# 🔬 Investigate machine learning models for deal close probability prediction
**ID:** SPIKE-0002 | **Feature:** FEAT-0006 | **Status:** planned
**Assignee:** Unassigned
**Timebox:** 2 weeks
Explore whether machine learning models can improve forecast accuracy beyond simple stage-based probability. Research what data features are most predictive of deal closure, evaluate ML frameworks, and determine if we have sufficient historical data for training.
## Research Questions (0/5 answered)
### 1. ❓ Do we have sufficient historical deal data (volume and quality) to train a reliable ML model?
_Not answered yet_
### 2. ❓ Which features are most predictive of deal closure (deal size, age, activity frequency, contact engagement, etc.)?
_Not answered yet_
### 3. ❓ What ML approaches are most suitable (logistic regression, random forest, gradient boosting, neural networks)?
_Not answered yet_
### 4. ❓ How much would ML-based forecasting improve accuracy compared to current stage-based method?
_Not answered yet_
### 5. ❓ What are the infrastructure and maintenance requirements for deploying an ML model in production?
_Not answered yet_
## Objectives
1. Analyze historical deal data to assess data quality and volume (need 1000+ closed deals minimum)
2. Perform feature engineering and correlation analysis to identify predictive features
3. Build prototype models using at least 3 different algorithms
4. Compare model accuracy against baseline stage-based probability method
5. Estimate infrastructure costs and ongoing maintenance effort
6. Provide go/no-go recommendation with detailed justification
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_Created: 2025-03-07T14:00:00Z | Updated: 2025-03-07T14:00:00Z_
_Tags: deals, forecasting, machine-learning, research_
