspects of gender equality across different countries and regions. Each entry in the dataset includes information on participation rates, economic opportunities, access to resources, and other indicators related to gender equality.

How to Run this code

Upload the dataset given in the Colab contents and copy its path abd paste in the fisrt code cell of the notebook.

Analysis Results

Algorithms Used:

• Linear Regression:

  • Training R^2 Score: 0.9995989036693392
  • Testing R^2 Score: 0.993058190670885
  • Mean Squared Error: 0.3897634503153191

• Decision Tree:

  • Training R^2 Score: 1.0
  • Testing R^2 Score: 0.5695720153169044
  • Mean Squared Error: 24.167344343314145

• Random Forest:

  • Training R^2 Score: 0.9734614728103018
  • Testing R^2 Score: 0.8151747524571897
  • Mean Squared Error: 10.377427954629916

• Support Vector Machine:

  • Training R^2 Score: 0.6282085345302745
  • Testing R^2 Score: 0.48880298970664693
  • Mean Squared Error: 28.702302393577067

• K-Nearest Neighbors:

  • Training R^2 Score: 0.9221306141555102
  • Testing R^2 Score: 0.9028481535504604
  • Mean Squared Error: 5.4548082612784965

Sustainable Development Goals (SDGs) Addressed

This analysis aims to contribute to Sustainable Development Goal 5 (Gender Equality) by providing insights into gender disparities and facilitating evidence-based decision-making. By leveraging machine learning algorithms, we strive to identify key areas for intervention and policy formulation to advance gender equality worldwide.

Conclusion

Through the analysis of the Gender Equality Index dataset, we have gained valuable insights into the current state of gender equality across different countries. While significant progress has been made, there are still areas that require attention and targeted interventions. By employing advanced analytics techniques, we can drive positive change and work towards achieving gender equality and empowerment for all.

Strategies Used

• Data Preprocessing: Cleaning, normalization, and feature selection to prepare the dataset for analysis.
• Algorithm Selection: Experimentation with different machine learning algorithms to identify the most suitable models for predicting gender equality metrics.
• Evaluation Metrics: Utilization of R^2 score and mean squared error to assess model performance and accuracy.
• Interpretation: Analysis of algorithm outputs to identify key factors influencing gender equality and inform policy recommendations.

Contributors

• Nimra Waqar
• Sofia

Data Source

The Gender Equality Index dataset used in this analysis is sourced from the European Union Open Data Portal.

About

Equalize360: Harnessing ML & data mining to tackle gender inequality. Analyzing participation, economic opportunities & resource access to drive change.