Medicare with NLP

Overview

Medicare with NLP is a natural language processing (NLP) project that focuses on analyzing and processing medical text data. In this project, we use various NLP techniques and machine learning models to extract valuable insights from medical text data. The project involves text cleaning using regular expressions (re), text preprocessing with the Natural Language Toolkit (nltk), visualizations using WordCloud and a frequency distribution plot (FreqDist), feature engineering by flattening and splitting data into numerical and categorical columns, exploring data relationships with a heatmap, data transformation using robust scaling, and one-hot encoding with get_dummies. Additionally, we build two predictive models, a Linear Regression model and a Decision Tree Regressor, to make predictions based on the processed data.

Introduction

The Medicare with NLP project is aimed at leveraging natural language processing techniques to extract insights from medical text data. We follow a structured approach to clean and preprocess the data, explore relationships within the dataset, and build predictive models to assist with medical data analysis.

Data Cleaning

We start by cleaning the medical text data using regular expressions (re) to remove unwanted characters and symbols. Afterward, we utilize the Natural Language Toolkit (nltk) for further text preprocessing, such as tokenization, stop word removal, and stemming.

Exploratory Data Analysis

We perform exploratory data analysis (EDA) to gain insights into the dataset. This includes generating a WordCloud to visualize common terms and creating a frequency distribution plot (FreqDist) to analyze word frequency.

Feature Engineering

We flatten and split the data into numerical (numcols) and categorical (objcols) columns for further processing. A heatmap is used to visualize relationships between variables. To prepare the data for modeling, we apply robust scaling and one-hot encoding with get_dummies.

Machine Learning Models

We build two predictive models:

• Linear Regression: A linear regression model is created to predict medical outcomes based on the processed data.
• Decision Tree Regressor: A decision tree regressor is trained to provide non-linear predictions based on the dataset.

Contributing

Contributions to this project are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request.

License

This project is licensed under the GPU License. Feel free to use, modify, and distribute it as needed.