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https://github.com/idaraabasiudoh/drug_prescribtion_decision_tree_model
This repository contains a machine learning project focused on classifying drugs based on patient characteristics using a Decision Tree classifier. The project uses Python and popular data science libraries such as scikit-learn, pandas, and matplotlib.
https://github.com/idaraabasiudoh/drug_prescribtion_decision_tree_model
data-analysis jupyter-notebook machine-learning python3 scikit-learn
Last synced: about 23 hours ago
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This repository contains a machine learning project focused on classifying drugs based on patient characteristics using a Decision Tree classifier. The project uses Python and popular data science libraries such as scikit-learn, pandas, and matplotlib.
- Host: GitHub
- URL: https://github.com/idaraabasiudoh/drug_prescribtion_decision_tree_model
- Owner: idaraabasiudoh
- License: mit
- Created: 2024-08-10T01:58:46.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-10T02:16:58.000Z (5 months ago)
- Last Synced: 2024-11-14T21:12:19.502Z (2 months ago)
- Topics: data-analysis, jupyter-notebook, machine-learning, python3, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 131 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Drug Prescription Decision Tree Classifier
## Table of Contents
- [Introduction](#introduction)
- [Objectives](#objectives)
- [Dataset](#dataset)
- [Installation](#installation)
- [Usage](#usage)
- [Modeling](#modeling)
- [Evaluation](#evaluation)
- [Visualizing the Decision Tree](#visualizing-the-decision-tree)
- [Contributions](#contributions)
- [Acknowledgments](#acknowledgments)
- [Change Log](#change-log)
- [License](#license)
## Introduction
This repository contains a machine learning project focused on classifying drugs based on patient characteristics using a Decision Tree classifier. The project uses Python and popular data science libraries such as scikit-learn, pandas, and matplotlib.
## Objectives
The primary objectives of this project are:
- To implement a Decision Tree classifier using scikit-learn to predict drug type.
- To train, test, and evaluate the model on a dataset of patient characteristics and corresponding drug prescriptions.
- To visualize the decision tree for interpretability.
## Dataset
The dataset used in this project, `drug200.csv`, contains data on 200 patients, including age, sex, blood pressure (BP), cholesterol levels, the ratio of sodium to potassium, and the drug type prescribed. The dataset includes the following columns:
- `Age`: Age of the patient.
- `Sex`: Gender of the patient (F or M).
- `BP`: Blood Pressure level (LOW, NORMAL, HIGH).
- `Cholesterol`: Cholesterol level (NORMAL, HIGH).
- `Na_to_K`: Ratio of sodium to potassium in the blood.
- `Drug`: Type of drug prescribed.
[Dataset Source](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/drug200.csv)
### Requirements
Ensure you have the following dependencies installed:
- `Python 3.x`
- `numpy`
- `pandas`
- `scikit-learn`
- `matplotlib`
## Installation
To run this project locally, you need to have Python installed along with the required libraries. You can install the necessary packages using the following command:
Clone the repository and install the necessary dependencies:
```bash
git clone https://github.com/idaraabasiudoh/drug-classification-decision-tree.git
cd drug-classification-decision-tree
pip install -r requirements.txt
```
## Usage
To use this repository, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/idaraabasiudoh/drug-classification-decision-tree.git
```
2. Navigate to the project directory:
```bash
cd drug-classification-decision-tree
```
3. Run the classification script:
```bash
python classify_drug.py
```
## Modeling
The modeling process involves the following steps:
1. **Data Loading**: Import the dataset and understand its structure.
2. **Data Preprocessing**: Convert categorical variables to numerical values using label encoding.
3. **Data Splitting**: Split the data into training and testing sets.
4. **Model Training**: Train a Decision Tree model using the training set.
5. **Model Prediction**: Make predictions on the test set using the trained model.
## Evaluation
The performance of the model is evaluated using the test dataset. The key metrics used for evaluation include:
- **Accuracy**: This metric indicates the proportion of correct predictions made by the model out of all predictions.
```python
from sklearn import metrics
print("Decision Tree's Accuracy:", metrics.accuracy_score(y_testset, predTree))
```
### Example Code
Here is an example of how to evaluate the model using accuracy:
```python
from sklearn import metrics
# Assuming y_testset contains the actual values and predTree contains the predicted values
print("Decision Tree's Accuracy:", metrics.accuracy_score(y_testset, predTree))
```
## Visualizing the Decision Tree
The trained Decision Tree model can be visualized using the `export_graphviz` function from scikit-learn:
```python
from sklearn.tree import export_graphviz
# Visualize the decision tree
export_graphviz(drugTree, out_file='tree.dot', filled=True, feature_names=['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K'])
```
## Contributions
We welcome contributions from the community to improve this project. To contribute, please follow these steps:
1. **Fork the Repository**: Click the "Fork" button at the top right of the repository page to create a copy of this repository on your GitHub account.
2. **Clone the Repository**: Clone your forked repository to your local machine.
```bash
git clone https://github.com/your-username/drug-classification-decision-tree.git
```
3. **Create a New Branch**: Create a new branch for your feature or bug fix.
```bash
git checkout -b feature-name
```
4. **Make Changes**: Make your changes to the codebase.
5. **Commit Your Changes**: Commit your changes with a clear and descriptive commit message.
```bash
git commit -m "Description of your changes"
```
6. **Push to Your Branch**: Push your changes to your forked repository.
```bash
git push origin feature-name
```
7. **Open a Pull Request**: Open a pull request to merge your changes into the main repository. Provide a detailed description of your changes in the pull request.
We appreciate your contributions and will review your pull request as soon as possible. Thank you for helping improve this project!
## Acknowledgments
Saeed Aghabozorgi
### Other Contributors
## Change Log
| Date (YYYY-MM-DD) | Version | Changed By | Change Description |
| ----------------- | ------- | ---------- | ------------------------------------------------ |
| 2024-08-09 | 2.4 | Idara-Abasi Udoh | Project completion |
| 2022-05-24 | 2.3 | Richard Ye | Fixed ability to work in JupyterLite and locally |
| 2020-11-20 | 2.2 | Lakshmi | Changed import statement of StringIO |
| 2020-11-03 | 2.1 | Lakshmi | Changed URL of the csv |
| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |
| | | | |
| | | | |
##
© IBM Corporation 2020. All rights reserved.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.