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The Tech Behind Safety Conscious
Navigation
The application is built around police data collection, previous location, and precise criminology details, In terms of classifying crime, the utilization of K-means algorithms is taken into account in our construction. K-means is a popular clustering algorithm used in unsupervised machine learning to partition a given dataset into k distinct groups based on their feature similarities. Each group represents a cluster, and the algorithm minimizes the within-cluster variance.
The Process
Gathering crime data
ensuring it contains relevant features that describe each crime incident (e.g., location, type of crime, date, time, etc.). Preprocess the data by cleaning, normalizing, and transforming it as needed.
SORTING
Select the features from the crime data most relevant to analyze. In our case, focus on the geographic coordinates (latitude and longitude), time, and the type of crime.
Most likely, the data units would have different measurements, some being discrete, while some being percentage amounts; standardization techniques/normalization will take place to represent “feature selection.”
THE ELBOW METHOD
Select the features from the crime data most relevant to analyze. In our case, focus on the geographic coordinates (latitude and longitude), time, and the type of crime.
Most likely, the data units would have different measurements, some being discrete, while some being percentage amounts; standardization techniques/normalization will take place to represent “feature selection.”
Working Towards Redifing Navigation Systems
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