Data-Driven Insights: Uses historical crime records, geographic data, and socioeconomic indicators to identify patterns.

Predictive Algorithms: Implements machine learning models such as Decision Trees, Random Forests, SVM, and Neural Networks.

Spatial & Temporal Analysis: Accounts for time-based and location-based crime trends.

Visualization Tools: Often includes heatmaps and dashboards to assist police departments in resource allocation.

Real-Time Adaptability: Advanced systems can update predictions dynamically as new data becomes available.

To survey and analyze various machine learning techniques used in predicting crime rates.

To evaluate the accuracy and efficiency of different models in real-world datasets.

To understand the limitations of current predictive systems and suggest future improvements.

To explore ethical considerations related to fairness, bias, and data privacy in predictive policing.

Proactive Law Enforcement: Helps authorities prevent crimes before they occur by identifying high-risk areas and times.

Resource Optimization: Allows better deployment of personnel and surveillance based on predictive analytics.

Public Safety Enhancement: Reduces crime rates by enabling faster responses and targeted actions.

Policy Making: Supports urban planning and public safety policy decisions with empirical evidence.

Ethical Tech Advancement: Encourages development of responsible AI systems that ensure fairness and transparency.

Physiological Signal Analysis: Uses data from heart rate, EEG, GSR, and respiration to identify stress patterns.

Behavioral Monitoring: Tracks typing speed, facial expressions, and speech tone using ML algorithms.

Text and Voice-Based Detection: Analyzes voice tone or written messages (emails, chats) to detect stress through Natural Language Processing (NLP).

Real-Time Detection: Enables continuous monitoring for applications in healthcare, workplace, and education.

Multi-Modal Systems: Combines data from multiple sensors (wearable devices, smartwatches, etc.) for higher accuracy.

To develop a robust and accurate model capable of detecting stress in real-time.

To compare the performance of traditional ML models (SVM, Decision Tree, Random Forest) with advanced DL architectures (CNN, LSTM, Bi-LSTM).

To analyze the effectiveness of different data types (physiological, textual, audio) in stress prediction.

To create a prototype system that could be deployed in wearable technology or mental health monitoring apps.

Mental Health Support: Early stress detection can prevent anxiety, depression, and burnout.

Workplace Productivity: Helps organizations manage employee well-being and reduce absenteeism.

Personalized Healthcare: Enables doctors and therapists to monitor patients remotely and intervene proactively.

Real-Time Applications: Useful in high-stress professions (pilots, doctors, soldiers) for safety and performance monitoring.

Innovation in Wearable Tech: Lays the foundation for smartwatches and fitness trackers to become mental wellness devices.