CASE STUDY

OIL AND GAS

  1. Sriya-AI can predict equipment failures and maintenance needs to reduce downtime and improve efficiency.
  2. Dataset contains 60,000 records with 17 Categorical Features and 26 Numerical Features.
  3. Regression models were created using Lasso, Random forest, XGBoost, Recurrent Neural Network and Artificial Neural Network.
  4. Performance measurement values were obtained for feature sets of 17, 34, 51 and 61 with an average accuracy between 75% to 78%.