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Violence Against Women Analysis

international-day-for-the-elimination-of-violence-against-women-free-vector

This project involves data preprocessing, model implementation, and visualization to analyze patterns of violence against women.

Overview

This project utilizes machine learning techniques to analyze a dataset on violence against women. Key steps include data preprocessing, model implementation, and visualization to derive insights and predictions.

Key Steps

  1. Data Preprocessing:
  • Handled missing values
  • One-hot encoded categorical variables
  1. Model Implementation:
  • Split data into training and testing sets
  • Implemented Linear Regression, Lasso Regression, and Random Forest Regression
  1. Model Evaluation:

Random Forest Regression:

  • Mean Absolute Error (MAE): 2.92
  • R² Score: 0.92

Data Visualization:

  • Created visualizations to compare model predictions and identify key trends

Results

  • The Random Forest model achieved the best performance with an MAE of 2.92 and an R² score of 0.92.
  • Visualizations provided insights into the patterns and trends in the data, aiding in better understanding of the factors associated with violence against women.

Dependencies

  • numpy
  • pandas
  • matplotlib
  • seaborn
  • scikit-learn