Model accuracy plateaued at 78% on image classification task

Improved accuracy to 92% using transfer learning and data augmentation

Sentiment analysis model struggled with sarcasm

Implemented transformer architecture, raising F1 score from 0.68 to 0.84

Recommendation engine yielded low click‑through rates

Introduced collaborative filtering with matrix factorization, boosting CTR by 27%

Speech‑to‑text system had high word error rate in noisy environments

Added noise‑robust preprocessing and a CNN‑RNN hybrid, cutting error rate by 35%

Time‑series forecast deviated by ±15% on demand prediction

Applied Prophet with holiday effects, reducing mean absolute error to 4%