This project contains code for performing data analytics and building a predictive model. It covers various stages of the data science workflow, including data cleaning, exploration, analysis, and modeling.
This notebook includes the following main sections:
Data Cleaning: Cleaning and preprocessing of the dataset. Data Exploration: Exploring various aspects of the dataset such as age distribution, race, gender, diagnosis data, length of stay, etc. Data Analysis: Analyzing the relationships between different variables and their impact on readmissions. Modeling: Building a logistic regression model to predict readmissions based on selected features.
The notebook requires the following Python libraries:
- pandas
- numpy
- matplotlib
- seaborn
- sklearn
- statsmodels
These libraries can be installed via pip or conda. Example:
pip install pandas numpy matplotlib seaborn scikit-learn statsmodels
- Open the notebook using Jupyter Notebook, JupyterLab, or Google Colab.
- Make sure to have the required libraries installed.
- Run the notebook cells sequentially to execute the code step by step.
- Refer to the comments and markdown cells for explanations and insights.
- Customize the code and analysis according to your specific requirements.
This notebook was authored by Khalid Lawal.
This project is licensed under the [License Name] License - see the LICENSE.md file for details.