Predictive Policing Event Bot

Inspired by Neuromancer's data scavenging and Ex Machina's predictive AI, this project leverages publicly available event calendars (scraped using techniques similar to the 'Event Calendars' scraper project) to predict potential upticks in petty crime. The story: In a near-future struggling with resource allocation, local community watch groups are overwhelmed. This project provides them with early warnings of potential crime 'hotspots' based on the confluence of public events (concerts, festivals, protests) and historical crime data. Concept: The system scrapes event data (location, time, type of event) from various online sources. This data is then fed into a simple machine learning model (e.g., logistic regression or a basic neural network) trained on historical crime data correlated with past events. The model outputs a 'risk score' for each event location, indicating the likelihood of petty crime. How it works: 1. Web Scraping: Use Python libraries like Beautiful Soup and Scrapy to extract event information from websites like Eventbrite, local community calendars, and social media platforms. 2. Data Preprocessing: Clean and format the scraped data, including geocoding event locations and categorizing event types. 3. Machine Learning: Train a model using historical crime data (publicly available crime statistics or incident reports). Features might include event location, event type, day of the week, time of day, weather conditions (if available). 4. Risk Scoring: Apply the trained model to new event data to generate a risk score for each event location. 5. Alert System: Develop a simple web interface or mobile app to display event locations with corresponding risk scores, allowing community watch groups to focus their efforts on high-risk areas. Earning Potential: This system can be offered as a subscription service to community watch groups, neighborhood associations, or even small businesses. The value proposition is increased security and reduced losses due to petty crime. Revenue can be generated through tiered subscription plans based on the number of locations monitored or the level of support provided. The niche and low cost (relying on open-source tools and publicly available data) makes it a viable individual project with scalable earning potential.