In this project, I applied Artificial Intelligence techniques to solve a text classification problem using Natural Language Processing (NLP). The objective was to create a model that can automatically classify text data into predefined categories, leveraging the power of deep learning with TensorFlow.

The project was developed using Python and utilized TensorFlow’s Sequential API, which allowed me to build a simple yet effective deep learning model for text classification. The first phase involved data exploration where I thoroughly analyzed the text data, examined its structure, and identified key patterns to better understand the task at hand. I explored aspects such as the frequency of words, text lengths, and distribution across different classes, which provided valuable insights into the dataset.

Following data exploration, I performed essential data preprocessing steps, including normalization, tokenization, and lemmatization. Normalization involved converting text into a consistent format, removing any inconsistencies in the dataset. I used tokenization to break down the text into words or tokens, which are more manageable for the model to process. To reduce complexity, I implemented lemmatization, which helped reduce words to their base forms, ensuring that variations of the same word were treated as a single feature.

Once the data was cleaned and prepared, I built a text classification model using TensorFlow’s Sequential API. The model architecture was designed with an embedding layer to represent words as dense vectors, followed by dense layers for classification. I used the Adam optimizer, known for its efficiency in training deep learning models, to minimize the loss function and improve the model’s accuracy.

This project demonstrated my ability to apply deep learning techniques to text classification tasks using NLP. It showcased my understanding of the entire pipeline, from data preprocessing and exploration to building, training, and optimizing a machine learning model. By implementing the Adam optimizer, I was able to enhance model performance, ensuring that the model was both efficient and accurate in classifying text.

Overall, this project deepened my knowledge of NLP, TensorFlow, and deep learning. It highlighted my ability to develop models that can handle real-world text data and automatically classify it into meaningful categories, an essential skill for solving a wide range of problems in AI and machine learning.