I have developed an automated system for recommending training paths, capable of suggesting training areas adapted to each trainee profile. The solution uses collaborative filtering techniques and analysis of qualification history to improve the personalization and effectiveness of the training offer.
- Goal
Increase take-up of complete courses and reduce drop-outs by suggesting training courses in line with the interests, previous experiences and needs of each trainee.
- Technologies and Tools
- Python (Collaborative Filtering)
- Pandas
- SQL
- Results Obtained
- 87% acceptance rate of the recommendations generated
- +22% joining complete courses
- −17% dropping out of training
- Strategic Impact
This system has been integrated into the association's strategy as a tool to support continuous qualification, increasing the degree of personalization of training paths. It has helped to optimize the match between supply and demand, improving the trainees' experience and the effectiveness of the training programmes.
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