This project aims to predict the violent crime rate in different communities based on various socioeconomic, demographic, and law enforcement-related features. By leveraging advanced regression techniques and machine learning models, this project seeks to uncover patterns that influence crime rates and develop predictive models to assist policymakers, law enforcement agencies, and researchers in understanding crime trends.
The project utilizes the Communities and Crime dataset, available from the UCI Machine Learning Repository: 🔗 Communities and Crime Dataset
This dataset includes a wide range of features related to community characteristics, such as:
⊳ Socioeconomic indicators (e.g., income levels, unemployment rates)
⊳ Demographic data (e.g., population density, racial composition)
⊳ Law enforcement statistics (e.g., police presence, per capita law enforcement spending)
⊳ Data Preprocessing & Feature Engineering: Handling missing values, scaling numerical features, and selecting relevant predictors.
⊳ Exploratory Data Analysis (EDA): Understanding correlations between community features and crime rates.
⊳ Regression Models: Applying linear regression, ridge regression, LASSO, and elastic net to establish baseline predictive performance.
⊳ Machine Learning Implementation: Experimenting with random forests, gradient boosting, and deep learning to improve prediction accuracy. (working progress)
⊳ Model Evaluation: Comparing models based on metrics like R², RMSE, and MAE to determine the best approach.
⊳ Incorporating geospatial analysis to visualize crime distribution.
⊳ Exploring deep learning architectures for improved predictions.