Violence Against Women Analysis

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

2. Model Implementation:

• Split data into training and testing sets
• Implemented Linear Regression, Lasso Regression, and Random Forest Regression

3. 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