The course project is an opportunity for student groups to investigate a current analytics and data mining project using Big Data in the Cloud.
This domain focuses on analyzing and building predictive models using a gun violence dataset containing information on incidents in the United States from January 2013 to March 2020. Preprocessing steps include data cleaning, feature engineering, data transformation, and feature selection. Various machine learning models can be applied, such as decision trees and random forests, gradient boosting machines, and neural networks, classification methods to achieve good performance metrics. Metrics such as precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic (ROC) curve should be used to measure model performance as they provide more informative results than accuracy alone for imbalanced datasets. By using Python 3.x and necessary libraries like pandas, numpy, matplotlib, seaborn, and scikit-learn, analysts can gain insights into the patterns and factors contributing to gun violence incidents and build models to predict future incidents.
Data : https://www.kaggle.com/datasets/jameslko/gun-violence-data https://www.kaggle.com/datasets/konivat/us-gun-violence-archive-2014 https://www.gunviolencearchive.org
One of the primary business problems that can be addressed with this dataset is to identify the factors associated with gun violence incidents. This includes understanding the demographics of the victims and perpetrators, the types of weapons used, the location and timing of incidents, and other key variables that may be relevant. By analyzing these factors, law enforcement agencies, policymakers, and community organizations can develop targeted interventions to prevent gun violence and improve public safety.
Another business opportunity that can be identified with this dataset is to explore the impact of gun control policies on gun violence. This includes analyzing the effectiveness of existing policies, identifying gaps in policy implementation, and exploring potential policy solutions to reduce gun violence. This information can inform policy development and implementation at the local, state, and national levels.
Moreover, the dataset can be used to identify high-risk areas and populations for gun violence and develop interventions that address the root causes of gun violence, such as poverty, unemployment, and mental health issues. By targeting these factors, stakeholders can reduce the incidence of gun violence and improve the health and safety of communities.
In summary, the gun violence dataset presents a significant business opportunity to better understand the complex issue of gun violence, develop evidence-based interventions, and inform policy decisions. By analyzing this data, stakeholders can work together to reduce the harm caused by gun violence and promote public safety.