You will get Python, NLP, Deep Learning, Deployment
Project details
This project stands out due to its comprehensive application of advanced machine learning and deep learning techniques to perform sentiment analysis on hospital reviews. By leveraging BERT (Bidirectional Encoder Representations from Transformers), one of the most sophisticated NLP models available, we achieved highly accurate predictions of hospital ratings based on patient reviews. This project not only harnesses the power of cutting-edge AI but also translates it into actionable insights that can significantly improve hospital services and patient satisfaction.
What Sets Me and My Project Apart
Innovative Use of BERT
State-of-the-Art NLP: Utilizing BERT for fine-tuning on hospital reviews data allowed us to capture complex semantic relationships and context in the text, outperforming traditional models.
Precision and Accuracy: Achieved high accuracy, precision, recall, and F1 scores, demonstrating the model's effectiveness in understanding and predicting sentiments.
Comprehensive Evaluation Metrics
What Sets Me and My Project Apart
Innovative Use of BERT
State-of-the-Art NLP: Utilizing BERT for fine-tuning on hospital reviews data allowed us to capture complex semantic relationships and context in the text, outperforming traditional models.
Precision and Accuracy: Achieved high accuracy, precision, recall, and F1 scores, demonstrating the model's effectiveness in understanding and predicting sentiments.
Comprehensive Evaluation Metrics
Data Tool
PythonWhat's included $11
These options are included with the project scope.
$11
- Delivery Time 2 days
- Number of Pages Mined/Scraped 52
- Number of Sources Mined/Scraped 5
- Number of Revisions 1
Optional add-ons
You can add these on the next page.
Fast 1 Day Delivery
+$3,000
Additional Page Mined/Scraped
(+ 3 Days)
+$11
Additional Source Mined/Scraped
(+ 3 Days)
+$20
Additional Revision
+$20About Akshata
Data Scientist
Hyderabad, India - 1:26 pm local time
-Utilized web-scraping as a source of data extraction for HCP data extraction.
-Analyzed disease conditions and worked on tasks like data mining, validation, and cleansing.
-Leveraged machine learning, deep learning (BERT), PyTorch, Python, and data extraction methods (e.g., web scraping), and text analytics to streamline processes and provide valuable insights.
-Conducted ID profiling using classifiers (Machine Learning Approach).
-Implemented Deep Learning practices to ensure scalable and maintainable deployment of machine learning models.
-Applied computer vision techniques to analyze medical images and extract relevant features for disease prediction and monitoring
-Employed generative AI techniques to streamline processes and provide valuable insights into health-care-related tasks, such as disease analysis and data mining.
-Performed Sentiment Analysis(NLP: CNN & LLM Approach) to rate HCP based on his CRI sessions and Clinical trials.
Key Projects:
-Colgate: Conducted Dentistry HCPs Mapping project utilizing generative AI techniques for data synthesis and augmentation.
-BioMarin: Analyzed hospitals treating MPS-II, Orthopaedic Disorders using NPI Data base
-Centrixion: Identified hospitals treating Orthopedic Surgery, focusing on mining US HCPs, EHRs, obituaries, and mortality prediction for patients with rare diseases, integrating computer vision for patient monitoring
Steps for completing your project
After purchasing the project, send requirements so Akshata can start the project.
Delivery time starts when Akshata receives requirements from you.
Akshata works on your project following the steps below.
Revisions may occur after the delivery date.
Delivery time starts when I receive the project