I leverage Natural Language Processing (NLP) and text analytics techniques to extract valuable insights from unstructured text data. I'm proficient in Python and its NLP libraries (NLTK, spaCy) and have experience in text preprocessing, feature extraction, and building classification models.

My services include:

• Text Classification: Building and deploying models to categorize text into predefined categories, such as sentiment analysis, topic classification, or spam detection.

• Text Preprocessing and Feature Extraction: Cleaning and transforming raw text data, removing stop words, stemming/lemmatization, and extracting relevant features using techniques like TF-IDF or word embeddings.

• Named Entity Recognition (NER): Identifying and extracting named entities (people, organizations, locations, etc.) from text data.

• Sentiment Analysis: Analyzing text to determine the sentiment or emotional tone expressed (positive, negative, or neutral).

• Topic Modeling: Discovering hidden topics or themes within a collection of documents.

I have experience in:

• Analyzing customer reviews to uncover insights and trends using NLP techniques.

• Building predictive models to understand factors influencing customer behavior based on text data.

I'm eager to apply my NLP and text analytics skills to help you gain a deeper understanding of your text data and make informed decisions.