Predictive Employee Attrition System (PEAS)

PEAS draws inspiration from 'Order Histories' (analyzing patterns), 'Nightfall' (predicting behavior during crises), and '12 Monkeys' (using historical data to prevent a negative future). Imagine a scenario where an HR department, plagued by high turnover (like the impending darkness in Nightfall), needs to proactively address employee attrition before it cripples the company (like the virus in 12 Monkeys). PEAS helps them do just that.

Concept:
PEAS scrapes readily available internal data – performance reviews, promotion history, salary changes, project assignments, training completion rates, internal communication patterns (email frequency, slack channel participation), time logs (overtime hours), and even exit interview data (if available). This data is then fed into a machine learning model (built using libraries like scikit-learn or TensorFlow) trained to identify patterns that correlate with employee attrition. The model analyzes this data like the 'Order Histories' scraper but focused on HR data. The system assigns a "retention risk score" to each employee.

How it Works:
1. Data Collection: PEAS connects to existing HR systems (API integrations, database access, or even manual CSV uploads). It normalizes and cleans the data, dealing with missing values and inconsistencies.
2. Feature Engineering: The system automatically creates relevant features from the raw data. Examples: tenure, average performance rating, number of promotions in the last 3 years, recent project complexity, average overtime hours per week, sentiment analysis of internal communications. This process is key as useful data that might be overlooked can be used to identify attrition patterns.
3. Model Training & Prediction: A machine learning model (e.g., logistic regression, random forest, gradient boosting) is trained on historical attrition data. The model predicts the probability of an employee leaving the company within a specified timeframe (e.g., 6 months, 1 year).
4. Alerts and Reporting: HR managers receive alerts for employees with high retention risk scores. The system provides reports summarizing overall attrition risk, identifying key drivers of attrition, and suggesting potential interventions (e.g., offering a promotion, providing additional training, adjusting workload). This proactive approach is what sets it apart.
5. Intervention Tracking: PEAS allows HR to track the effectiveness of interventions. Did offering additional training reduce the attrition risk score for a particular employee? This feedback loop allows the model to learn and improve its predictions over time.

Niche & Low Cost:
- Focuses on a specific HR problem: attrition prediction.
- Utilizes existing data sources within companies.
- Can be implemented with open-source machine learning libraries.
- No expensive hardware or infrastructure is required (cloud-based deployment).
- Targeted at small to medium-sized businesses that can't afford expensive enterprise HR solutions.

High Earning Potential:
- Subscription-based pricing model (e.g., per employee per month).
- Value proposition: reduce employee turnover costs and improve employee engagement.
- Potential for add-on features, such as employee well-being tracking and personalized career development plans.
- Partnerships with HR consulting firms to provide implementation and support services.