OPTIMAL PARAMETER SELECTION FOR WOMEN SAFETY ANALYSIS

In today's world, women's safety is a significant and crucial problem. Even in these modern times, where technology has improved considerably, women's safety remains a worry. Incidences of rape, stalking, taunting, sexual assault, molestation, harassment, domestic abuse and other forms of violence are progressing daily. Many rules and regulations have been enacted to prevent these heinous acts. Despite this, the rate of crime is rapidly increasing. The Government has developed a number of devices and applications; however they do not make use of advanced technologies like Machine Learning, Data Science etc. The paper highlights various research papers in the context of women safety, women awareness and machine learning process. Machine learning has the power to predict and analyze the data in an effective and efficient way. As the quality of data plays a vital role during the analysis, so data preparation is a crucial step of machine learning process because the raw data may be noisy, incomplete or inconsistent. The collected data from women respondents is prepared and pre-processed to transform the raw data into a structured and understandable format. Lastly, the data is analyzed using principal component analysis, KMO and Bartlett's Test to predict the important and correlated parameters that influence the women safety