Context Introduction: The dataset used for this experiment is real and authentic. The dataset is acquired from UCI machine learning repository website [13]. The title of the dataset is ‘Crime and Communities’. It is prepared using real data from socio-economic data from 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crimedata from the 1995 FBI UCR [13]. This dataset contains a total number of 147 attributes and 2216 instances. The per capita crimes variables were calculated using population values included in the 1995 FBI data (which differ from the 1990 Census values). Content The variables included in the dataset involve the community, such as the percent of the population considered urban, and the median family income, and involving law enforcement, such as per capita number of police officers, and percent of officers assigned to drug units. The crime attributes (N=18) that could be predicted are the 8 crimes considered 'Index Crimes' by the FBI)(Murders, Rape, Robbery, .... ), per capita (actually per 100,000 population) versions of each, and Per Capita Violent Crimes and Per Capita Nonviolent Crimes) predictive variables : 125 non-predictive variables : 4 potential goal/response variables : 18 Acknowledgements http://archive.ics.uci.edu/ml/datasets/Communities%20and%20Crime%20Unnormalized U. S. Department of Commerce, Bureau of the Census, Census Of Population And Housing 1990 United States: Summary Tape File 1a & 3a (Computer Files), U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992) U.S. Department of Justice, Bureau of Justice Statistics, Law Enforcement Management And Administrative Statistics (Computer File) U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992) U.S. Department of Justice, Federal Bureau of Investigation, Crime in the United States (Computer File) (1995) Inspiration Your data will be in front of the world's largest data science community. What questions do you want to see answered? Data available in the dataset may not act as a complete source of information for identifying factors that contribute to more violent and non-violent crimes as many relevant factors may still be missing. However, I would like to try and answer the following questions answered. 1. Analyze if number of vacant and occupied houses and the period of time the houses were vacant had contributed to any significant change in violent and non-violent crime rates in communities 2. How has unemployment changed crime rate(violent and non-violent) in the communities? 3. Were people from a particular age group more vulnerable to crime? 4. Does ethnicity play a role in crime rate? 5. Has education played a role in bringing down the crime rate?