This study investigated the effectiveness of various models in deep learning in performing sentiment analysis on code-mixed Hinglish text, a hybrid language is widely used in digital telecommunication. Hinglish presents unique challenges due to its informal nature, frequent code-switching, and complex linguistic structure. This research leverages datasets from the HinGE, SemEval-2020 Task 9 & E-Commerce Reviews, datasets, competition, and employ models such as RNN (LSTM), BERT-LSTM, CNN, and a proposed BiLSTM model with Data Augmentation.