KSP-final - Prototype

Hi, We are Siren Squad! 👋

Predictive Crime Analytics

Predictive Crime Analytics is a web-based application developed using Django that offers advanced crime analysis and prediction capabilities. Leveraging machine learning algorithms and historical crime data, the platform provides insights into crime hotspots, trends, and patterns. Users can explore crime statistics, conduct behavioral analysis of criminals and victims, and predict future crime occurrences based on past data. The application also offers month-wise crime group analysis, empowering law enforcement agencies and policymakers with valuable information for proactive crime prevention strategies. With its user-friendly interface and powerful analytics features, Predictive Crime Analytics aims to enhance public safety and support decision-making in combating crime.

Technology Used:

• Python
• HTML
• CSS
• JavaScript
• Bootstrap
• Django

Libraries Used:

• Seaborn
• Plotly
• numpy
• pandas
• fb prophet
• Scikit-learn
• folium
• geocoder -matplotlib

API Used:

• ApexCarts.js

ScreenShots

Installation

Install my-project with git clone

Get started!

   create a folder in the name you needed `your folder name`

  git clone https://github.com/sandeepstele/KSP-final

Environment Variables

To run this project, you will need to run the virtual Environment file myVenv

   source myVenv/Scripts/activate

If your system doesn't have the virtual Environment then,

   pip install venv

   python3 -m venv .venv

   source .venv/bin/activate

For Unix/MacOS

To Run the project

After cloning and running myVenv

To run the myVenv in the Git Bash Terminal

source myVenv/Scripts/activate

Install django in the virtual environment

  pip install django

start the database to run

   python manage.py makemigrations

   python manage.py migrate

After migration to manage the database create a superuser

   python manage.py createsuperuser

Go to the project directory

  cd Datathon

Start the server

  python manage.py runserver

Open Your Browser:

Visit http://localhost:8000 to start exploring Predictive Crime Analytics!

Features:

Geospatial Analysis using K-means and hotspot is visualised on Map

• District Based
• Crime Based
• District and Crime inter-connected

Socio-Economic Analysis of Data:

• Plotting heatmaps for understand the occupational distrobution among victims and criminals.
• Pie chart for understand the district distribution based on crime occurences.

Identify correlated crimes occurring together within the same timeline and visualise their distribution among beat duties:

• Correlated Crimes: Analyze most occurring time groups to find correlated crimes that frequently co-occur.
• Visualization: Create a bar graph showing counts of correlated crime pairs for quick insights.
• Beat Duty Distribution: Generate pie charts illustrating how occurrences are distributed among beat duties for each correlated crime pair.