Inspiration

Problem Statement : “Last year, an alarming increase in homicides left communities reeling as officials searched for answers. Over the past few decades, the rate at which this horrendous crime is increasing is alarming, and Governments and Police all over have been looking for insights and solutions to help them understand the situation more.”

Solution: We used the Homicide Report 1980-2014 Dataset from Kaggle (https://www.kaggle.com/murderaccountability/homicide-reports) to understand the different cases related to homicides in the US for the years 1980-2014 and plotted necessary graphs to correlate possible features and developed an ML model to predict if a homicide case can solved or not using the given dataset.

What it does

We used the Homicide Report 1980-2014 Dataset from Kaggle (https://www.kaggle.com/murderaccountability/homicide-reports) to understand the different cases related to homicides in the US for the years 1980-2014 and plotted necessary graphs to correlate possible features and developed an ML model to predict if a homicide case can solve or not using the given dataset.

How we built it

Using Google Colab , modules include sklearn, pandas, seaborn, etc.

Challenges we ran into

Time Management and Categorical Data Encoding

Accomplishments that we're proud of

ML Model (Random Forest)

What we learned

Time Management

What's next for Homicide Report 1980-2014

Neural Network to predict the same and increase accuracy of the model

Built With

  • colab
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