-Developed a predictive model using the state-of-the-art LSTM architecture to assess health of XLPE cables -Leveraged LSTM's ability to capture temporal patterns in sequential data for over 93% accuracy -Utilized 5 different features like age, neutral corrosion and relevant factors for accurate predictions -Demonstrated the model's efficacy in predicting cable health for 2500 power cables in testing