Crime against Women Analytics

Safety of women is a priority!Crime | Women Empowerment

Inspiration

Safety and well-being being the need of the hour, motivated us to take up this domain of crime against women, streamlining it to target and do a comparitive study with the rate of alcohol consumption.

Problem

Ineffective deployment and utilisation of the police forces and resources leads to a highly disorganised system and the barrier against crime is compromised. We have made an attempt to strengthen this barrier!

Solution

We analyse the crime rates, as mentioned earlier and make predictions for the future. Based on these predictions, we recommend measure that can be taken to ensure the prevention of these crimes.

What it does

Using our knowledge of data analytics and machine learning, we are trying to decipher the crime rate statistics for each district in the India districts and states based on the loacation. The alcohol consumption dataset reviews how the urban and rural areas, specifically, affect the crime rate of domestic violence in these regions. It helped us make a correlation between these two factors and gain a deeper understanding. Our conclusion has been really well reflected in our inference.

How we built it

Using the IBM-Z L1CC VM Instance and Jupyter Lab which provided us with pre-installed & conveniently accessible packages. The IBM-Z Open Editor extension on Visual Studio Code helped us collaborate and work effectively.

Our reason for choosing KNN alogrithm was because it provided the most accurate values for our training dataset.

Challenges we ran into

After long and dedicated hours of finding the ideal dataset, handling the large amount of data and ensuring it's normalisation was a tough task, but we are glad we got that done. Training our model to make an effective prediction was another challenge we had to face head-on and added to the overall challenging, yet amazing experience.

Accomplishments that we're proud of

Making a detailed inference and ESTABLISHING CORRELATION BETWEEN ALCHOL CONSUMPTION IN RURAL AREAS & CRIME RATE IN THESE REGIONS. Reaching a decent stage to ensure our project submission within the time constraints, feels like an achievement in itself.

What we learned

The importance of crime prevention & the effective usage of ML/DL algorithms on a larger scale. We realised the importance that data holds in today's world and the vast amount of potential it holds, especially in terms of hidden relationships and correlations. Team work and the effective managemnt of time are two things we have surely learnt to value and understand better after this datathon.