This repository contains a machine learning project focused on predicting patient conditions based on textual prompts. The model classifies inputs into categories such as depression, birth control, and acne.
- Utilizes advanced NLP techniques to analyze patient-provided text inputs.
- Classifies conditions into categories like depression, birth control, and acne.
- Comprehensive Exploratory Data Analysis (EDA) performed to ensure high-quality insights.
- Built using state-of-the-art NLP tools and techniques.
-
Conditions Count Analysis: A visualization showcasing the distribution of different patient conditions, helping identify the most and least common categories.
-
Birth Control EDA: A focused analysis on birth control-related cases, providing insights into trends and patterns within this category.
These analyses help in understanding the dataset structure and guiding model development.
- Clone the repository:
git clone https://github.com/Prtm2110/Patient-Condition-Predictor.git
- Navigate to the project directory:
cd Patient-Condition-Predictor - Install required dependencies:
pip install -r requirements.txt
- Run the Streamlit application:
streamlit run app.py
- Natural Language Processing (NLP)
- Deep Learning (Keras, TensorFlow)
- Exploratory Data Analysis (EDA)
- Streamlit (for web app deployment)
- Python (NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn)
For any inquiries, feel free to reach out via GitHub Issues or email.
Happy Coding! 🚀