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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