Crime Prediction & Hotspot Analysis
ML model that predicts crime patterns from historical data with interactive Folium map visualizations and a Streamlit UI.
About This Project
A machine learning project that analyzes historical crime data to predict future crime hotspots. Uses Random Forest, XGBoost, and LSTM models for prediction. Features interactive Folium map overlays, time-series analysis, crime type classification, Streamlit web interface, and a detailed research report. Great for CSE/Data Science final year projects with strong academic value.
What You Will Get
Full Source Code
Every file, folder, and config — no hidden parts
Project Report
Academic-format documentation, ready to submit
Presentation (PPT)
Slide deck for viva and project presentation
Database Files
SQL dump or Firestore export with sample data
Setup Instructions
Step-by-step installation and run guide
WhatsApp Support
Developer helps you understand the code
Tech Stack
Who Is This Project For?
Final Year Students
Submit a complete, impressive final-year project
Last-Minute Submissions
Deadline approaching? Get instant access
Beginners
Learn by reading and modifying real, working code
Internship Seekers
Enhance your portfolio with real-world projects
Learners
Understand how complete apps are built end-to-end
Competition Participants
Start strong with a solid, working codebase
Tags
Confused? Talk to the Developer
Our developer will answer all your questions before you buy — project features, submission readiness, viva prep, and more.
Frequently Asked Questions
Secure Payment
256-bit SSL
Instant Delivery
After payment
Verified Code
Expert reviewed
7-Day Refund
No questions asked